January 2026 Benchmarks: The Gap Between Typical And Top Performing Businesses
The gap between median and top-quartile performance in January shows what disciplined growth actually looks like in 2026.
CJ Hunter
February 18, 2026
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min read
table of contents
TL;DR
Median businesses paid more for less: Spend rose, but new customer revenue fell and CAC increased.
Top quartile improved efficiency: Higher budgets, stronger conversion, lower CAC.
Acquisition discipline was the gap: Leaders enforced MER floors and CAC ceilings.
Creative and guardrails win: Separate first-time metrics and scale creative before budgets.
January 2026 is our first clean look at how ecommerce businesses are entering the new year. The topline story: the median business is still paying more to grow, while the top quartile is turning the same market into cheaper, more efficient acquisition.
In this article, we’re breaking down what happened in January, how typical performance compares to the 75th percentile, and what operators should do with these benchmarks.
How The Typical Business Started 2026
Across Northbeam customers, the median business saw modest growth and worsening new customer economics in January 2026:
There are a few clear implications:
Businesses increased budgets, but revenue did not keep pace, so blended MER barely moved.
New customer revenue actually declined, which tells you acquisition was the weak link, not overall demand.
New customer MER fell and CAC rose, at the same time conversion rate dropped, which is exactly what you see when more spend is chasing lower intent traffic.
If January felt like you were working harder for less convincing performance on first time buyers, these numbers explain why.
What Top Performers Did Differently
The 75th percentile tells a very different story. In the same month, the best performers delivered step change gains in both growth and efficiency:
This is what healthy, scalable growth looks like:
Revenue nearly kept pace with a very aggressive spend increase.
Both blended and new customer MER improved, instead of being sacrificed.
New CAC fell while conversion rate increased, which means these businesses bought better traffic and converted more of it into first time customers at a lower marginal cost.
In other words, January was not just “easier” for the top quartile. They operated with a different set of rules.
Four Levers Behind January’s Top Quartile
1. What 2025 Told Us: Creative Systems Built For Higher Spend
Higher budgets only work if your creative pipeline can support them. Our 2025 data showed a clear relationship between spend bands and ad volume: businesses that spent more launched dramatically more ads, especially on platforms like Meta and TikTok.
In January 2025, the top quartile looked like businesses that had already internalized this:
They committed to a minimum number of new concepts per week based on their monthly spend, instead of trying to stretch a single winning ad across the month.
They treated creative capacity as a strategic constraint, budgeting time and resources accordingly.
They used an attribution source of truth to decide which ideas earned scale, instead of relying on noisy in platform metrics alone.
The result is visible in the benchmarks: higher spend, higher conversion, and lower CAC at the same time.
Higher budgets only work if your creative pipeline can support them.
In January 2026, the top quartile’s results are exactly what you would expect from businesses that took that lesson seriously
Median businesses nudged budgets up and accepted whatever economics the market delivered. Top performers acted as if MER and CAC were hard constraints, not nice to have metrics.
Practically, that means:
Defining MER floors that reflect contribution margin and cash needs, not arbitrary targets.
Setting CAC ceilings by product or AOV band, so you never let auction pressure quietly erase unit profit.
Wiring these thresholds into weekly operating views so violations trigger action, not end of month surprises.
You cannot control the market, but you can control how much pain you are willing to tolerate before you pull back.
3. First Time Performance As A Separate Scorecard
In the January aggregate, blended metrics do not tell the full story. Revenue is up a little, MER is flat to slightly positive, and it would be easy to decide you are fine. New customer metrics say otherwise.
Top performers watched first time economics independently:
First Time MER, First Time CAC, and First Time Revenue sat next to blended metrics in every core report.
Any period where revenue grew while first time MER fell was treated as a problem, not a win.
Channels, campaigns, and creative were evaluated on whether they added net new customers at an acceptable price, not just whether they pushed blended MER up for a week.
That discipline shows up directly in the January gap between median and 75th percentile new customer performance.
4. Smarter Use Of January As A Calendar Moment
January is not just “month one” on a clean slate. It is a very specific demand environment: resolution season in some verticals, hangover season in others, and a reset after Q4 promo pressure.
The data suggests that:
In categories where Q1 demand is structurally strong, winning businesses leaned into January but kept CAC and payback rules tight, instead of assuming last year’s promo economics would hold forever.
In more fragile verticals, they treated January as a controlled test month for offers and creative, not as a place to force topline at any cost.
Across the board, they judged January performance against their own category and size cohort, not generic ecommerce averages.
That is why you see top performers able to add new revenue at lower CAC even as the median business struggles.
How To Use These Benchmarks In Your Own Planning
You cannot copy the exact numbers from the 75th percentile, but you can copy the operating patterns that got them there. A practical way to work with the January 2026 data:
Benchmark Honestly
Compare your January shifts in spend, revenue, MER, new revenue, new MER, CAC, and conversion rate to both the median and 75th percentile views.
Decide whether you are closer to “typical” or “top quartile” and why.
Codify Guardrails
Turn those comparisons into explicit floors for MER and ceilings for CAC, with separate lines for blended and first time performance.
Use them to shape bids, budgets, and channel mix from February onward, instead of treating them as a one time post mortem.
Rebuild Your January And February Scorecards
Put first time MER, first time CAC, and new revenue on the same level as blended MER in dashboards.
Flag any week where new customer economics deteriorate while topline looks fine, and react quickly instead of waiting for a quarter end review.
Align Creative Throughput With Ambition
If you plan to push budgets meaningfully above January levels, lock in the creative volume that will be required to keep CAC from drifting up.
Make sure you have the measurement in place to identify which ideas deserve scale and which should be cut early.
The Takeaway For 2026
January 2026 shows two very different realities inside the same market. The median business nudged spend up, saw modest revenue growth, and watched new customer economics slip. The top quartile increased budgets aggressively, grew faster, and improved efficiency at the same time.
The difference is not access to some secret channel. It is whether you are willing to run the year on explicit guardrails, separate scorecards for first time performance, calendar aware planning, and creative systems that can actually support the spend level you want.
If you get those four pieces right, the January 2026 benchmarks stop being a warning and start becoming a baseline for what your own growth can look like over the rest of the year.
Most marketing teams can track leads. Far fewer can confidently explain how those leads turn into revenue.
Revenue attribution connects early marketing touchpoints to downstream sales outcomes, helping bridge the gap between marketing, sales, and RevOps.
This guide breaks down what revenue attribution is, how lead attribution fits in, common models, measurement challenges, and how to implement it responsibly. The goal is not perfect precision, but shared clarity and alignment around what actually drives growth.
What Revenue Attribution Is and How It Differs from Lead Attribution
Revenue attribution is the practice of connecting marketing activity to actual sales outcomes. Instead of stopping at leads or form fills, it looks at how early marketing touchpoints influence opportunities, deals, and closed revenue over time.
Lead attribution plays a role here, but it is an input, not the end goal. It helps explain where leads come from, while revenue attribution asks what happened next.
Did those leads turn into opportunities? Did they contribute to pipeline movement? Did they help close deals?
This distinction matters because attribution can be framed at different levels:
Lead-based attribution assigns credit at the point of lead creation.
Opportunity-based attribution focuses on what influenced pipeline creation or acceleration.
Revenue-based attribution ties influence directly to closed deals.
For B2B teams, stopping at MQLs creates blind spots. Long sales cycles, multiple stakeholders, and sales activity all shape outcomes after the lead stage.
A common misconception is that revenue attribution exists to prove marketing “owns” revenue. In reality, its value lies in creating shared visibility into how growth actually happens.
Why Revenue Attribution Is Hard (and Why It Matters Anyway)
Revenue attribution breaks down in practice because B2B buying rarely follows a clean, linear path.
Sales cycles stretch across months or quarters, with prospects moving in and out of active evaluation. Along the way, multiple stakeholders engage with different messages, channels, and people, often at different times and for different reasons.
Not all of those interactions are captured digitally. Sales conversations, events, referrals, and partner activity frequently happen offline or outside standard marketing systems.
Even when data exists, CRM handoffs between marketing and sales are inconsistent. Leads may be reclassified, contacts merged, or opportunities created late, obscuring earlier influence.
Despite these challenges, weak revenue attribution carries real costs. When teams cannot credibly connect marketing effort to sales outcomes, budgeting becomes political, optimization stalls, and trust erodes across functions.
The goal is not perfect tracking, but a shared, defensible view of what influences revenue and where investment actually pays off.
Core Revenue Attribution Models Explained
Revenue attribution models define how credit is assigned across marketing and sales touchpoints. Each model answers a different question, which is why no single approach works for every team or use case.
First-Touch Revenue Attribution
Definition: Assigns all revenue credit to the first recorded marketing interaction.
Use cases: Understanding which channels are effective at creating initial demand or awareness.
Limitations: Ignores everything that happens after the first touch, including sales activity and deal acceleration.
Last-Touch Revenue Attribution
Definition: Assigns all revenue credit to the final interaction before opportunity creation or close.
Use cases: Evaluating conversion-focused programs and late-stage influence.
Limitations: Overweights closing tactics and undervalues earlier marketing efforts that shaped the deal.
Linear and Multi-Touch Revenue Attribution
Definition: Distributes credit across multiple interactions throughout the buyer journey.
Use cases: Gaining a more holistic view of how marketing and sales jointly influence revenue.
Limitations: Sensitive to data quality, model configuration, and interpretation. Results can vary widely.
Position-Based Revenue Models
Definition: Position-based models weight key moments, often first and last touch, more heavily.
Use cases: Teams seeking balanced visibility without heavy modeling complexity.
Limitations: Still rely on assumptions about which moments matter most.
When Simpler Models Outperform Complex Ones
In practice, simpler models often outperform complex ones because they are easier to explain, validate, and apply consistently. Attribution only creates value when teams trust it enough to act on it. Stability and shared understanding matter more than mathematical precision.
Mapping Marketing Touchpoints to Sales Outcomes
Mapping marketing touchpoints to sales outcomes works best when it follows a deliberate, shared process rather than an attempt to capture everything at once.
1. Align Marketing, Sales, and RevOps Up Front
Attribution should be treated as a cross-functional agreement, not a marketing-only exercise. Clarify how sales activity fits into attribution, establish review cadences, and create feedback loops to surface issues early. Alignment at this stage helps avoid attribution-driven tension later.
2. Scope Attribution Responsibly
Define what questions attribution needs to answer. Focus on decisions you want to inform, such as budget allocation or program prioritization, and avoid overextending attribution beyond what data can support.
3. Define What Counts as a Marketing Touch
Not every interaction should be included. Clearly document which activities qualify as marketing touches, such as campaigns, content engagement, or events, and apply those rules consistently.
4. Align Touchpoints With Funnel Stages
Map touches to stages like awareness, opportunity creation, and deal progression. This helps distinguish between activities that open doors and those that move deals forward.
5. Understand Opportunity Creation vs Opportunity Acceleration
Some programs generate new opportunities. Others influence deal velocity, size, or win rate. Treating these as distinct outcomes improves interpretation.
6. Decide How to Handle Sales-Led and Partner-Led Deals
Define rules for deals initiated by sales or partners. Clear handling prevents over-attribution while still recognizing marketing’s supporting role.
Common Revenue Attribution Pitfalls and How to Avoid Them
Even well-intentioned attribution efforts can backfire when teams push models beyond their intended purpose or apply them inconsistently. The most common failures are avoidable with clear guardrails and shared expectations.
Over-Claiming Marketing Influence
Pitfall: Attribution models are used to maximize marketing credit rather than reflect reality, eroding trust with sales and finance.
Fix: Scope attribution to influence, not ownership. Document assumptions, acknowledge uncertainty, and present results as directional inputs for decision-making.
Ignoring Sales Activity or Timing
Pitfall: Marketing touchpoints are analyzed in isolation, without accounting for sales outreach, deal stage, or timing.
Fix: Incorporate sales activity into attribution views and separate early-stage influence from late-stage acceleration to preserve context.
Changing Models Mid-Quarter
Pitfall: Teams tweak attribution models or windows in response to short-term performance, making trends impossible to interpret.
Fix: Lock attribution definitions for a given period and review changes on a defined cadence, not reactively.
Treating Attribution as Compensation Logic
Pitfall: Attribution is used to determine credit, quotas, or incentives, turning it into a political tool.
Fix: Keep attribution focused on learning, planning, and optimization. Compensation decisions should rely on broader performance measures.
Revenue Attribution KPIs
Revenue attribution should inform a focused set of outcome-oriented metrics, not create a parallel reporting universe.
Core KPIs revenue attribution should support include:
Marketing-influenced revenue, to show where marketing plays a meaningful role.
Pipeline contribution, to understand impact earlier in the revenue cycle.
Win rate by source, to assess deal quality, not just volume.
Sales cycle length by touch pattern, to identify acceleration effects.
Equally important are diagnostic checks that test attribution health:
Sensitivity to model or window changes.
Consistency in lead-to-opportunity conversion.
Overlap across channels that may indicate double-counting.
Together, these metrics help teams trust attribution as a decision-making input rather than a scorekeeping exercise.
Revenue Attribution Governance
Revenue attribution only works when it is governed as a shared system, not an ad hoc analysis.
Teams should lock attribution definitions, models, and windows for each reporting period to ensure results remain comparable over time. Frequent changes undermine confidence and make trends impossible to interpret.
Clear ownership is equally important. Establish who is responsible for maintaining attribution logic, who interprets results, and how questions or disputes are resolved.
Just as critical is documenting assumptions, known limitations, and data gaps. Transparent documentation sets realistic expectations, prevents misuse, and reinforces attribution’s role as a learning tool rather than a source of absolute truth.
Core Revenue Attribution Tools
Revenue attribution relies less on a single platform and more on how systems work together across the revenue stack.
Data Sources
Attribution starts with integrated data from marketing platforms, CRM systems, and sales activity logs. These sources capture campaign engagement, lead and opportunity records, and direct sales interactions that shape deal outcomes.
Identity and Linkage
Effective attribution depends on reliable identity resolution across leads, contacts, accounts, and opportunities. This layer connects early marketing engagement to downstream revenue, enabling analysis across long, multi-touch buying journeys.
Attribution Logic
Attribution tools apply defined models, windows, and weighting rules to determine how credit is assigned. This logic should be configurable, documented, and stable over time, allowing teams to compare performance consistently rather than chasing short-term signal shifts.
Outputs
The value of attribution appears in its outputs. Common outputs include revenue and pipeline reports, trend analyses by channel or touch pattern, and planning inputs that inform budget allocation and program strategy.
Attribution tools are most effective when outputs are easy to interpret and directly tied to decision-making workflows.
Revenue Attribution Implementation Plan
Revenue attribution works best when it is introduced deliberately, with clear intent and shared ownership, rather than treated as a technical configuration exercise.
Phase 1: Define Attribution Scope and Goals
Start by agreeing on what attribution is meant to support, such as budgeting, prioritization, or planning. Clear success definitions prevent misalignment and unrealistic expectations later.
Phase 2: Audit Data and Funnel Handoffs
Review marketing, sales, and CRM data to identify gaps, inconsistencies, and broken handoffs. Fixing foundational issues early improves confidence in downstream insights.
Phase 3: Select and Document Attribution Model
Choose a model and window that reflect your sales cycle and data maturity. Document assumptions and lock configurations to ensure consistency and comparability over time.
Phase 4: Operationalize Insights
Integrate attribution outputs into regular planning and review processes. Attribution delivers value only when insights influence investment decisions and strategic trade-offs.
From Revenue Attribution to Action
Revenue attribution connects marketing effort to real business outcomes by extending analysis beyond leads and into pipeline and revenue.
The real value of attribution comes when shared definitions, stable models, and cross-functional alignment are all in place. When governed well, attribution stops being a debate about credit and becomes a trusted input for planning, investment, and growth decisions.
From a distance, 2025 looked like a clean win for ecommerce. Across Northbeam’s dataset, businesses increased ad spend and revenue, and the “growth is back” story finally felt true.
Up close, a little more nuance is required for the true read..
By vertical, 2025 was a year of trade‑offs: some categories grew fast but at the expense of MER and CAC, others protected economics and took slower topline, and a handful managed a more balanced path.
Using median results from our 2025 Year In Review whitepaper, here’s how eight major verticals actually performed, and what operators should do with that in 2026.
Sporting Goods & Fitness: Aggressive Growth at a Cost
For Sporting Goods & Fitness, 2025 was a big year on revenue, but an expensive one.
At the median, businesses in this category increased spend by about +25% and grew revenue roughly +17%. That’s genuine expansion. But median MER fell by nearly 7%, first‑time MER dropped by more than 11%, and first‑time CAC climbed about +17% year over year.
In other words, every marginal customer cost significantly more and delivered less efficient revenue than in 2024, especially in the back half of the year where December was notably harsh for new‑customer economics.
2026 operator takeaway:
Treat Sporting Goods & Fitness as a category where early‑year is your acquisition window and the rest of the year should be managed accordingly. The data shows February through May as the most forgiving period for new‑customer growth. Use those months to build strong cohorts, then spend more of the back half defending MER and increasing LTV (bundles, upsells, replenishment) instead of forcing volume when CAC is structurally higher.
Food & Beverage: Growth that Leaned on LTV
Food & Beverage looks like a quiet success story on the surface. Median revenue grew a bit over +9% while median ad spend actually declined around –5%. That is a strong headline.
Underneath, the category was still under pressure. Median MER slipped by nearly 4%, first‑time MER by about 11%, and first‑time CAC increased more than 5%. In practical terms, businesses got better at squeezing revenue out of existing budgets and existing customers, but new customers were harder and more expensive to win.
2026 operator takeaway:
Plan as if higher acquisition costs are here to stay. That means:
Setting clear CAC thresholds for prospecting.
Designing acquisition and lifecycle together: subscription mechanics, post‑purchase flows, and thoughtful bundling that help you recoup weaker first‑time MER over time.
The businesses that treat LTV as part of their acquisition constraint, rather than an afterthought, will be the ones that can keep growing in this environment.
Home & Furniture: Timing as the Main Edge
Home & Furniture sits in a familiar position: high AOV, long consideration, and structurally higher CAC than many other categories. In 2025, median businesses grew spend by roughly +8.5% and revenue by about +9.0%, which means budgets and top‑line stayed fairly aligned.
The pressure showed up in how much it cost to create that revenue:
Median MER declined about –2.6%.
Median first‑time MER dropped around –7.5%.
Median first‑time CAC increased close to +9.5%.
Median first‑time revenue still grew about +6.7%.
So the sector did add new customers and revenue, but at a higher acquisition cost and tighter efficiency than the year before.
The nuance that matters is timing. The 2025 data highlights May and August as months where Home & Furniture businesses managed to grow revenue and hold comparatively better MER. Q4 produced strong revenue, but new‑customer economics in those weeks were noticeably weaker than in those spring and late‑summer windows.
2026 operator takeaway:
Think of the year as a sequence of deliberate acquisition campaigns rather than a constant push. Lean into proven intent peaks; move‑in seasons, refresh cycles, and the May/August windows the data calls out, and raise the bar for prospecting outside them. In this vertical, you win by being right on timing and payback, not by being “always on.”
Fashion & Accessories: Growth Inside a Narrow Band
Fashion & Accessories did grow, but with very little margin for error. Median spend was up about +7%, median revenue roughly +6%. Meanwhile, median MER fell just under –2%, first‑time MER fell more than –5%, and first‑time CAC rose nearly +9%.
The monthly curves show a familiar pattern: a few “healthy” months, mainly late spring and early summer, where revenue and MER improved together, and long stretches where raising budgets simply meant paying more for the same or slightly worse revenue outcomes. Fashion could absolutely grow in 2025; it just could not grow indiscriminately.
2026 operator takeaway:
Treat Fashion as a tight‑corridor category. Use the whitepaper’s shape as a sanity check when you build your plan:
Design scale around the months where your vertical historically supports both growth and MER.
Cap CAC tightly in weaker periods.
Use merchandising and bundling (for example, full‑look outfits or multi‑buy offers) to create AOV headroom that helps absorb higher CAC.
Beauty & Personal Care: Uneven Year, Mid‑Year Payoffs
Beauty & Personal Care had one of the more uneven journeys through 2025. At the median, spend and revenue both increased around +3–4%, but MER fell roughly –4%, first‑time MER about –8%, and first‑time CAC rose just over +4%.
Q1 stands out as particularly difficult: weaker revenue, compressed MER, and poor new‑customer economics created a sense that “growth feels bad.” Results improved later; mid‑year and in select Q4 periods, where businesses came in with well‑tested promos and creative instead of scrambling in‑season.
2026 operator takeaway:
Split the year in two:
First half: a structured test phase for offers, bundles, and creative platforms that actually defend MER.
Second half: execution—scale only what your own data shows is working.
In beauty, going into holiday with untested concepts is how you end up paying 2025‑style CAC for 2024‑style guesses.
Health & Wellness: Resolution Demand vs. Sustainability
Health & Wellness leaned hard into Q1 resolutions. At the median, spend increased about +9.4%, but revenue only around +4.3%. MER slipped roughly –1.5%, first‑time MER about –7%, and first‑time CAC climbed nearly +7.8%.
So yes, businesses won a lot of January and early‑year customers, but at worse efficiency than 2024. In the back half of the year, the category relied more on existing customers to carry growth as new‑customer economics stayed under pressure.
2026 operator takeaway:
For Health & Wellness, the critical question isn’t just how many people you bring in during January; it’s what those cohorts are worth over time. Use the 2025 shifts in first‑time CAC and first‑time MER as hard context when you:
Define acceptable payback windows for your resolution campaigns.
Design lifecycle programs, product pathways, and pricing that make those Q1 cohorts financially rational.
Only then decide how far you’re willing to push bids and budgets in the resolution spike.
Baby & Kids: When Always‑On Stops Working
Baby & Kids is where “spend more” was most clearly punished.
On averages, spend rose about +10%, but revenue only +2%, with average MER down roughly –7% and average CAC up +15%. Even at the median—where revenue grew a healthier +7.2% and MER improved about +2.4%—median CAC still increased around +4.2%.
Combine that with heavy seasonality and you get the real story: many businesses pushed budget into the wrong weeks at the wrong cost and paid for it in MER and CAC.
2026 operator takeaway:
Here, timing and cohort quality matter more than raw budget. Use the industry curves to identify:
Which months and promo windows historically deliver both higher revenue and acceptable MER.
Which high‑CAC pushes did not produce resilient cohorts or meaningful LTV.
Then build a 2026 plan that concentrates acquisition in those proven windows and runs lean everywhere else.
Technology is the clearest example of aggressive scale in the report.
On averages, Tech businesses increased spend by about +38% and revenue by +28%, while average MER fell roughly –7% and average CAC jumped over +33%. At the median, things look milder: spend +1.3%, revenue +2.8%, MER +1.5%, but first‑time MER still down about –3% and first‑time CAC up roughly +13%.
The gap between average and median tells you what’s really happening: larger tech advertisers are driving the visible growth and are the ones most willing to accept painful CAC and MER trade‑offs. Smaller players are stuck in the same auction, with much less room for error.
2026 operator takeaway:
You cannot simply mirror the biggest budgets in your space. Use MMM and incrementality to determine:
Where spend is truly creating net‑new demand.
Where it’s just shifting conversions between channels or cannibalizing branded/organic.
Let the whitepaper’s Tech benchmarks define your upper limit on how much pain (MER decline and CAC increase) you’re willing to tolerate, not your default state.
How to Use These Takeaways In 2026
These industry breakdowns aren’t about bragging rights. They’re about understanding the shape of your category’s trade‑offs: how 2025 balanced revenue, MER, and CAC by vertical, and what that implies for 2026.
The key moves are:
Benchmark locally, not globally. Compare your performance to businesses in your size band and vertical using the median stats, not to “ecommerce overall.”
Set guardrails that reflect reality. Use the MER, first‑time MER, and CAC shifts above as the starting point for your 2026 floors and ceilings—not wishful thinking.
Plan around your calendar shape. Build explicit scale windows and harvest windows using the months where your vertical actually supported efficient acquisition.
Every figure cited here, revenue, MER, and CAC changes by vertical, comes directly from Northbeam’s 2025 Year in Review. To download the full data set, click here.
High-performing teams do not win simply because they collect data, they win because they interpret it in time, with clarity, and act on it responsibly.
As marketing stacks grow more complex and markets shift faster, teams face a stark reality: the risks of doing intelligence poorly often outweigh the risks of not doing it at all.
This article focuses less on definitions and pillars and more on common failures that trip teams up, the hidden risks of weak intelligence practice, and practical steps teams can take to avoid those failures. It is meant as a reality check, because intelligence is powerful only when it is done well.
What Is Marketing Intelligence and What It Is Not
By definition, marketing intelligence is the practice of turning marketing data into informed decisions. It answers questions like: What is changing in our market? Why is performance shifting? What should we do next?
It is not the same as data, reporting, or analytics:
Data is the raw input
Reporting organizes data into charts and dashboards
Analytics explains what happened and sometimes why
Marketing intelligence goes a step further: it synthesizes multiple signals, adds context, and produces insight that supports a decision or recommendation.
Marketing intelligence is also distinct from business intelligence. Business intelligence typically focuses on company-wide performance such as finance, operations, or supply chain metrics.
Marketing intelligence is more outward- and forward-looking. It combines internal performance data with external signals like customer behavior, competitive moves, market trends, and platform changes.
Importantly, marketing intelligence tools do not tell the whole story. Tools support intelligence, but they are not intelligence on their own. A dashboard without interpretation does not guide action. A report that is reviewed but never used does not create value.
Marketing intelligence only exists when insights are translated into decisions, priorities, and changes in execution.
The Hidden Costs of Bad Intelligence
When marketing intelligence fails, the impact is not just sloppy reporting, it can lead to misallocated budgets, missed opportunities, and strategic paralysis. Here are the most common failure modes teams face.
1. Insight Overload Without Prioritization
Many teams make the mistake of surfacing every anomaly, trend, and metric variation, assuming volume equals insight. When decision-makers are drowned in signals, nothing actually gets acted on.
Risk: Leaders delay decisions because they cannot distinguish noise from what truly matters.
Fix: Curate insights by asking these questions: Does this support a clear decision, and what action would this trigger. Rank intelligence outputs by strategic impact and urgency before sharing them.
2. Lagging Intelligence That Arrives Too Late
Intelligence that lands after decisions have already been made is not intelligence, it is reporting. Too often, teams rely on slow, backward-looking analyses that are outdated by the time they reach stakeholders.
Risk: Teams react to outcomes rather than shaping them.
Fix: Build faster data pipelines and review cadences timed to planning cycles. Use streaming signals and forward-looking market indicators where feasible, and automate alerts for critical changes, such as shifts in competitor activity or platform policies.
3. Overconfidence in Incomplete Signals
Partial data, vanity metrics, or short timeframes can create false confidence. Acting on incomplete or ambiguous signals can be worse than acting slowly, because it can embed poor strategy into your execution.
Risk: False positives lead to wasted budget and poor strategic decisions.
Fix: Treat uncertainty as part of intelligence. Cross-validate signals across multiple data sources, such as performance, customer behavior, and market trends. If you do not have sufficient data for confidence, surface that uncertainty rather than masking it.
4. Siloed Ownership and Misaligned Incentives
When intelligence lives in analytics teams without a clear handoff to planners and operators, it becomes noise. If incentives differ across teams, intelligence fails its primary purpose.
Risk: Insights die in dashboards and never influence decisions.
Fix: Define clear roles and responsibilities, including who owns data integrity, who interprets signals, and who makes decisions. Establish shared KPIs that align intelligence outputs with business outcomes.
Core Inputs That Power Marketing Intelligence
Effective examples of marketing intelligence depend on combining multiple data sources into a coherent view of performance, customers, and the competitive landscape.
Performance Data
Performance data shows how your marketing efforts are working today.
Channel-level performance across paid, owned, and earned media
Campaign results, including creative, targeting, and sequencing
Funnel metrics such as conversion rates, velocity, and drop-off points
Customer Data
Customer data provides insight into who your audience is and how they behave over time.
Behavioral signals across touchpoints and channels
Cohort analysis to understand retention, value, and engagement patterns
Lifecycle stage indicators, from first touch to expansion or churn
Market and Competitive Data
Market and competitive data adds essential external context to internal performance.
Market trends, benchmarks, and seasonal patterns
Competitor messaging, positioning, and pricing changes
Channel presence and shifts in media strategy
External Signals and Data Quality
External signals help teams anticipate change before it impacts performance.
Macro and economic indicators
Regulatory developments and policy changes
Platform updates, algorithm changes, and ecosystem shifts
Across all inputs, data quality matters. Consistent definitions, reliable sources, and proper context are critical to ensuring insights are trustworthy and actionable.
Why Good Intelligence Still Fails
Even with tools and data, intelligence can fall short if it does not integrate meaningfully into daily execution and strategic planning.
Failure: Treating Tools as a Silver Bullet
Dashboards, competitor intelligence platforms, and automated reports create the illusion of intelligence without interpretation and decision frameworks.
Truth: Tools enable intelligence, they do not create it.
Best Practice: Tie every dashboard or alert to a decision workflow. If a signal will not trigger a decision or hypothesis test, reconsider its place in your stack.
Failure: Ignoring Data Quality
No amount of analysis can rescue flawed data. Inconsistent definitions, fragmented sources, and unclear timeframes create noise that misguides teams.
Truth: Bad data is worse than no data because it creates false paths.
Best Practice: Invest early in data governance with standardized definitions, consistent time periods, and centralized data sources. Create a simple data quality scorecard for key intelligence feeds.
Emerging Risk Areas in Marketing Intelligence
As intelligence matures across organizations, deeper systemic risks emerge that leaders must anticipate and manage.
The Risk of Bias in Data Interpretation
Even large and rich datasets can mislead when analysts do not account for bias in data selection or analysis methodologies. Bias can arise because of overreliance on certain sources or because the context behind the data is not well understood.
Risk: Decisions based on biased insights can undermine strategy and obscure true market dynamics.
Fix: Encourage diverse source selection, use blind analysis techniques where possible, and involve cross-functional perspectives to challenge assumptions. Encourage teams to ask what the data might be missing as part of the analysis conversation.
The Risk of Overdependence on Secondary Data
Some organizations mistakenly rely heavily on secondary or syndicated data sources such as industry reports, press releases, or generic market dashboards.
Risk: Secondary sources can omit nuance and real-time shifts, leading to stale or superficial intelligence.
Fix: Balance secondary sources with primary research and unique data signals such as survey results, customer feedback, or real-time behavioral tracking. Investing in primary data collection allows teams to fill gaps that generic sources cannot address.
The Risk of Misalignment With Business Context
Intelligence that is disconnected from actual business priorities or decision contexts loses impact. Analysts must have an accurate understanding of the organization’s strategic goals to ensure insights are relevant.
Risk: Intelligence may deliver insight that is interesting but not actionable.
Fix: Start each cycle of analysis with a conversation about strategic objectives. Document business goals related to the intelligence questions and ensure alignment with planning.
Common Marketing Intelligence Failures and How to Avoid Them
Most marketing intelligence failures are not caused by lack of data, but by how intelligence is generated, shared, and used.
Insight Overload Without Prioritization
One of the most common failures is producing too many insights without clear prioritization. When teams surface every anomaly or trend, decision-makers struggle to determine what actually matters.
Effective marketing intelligence filters signals, frames trade-offs, and highlights the few insights that warrant action.
Lagging Intelligence That Arrives Too Late
Intelligence loses value when it arrives after decisions have already been made. If insights are tied to slow reporting cycles or retrospective analysis, teams end up reacting rather than shaping outcomes.
Successful programs focus on timeliness, ensuring intelligence is delivered in time to influence planning and execution.
Overconfidence in Incomplete Signals
Another risk is acting on partial or misleading data. Single metrics, short time frames, or unvalidated assumptions can create false confidence.
Strong intelligence practices emphasize context, triangulation, and uncertainty, helping teams understand what the data does and does not support.
Siloed Ownership and Misaligned Incentives
When intelligence ownership is unclear, insights often live in silos and fail to influence decisions. Clear accountability, shared goals, and alignment with decision-makers are essential to turning insight into action.
Conclusion: Intelligence That Works Is Intentional and Integrated
Marketing intelligence is not just about producing more reports or dashboards, it is about creating clarity, reducing uncertainty, and guiding better decisions. The biggest failures are not technical, they are organizational and process-based.
By prioritizing timely, actionable insights, aligning teams around strategic goals, and governing intelligence with purpose, organizations can avoid the common pitfalls that turn good data into bad decisions.
With the right culture, cadence, and discipline, marketing intelligence does not just inform, it transforms how teams think, act, and win in competitive markets.
Many SEO programs generate traffic, but far fewer can explain why that traffic matters to the business. Too often, SEO objectives are vague, metric-driven, or disconnected from the decisions teams actually need to make.
This article breaks down how to define SEO objectives in business terms, map the right metrics to real goals, avoid common planning traps, and implement a measurement-ready SEO framework.
SEO can be a tool for prioritization and decision-making, not just another line in a performance report.
What an SEO Objective Is (and What It Is Not)
An SEO objective is a business-aligned outcome that SEO is responsible for influencing. It answers the question: what should organic search meaningfully change for the business?
A strong SEO objective is specific, measurable, and connected to a real decision or priority, not just a performance report.
This is where many teams get tripped up. SEO objectives, SEO goals, and SEO tactics should not be used interchangeably.
An SEO objective defines the outcome.
An SEO goal sets a target that signals success
SEO tactics are the actions you take to get there.
For example, “improve qualified demo starts from non-branded search” is an objective. “Publish comparison pages” is a tactic.
That’s why “increase traffic” is not an SEO objective. Traffic is a byproduct, not a business outcome. More sessions only matter if they lead to revenue, retention, efficiency, or strategic visibility.
It’s also important to distinguish leading vs lagging SEO objectives. Leading objectives focus on early indicators like visibility in priority queries. Lagging objectives measure downstream impact like conversions or pipeline contribution.
Why SEO Objectives Often Fail to Drive Impact
Even well-funded SEO programs can stall if objectives aren’t designed to support real business decisions. Most failures trace back to a small set of structural issues, not execution quality.
Over-Reliance on Vanity SEO Metrics
Rankings, impressions, and raw traffic are easy to report, but they rarely reflect business value. When success is defined by surface-level SEO metrics, teams optimize for visibility rather than outcomes.
Misalignment with Product, Sales, or Lifecycle Goals
SEO objectives often exist in a vacuum. If organic priorities aren’t tied to how a product is sold, adopted, or retained, SEO efforts struggle to influence revenue or customer growth.
Lack of Ownership and Decision Authority
When no one owns the objective end to end, SEO becomes a reporting function instead of a strategic lever. Teams may identify issues but lack authority to change content, architecture, or roadmap priorities.
Time Lag Between SEO Work and Results
SEO operates on longer timelines than paid channels. Without clear expectations, leaders lose confidence before impact materializes, leading to shifting goals and abandoned initiatives.
The Result: Reactive Optimization and Wasted Effort
Together, these issues push teams into chasing short-term signals instead of building durable, compounding search performance.
Core Business Objectives SEO Can Support
When SEO objectives are aligned to business priorities, organic search becomes a durable growth engine rather than a traffic channel. The key is anchoring SEO to outcomes the business already measures and values.
Demand Generation and Pipeline Creation
SEO can drive qualified demand by capturing high-intent searches across problem, solution, and comparison queries. When tied to pipeline metrics, organic search supports predictable lead flow and reduces reliance on paid acquisition.
Revenue Efficiency and Cost Reduction
By replacing or supplementing paid traffic, SEO improves customer acquisition cost over time. Strong organic performance also reduces pressure on sales teams by pre-educating prospects before prospects enter the funnel.
Brand Visibility and Category Authority
SEO shapes how a brand appears across an entire category, not just branded terms. Consistent visibility in non-branded searches builds authority, trust, and long-term mindshare earlier in the buying journey.
Customer Education and Self-Serve Support
Search-optimized content helps users answer questions independently, reducing support volume and accelerating onboarding. This is especially valuable for complex products where education directly impacts activation and satisfaction.
Retention and Expansion Through Content
SEO supports the full customer lifecycle. Content addressing advanced use cases, integrations, and best practices reinforces retention and creates expansion opportunities.
Choose the Right Business Anchor for SEO
The strongest SEO objectives start with a single primary business objective. Teams should anchor SEO to the outcome that reflects current priorities, then align metrics, tactics, and resources accordingly.
Choosing SEO Metrics That Actually Reflect Progress
Metric Category
Example Metrics
Best Used For
Visibility Metrics
Impressions, Share of Voice
Awareness and category positioning objectives
Engagement Metrics
Click-through rate (CTR), Dwell signals
Diagnosing content quality and intent alignment
Outcome Metrics
Conversions, Assisted value
Revenue-, pipeline-, and retention-focused objectives
Instead of tracking everything, effective teams select a small set of metrics that signal progress toward real business outcomes.
Visibility Metrics
Visibility metrics show whether your content is appearing where it matters.
Key metrics include:
Impressions: Indicate presence in search results for priority queries.
Share of Voice: Measures visibility relative to competitors across a defined keyword set.
Best used as: leading indicators for awareness and category positioning objectives.
Engagement Metrics
Engagement metrics help validate whether visibility is translating into meaningful user interaction.
Key metrics include:
Click-Through Rate (CTR): Signals relevance and effectiveness of titles and descriptions.
Dwell Signals: Time on page, scroll depth, or engagement proxies that suggest content usefulness.
Best for: diagnosing content quality and intent alignment.
Outcome Metrics
Outcome metrics connect SEO directly to business impact.
Key metrics include:
Conversions: Leads, signups, purchases, or other primary actions driven by organic traffic.
Assisted Value: SEO’s contribution to downstream conversions across the buyer journey.
Best for: revenue, pipeline, and retention-focused objectives.
Diagnostic Metrics vs Success Metrics
Not all metrics are equal:
Success metrics define whether an objective was achieved.
Diagnostic metrics explain why performance changed and where to intervene.
How to Avoid Metric Overload
Limit each SEO objective to one or two success metrics, supported by a small set of diagnostics. Metrics should drive decisions, not dashboards.
Common SEO Objective Mistakes and How to Fix Them
Even well-intentioned SEO objectives can fail if they aren’t grounded in operational reality. These common mistakes often undermine impact and credibility.
Setting Objectives Without Baselines
Without a baseline, teams can’t tell whether progress is meaningful or just noise. Objectives should start with a clear understanding of current performance so improvement can be measured honestly.
Changing Objectives Too Frequently
SEO requires time to compound. Constantly shifting objectives resets momentum and erodes stakeholder confidence. Objectives should remain stable long enough to evaluate impact, even if tactics evolve underneath them.
Optimizing Metrics No One Acts On
If a metric doesn’t trigger a decision, it isn’t useful. Objectives tied to passive metrics lead to reporting cycles instead of action. Every tracked metric should have a clear owner and response plan.
Ignoring Constraints Like Resources and Tech Debt
Objectives that ignore engineering capacity, content bandwidth, or technical limitations create frustration and missed expectations. Feasible objectives acknowledge constraints and prioritize accordingly.
Practical Guardrails And Corrections
Effective teams document assumptions, limit objectives per quarter, and revisit baselines before setting new targets. Guardrails keep SEO focused, credible, and aligned with what the organization can actually execute.
SEO Objective Measurement & Governance
Clear objectives only drive impact if they’re supported by disciplined measurement and governance. Without structure, even well-defined SEO objectives drift into reporting exercises.
Core KPIs To Track Alongside SEO Objectives
Effective measurement focuses on a small set of KPIs that reflect business contribution. These often include organic contribution to key outcomes, conversion rate by query or page type, time-to-impact for SEO initiatives, and content decay or refresh velocity.
Together, these metrics show not just performance, but durability and momentum.
Diagnostic Checks
Diagnostic checks help teams understand why performance changes. Objective-to-metric consistency ensures success is measured as intended.
Monitoring sensitivity to algorithm or SERP changes protects against misattribution, while cross-channel cannibalization analysis clarifies how SEO interacts with paid, email, and other channels.
Governance Considerations
Strong governance keeps SEO accountable and defensible.
Objectives should have clear ownership, typically shared between SEO and marketing leadership. A quarterly review cadence allows teams to assess progress without overreacting to short-term noise.
Documenting assumptions, constraints, and limitations ensures transparency and better cross-functional alignment.
Best Software Tools & Workflows to Measure SEO Objectives
Measuring SEO objectives effectively requires a connected workflow rather than a single tool. The goal is to move cleanly from inputs to insight, planning, and execution.
Inputs
Search performance data from platforms like Google Search Console
Site and behavior analytics from tools such as Google Analytics or Adobe Analytics
Conversion, revenue, or lifecycle data from CRM or customer data platforms
Analysis
Query grouping to understand intent and topic performance
Page-level analysis to identify high- and low-impact content
Trend analysis to monitor progress against objectives over time
SEO platforms, analytics tools, and BI tools used together to surface insight
Planning
Roadmaps tied directly to SEO objectives and expected effort
Prioritization frameworks based on impact, resources, and dependencies
Project management and planning tools to align teams and timelines
Execution
Content creation and optimization through content management systems
Technical SEO improvements using auditing and monitoring tools
Authority-building initiatives supported by outreach or digital PR tools
Workflow for SEO Objectives
Turning SEO objectives into sustained business impact requires a repeatable workflow with clear ownership at every stage.
This phased approach helps teams move from SEO strategy to execution without losing alignment or momentum.
Phase 1: Define Business-Aligned SEO Objectives
SEO and marketing leadership should work together to define a small set of objectives tied directly to business priorities such as pipeline growth, revenue efficiency, or customer education.
The output should be a documented set of SEO objectives with clear success criteria, assumptions, and constraints. These objectives guide prioritization and serve as the reference point for all downstream work.
Phase 2: Baseline And Metric Alignment
Once objectives are set, analytics or SEO operations teams establish baselines and align metrics. This includes documenting current performance, selecting success and diagnostic metrics, and building dashboards that track progress over time.
The goal is to ensure the team can distinguish real improvement from short-term noise before execution begins.
Phase 3: Roadmap And Execution
With objectives and metrics in place, the SEO team builds a roadmap of initiatives tied explicitly to each objective. Content, technical, and authority efforts are prioritized based on expected impact and effort.
Execution focuses on delivering these initiatives consistently while monitoring early signals and constraints.
Phase 4: Review And Iterate
SEO objectives should be reviewed on a regular cadence with cross-functional stakeholders. Teams assess performance, revisit assumptions, and refine objectives as business priorities evolve.
This phase ensures SEO remains aligned, defensible, and continuously improving rather than static.
From Metrics to Meaningful Impact
Effective SEO starts with clarity about what your business actually needs. When objectives are anchored in real outcomes rather than surface metrics, SEO becomes a tool for prioritization and accountability, not just reporting.
SEO metrics should support learning and decision-making, not serve as the goal themselves. With clear ownership, disciplined measurement, and strong governance, SEO objectives can deliver durable, defensible impact that compounds over time.
At the market level, 2025 looked like a comeback year for DTC. Across Northbeam’s dataset, businesses increased ad spend and revenue by the mid‑teens on average, confirming that growth “returned” after a choppy few years.
But that story did not apply evenly. When you cut the data by company size, the sub‑$5M cohort emerges as the group that struggled most.
Our whitepaper findings are blunt: sub‑$5M businesses were hit hardest, with revenue declining YoY, despite posting the smallest increase in spend and seeing declines in both total and first‑time revenue. For these businesses, 2025 wasn’t a victory lap. It was a survival year.
What the numbers actually say for sub‑$5M businesses
On a median basis, sub‑$5M businesses nudged spend up just +1.66%, but revenue fell –1.43% year over year. That alone tells a clear story: even modest attempts to grow were met with softer performance.
Underneath that:
First‑time revenue declined more steeply than total revenue, confirming that acquisition was the main pressure point, not just one‑off volatility in returning buyers.
MER and first‑time MER both fell, with median MER down meaningfully and first‑time MER deteriorating even faster.
First‑time CAC rose, and both conversion rate and new‑visit share declined, signaling that traffic was more expensive, lower intent, and less likely to convert than in 2024.
In other words: early‑stage businesses paid more, brought in weaker traffic, and earned less on each incremental dollar of new revenue. The whitepaper summarizes this cohort as having a year “focused on cash preservation and margin protection rather than aggressive growth”, and the data backs that up.
Why early-stage businesses were most exposed
The same macro forces hit everyone, but they landed hardest under $5M.
1. Growth skewed toward businesses with more budget and maturity
Across the customer base, growth in 2025 was unevenly distributed by size. Smaller businesses largely played defense, mid‑market advertisers pushed for disciplined expansion, and upper‑mid/enterprise cohorts drove most of the topline gains.
As you move up the revenue ladder, the pattern is consistent: more spend, more revenue, and higher first‑time CAC. Larger businesses could afford to trade efficiency for market share. Sub‑$5M businesses didn’t have that option; they faced the same auction pressure without the balance sheet or measurement maturity to lean in safely.
2. Acquisition got structurally harder
At the business‑performance level, 2025 was defined by:
First‑time MER dropping much faster than blended MER,
First‑time CAC rising, and
Conversion rate and new‑visit share falling across the dataset.
That’s exactly the environment in which small businesses struggle most. When auctions are crowded, traffic is less intent‑rich, and new‑customer efficiency deteriorates, early‑stage operators can’t simply outbid competitors or wait for long payback windows. They’re forced into shorter‑runway decisions: pull back, protect cash, and accept slower top‑line progress.
3. “Average” strategies were calibrated to larger operators
Our whitepaper found that upper‑mid and enterprise businesses turned 2025 into a true step‑change year by using better measurement, creative, and channel strategy, even as acquisition costs rose.
The risk for sub‑$5M businesses is copying the shape of those strategies without the same infrastructure:
Running more channels than the team can really manage
Scaling budgets into tougher months because “the category is growing”
Chasing topline targets while the underlying unit economics (especially first‑time MER and CAC) quietly erode
The result is exactly what the data shows: tiny spend increases, real revenue declines, and a year dominated by defensive moves.
2025 as a survival year, and what changes in 2026
If your revenue is under $5M and your 2025 feels like this picture, the good news is: you were not alone, and you were not imagining it. The bad news is that 2026 can’t just be “try again, but harder.”
The strategic recommendations section of the whitepaper is effectively a playbook for early‑stage businesses to turn survival into disciplined growth.
1. Put hard guardrails around efficiency
The first step is to get explicit:
Set MER floors and CAC ceilings that reflect your reality under $5M, not market‑wide averages.
Make those thresholds visible in your weekly operating cadence so you know when a test or scale attempt is violating the rules in real time, not in a post‑mortem.
For this cohort, that usually means prioritizing survival math first: protecting payback, contribution margin, and runway before chasing aggressive growth curves.
2. Separate blended and new-customer performance
Because returning customers carried so much of 2025’s performance, we recommend evaluating first‑time MER and CAC independently from blended results.
Treat any period where revenue is up but first‑time MER is down as a red flag: you’re likely leaning too hard on your base and loading in fragile cohorts when you do acquire.
3. Benchmark against the right peers
Based on our findings, I recommend benchmarking by size and industry cohort, rather than against the entire market.
If you’re under $5M:
Use the sub‑$5M and $5–10M curves as your reference points, not the $50M+ charts.
Layer on your category’s month‑by‑month shape (for example, the heavy seasonality and promo dependence in Baby & Kids or Food & Beverage) to decide when you can afford to test scale and when you should purely protect cash.
The goal isn’t to match the biggest players; it’s to move from the left tail of your own cohort toward the healthy middle.
4. Scale creative systems before budgets, even at small spend
Finally, the platform‑level data makes one more thing clear: as businesses spend more, they are forced to launch more ads, particularly on Meta, TikTok, Axon, Snap, and even Pinterest.
For sub‑$5M businesses, that doesn’t mean hundreds of creatives per month, but it does mean:
Treating creative throughput as a constraint; don’t raise budgets beyond what your team (or partners) can realistically support.
Building a lightweight but consistent testing loop so you’re not relying on a single “hero” ad to carry months of spend in a fast‑decay environment.
The whitepaper’s findings here are simple: scale creative systems before budgets. That’s doubly true when you’re under $5M and every mis‑spent dollar hurts.
From survival to disciplined growth
Our 2025 Year in Review doesn’t sugarcoat things for early‑stage businesses: sub‑$5M operators saw revenue decline, MER compress, and acquisition costs rise, all while making only the smallest increases in spend. It was a year defined by cash preservation and margin protection, not hypergrowth.
But it also hands you a framework for what to do next:
Anchor 2026 in clear guardrails.
Judge yourself on new‑customer economics, not just blended results.
Compare your trajectory to businesses your own size, in your own category.
And only scale budgets when your creative, measurement, and channel mix are ready to support that spend.
For sub‑$5M businesses, that’s what moving from survival to disciplined, compounding growth actually looks like.
In a marketing environment defined by fragmented channels, rising competition, and constant market shifts, teams need more than performance metrics. They need marketing intelligence that connects internal data with external signals to guide real decisions.
This guide breaks down what marketing intelligence is and how to use data to build out your marketing intelligence strategy, outsmart competitors, and drive measurable business impact.
Clarifying What Marketing Intelligence Really Means
Marketing intelligence is the process of gathering, standardizing, and analyzing both internal performance data and external market and competitive data to support real marketing decisions.
Not just what to optimize, but where to play, where to pull back, and how to respond when the market shifts.
This distinction matters, because marketing intelligence is frequently confused with adjacent disciplines.
Business intelligence, for example, tends to look inward. It focuses on historical performance, financials, and operational metrics.
Marketing research, on the other hand, is often episodic and qualitative. Think surveys, focus groups, and brand studies run at specific moments in time.
Marketing intelligence sits at the intersection of the two. It connects performance metrics with customer behavior, product signals, and competitor activity.
It answers questions like: Why did this channel suddenly become more expensive? Which segment is quietly being captured by a competitor? Where is the market overserved, and where is it being ignored?
Marketing intelligence gives teams a way to move from isolated metrics to informed decisions, and from reactive optimization to proactive strategy.
The Four Pillars of Marketing Intelligence
A useful way to organize marketing intelligence is around four distinct but connected pillars. Together, they create a more complete view of how your marketing is performing, how your audience is behaving, and how the market around you is evolving.
Performance Intelligence
Performance intelligence is the most familiar pillar. It covers campaign and channel data, including spend, ROAS, conversion rates, and cost efficiency. This data tells you what is working right now and where budgets are being over or underutilized.
Customer Intelligence
Customer intelligence focuses on how real people move through your funnel and lifecycle. This includes behavioral data, engagement patterns, preferences, retention signals, and churn indicators. It helps answer questions about who is responding to your marketing, who is dropping off, and why.
Product and Offering Intelligence
Product and offering intelligence connects marketing to what you actually sell. It looks at product usage, feature adoption, customer feedback, and how your offering is perceived relative to alternatives.
Competitive and Market Intelligence
Competitive and market intelligence brings external context into the picture. It tracks competitor behavior, category trends, pricing shifts, and emerging white space.
This pillar helps teams understand whether performance changes are driven by internal execution or by broader market forces, allowing marketing to respond strategically and use marketing intelligence to beat competitors.
From Data to Strategic Insight: The Workflow
A repeatable marketing intelligence process ensures intelligence moves from raw inputs to real decisions, and back again, as part of an ongoing strategic loop.
1. Define the Intelligence Questions
The workflow starts with the questions, not the data.
Effective marketing intelligence is driven by specific, decision-oriented questions such as which competitor is gaining share in a key channel, or which customer segment is being underserved.
These questions anchor the entire process and prevent teams from chasing metrics that look interesting but do not influence strategy.
2. Identify and Gather Data Sources
Once the questions are defined, teams can identify the data needed to answer them. This typically includes internal sources like analytics and ad platform performance, alongside external inputs such as market research, social listening, and competitor signals.
3. Normalize and Synthesize the Data
Raw data is rarely comparable out of the box. Normalization ensures consistent definitions, timeframes, and metrics across sources, while synthesis brings disparate data into a central view. This step is critical for turning fragmented inputs into a coherent intelligence foundation.
4. Generate Insights
Insight generation is where patterns emerge. This might reveal that a competitor has quietly reduced spend in a channel, or that a segment is saturated with similar messaging. These insights explain the “why” behind performance changes and highlight opportunities or risks.
5. Take Action and Align
Insights only matter if they drive decisions. At this stage, marketing adjusts budgets, creative, targeting, or go-to-market strategy based on what the intelligence reveals. Alignment across teams ensures changes are intentional and measurable.
6. Monitor and Iterate
Finally, teams track the impact of their actions. Results feed back into the intelligence process, refining future questions and enabling continuous adaptation as market conditions evolve.
Translating Intelligence into Actionable Strategy
Marketing intelligence earns its place when it directly informs decisions. The value is not in knowing more, but in acting differently and with greater confidence.
Here are four marketing intelligence use cases for marketers:
Channel Optimization
One of the most immediate applications of marketing intelligence is channel optimization. When intelligence reveals that a competitor is reducing spend or exiting a channel, it creates an opportunity to test increased investment at potentially lower cost.
Instead of reacting to rising or falling performance in isolation, teams can make proactive budget shifts informed by competitive marketing intelligence and measure whether the move delivers incremental gains.
Segment and Product Opportunities
Intelligence also surfaces opportunities that are easy to miss when focusing only on aggregate metrics. By combining customer behavior, performance data, and market signals, teams can identify nuanced segments that are underserved or overlooked.
Marketing can then tailor messaging, creative, and product positioning to those audiences and use attribution to track whether engagement and conversion improve relative to baseline.
Attribution Context
Attribution data rarely tells the full story on its own. Marketing intelligence provides the context needed to interpret changes in performance.
A spike in costs or a drop in conversion may be driven by external factors such as a competitor campaign or a broader market shift. Intelligence helps teams distinguish between internal execution issues and external pressure, allowing them to adjust models, expectations, and strategy accordingly.
Risk Mitigation
Beyond growth, marketing intelligence supports risk management. Early signals around regulatory changes, platform policy shifts, or macroeconomic trends give teams time to adapt positioning, messaging, or channel mix.
By spotting these signals early, marketing can reduce exposure and maintain stability while competitors scramble to react.
Enabling Tools & Team Setup
Tools, roles, and governance determine whether marketing intelligence becomes a repeatable capability or a one-off analysis that never scales.
Technology Stack
Marketing intelligence typically sits on top of a shared data foundation. This often includes a data warehouse or lake to centralize internal and external data, supported by ETL tools to keep data fresh and consistent.
BI and dashboarding tools make insights accessible to marketers and leaders, while real-time alerts can surface sudden shifts in performance or competitive activity.
In some cases, dedicated competitor intelligence platforms add structured external signals, but they should complement, not replace, core data infrastructure.
Team Roles and Governance
Clear ownership is critical. Someone must be responsible for maintaining the intelligence pipeline, often a combination of data engineers, analysts, and marketing stakeholders.
Equally important is governance. Teams need defined review cadences, shared definitions, and clarity on who has the authority to act on insights. Without this, intelligence risks becoming informational rather than actionable.
Data Hygiene and Ethics
Marketing intelligence relies on trust, which comes from data quality and ethical practices. Teams must ensure consistent definitions, reliable sources, and compliance with privacy and platform rules when using external or competitor data.
Ethical boundaries matter: intelligence should inform strategy without crossing into deceptive or non-compliant behavior. A disciplined approach protects both credibility and long-term effectiveness.
Common Pitfalls & How to Avoid Them
Challenge
Best Practice
Collecting data without insight
Every intelligence output should answer a clear question and recommend a next step.
Over-investing in tools
Tools should support a defined intelligence process and integrate with marketing ops.
Relying only on historical data
Use forward-looking indicators to keep intelligence timely and relevant.
Separating intelligence from execution
Intelligence should feed directly into planning, optimization, and measurement loops.
Misalignment with business goals
Align intelligence to KPIs so insights are prioritized, acted upon, and valued.
Marketing intelligence often fails not because teams lack data, but because the data is poorly integrated into how decisions are actually made.
Recognizing the most common marketing intelligence pitfalls can help teams avoid wasted effort and build intelligence that drives real impact.
Collecting Data Without Converting It to Insight
One of the most common mistakes is treating data collection as the end goal. Teams gather metrics, build dashboards, and surface interesting observations, but stop short of generating clear insights or recommended actions.
To avoid this, every intelligence output should explicitly answer a question and suggest a decision or next step.
Over-Investing in Tools
Another pitfall is investing heavily in technology before defining which intelligence is needed. Without clear questions and workflows, even the most advanced tools become underused.
Tools should support a defined intelligence process and integrate directly with marketing operations, not exist as standalone reporting layers.
Relying Only on Historical Data
Historical performance data is valuable, but it is inherently backward-looking. Teams that rely solely on past results risk missing emerging trends, competitive shifts, or early warning signals.
Incorporating forward-looking indicators such as data-driven market intelligence, competitor behavior, and customer sentiment helps intelligence stay relevant.
Separating Intelligence From Attribution and Process
When intelligence is treated as separate from attribution or campaign execution, it loses influence. Intelligence should feed directly into planning, optimization, and measurement loops.
This integration ensures insights are tested, validated, and refined through real performance outcomes.
Failing to Align With Business Objectives
Finally, intelligence that is not tied to measurable business goals struggles to gain traction. Aligning intelligence efforts with clear KPIs and strategic objectives ensures insights are prioritized, acted upon, and valued by leadership.
Marketing Intelligence in Action
Consider a mid-stage B2B SaaS company operating in a competitive category where paid search has historically been expensive and crowded.
Performance data alone shows rising costs and flattening returns, but marketing intelligence adds crucial context. By monitoring competitor activity alongside internal performance, the team notices a quiet shift: several key competitors have reduced paid search spend specifically in the mid-market segment.
Rather than treating this as a coincidence, the team frames it as an intelligence signal. They reallocate budget into that segment, pair it with sharper product messaging tailored to mid-market needs, and closely monitor results through their attribution system.
Over the following weeks, conversion rates improve, cost efficiency increases, and the company sees an 18% lift in MQLs from the segment, along with stronger ROAS.
Attribution confirms that the gains are incremental, not simply the result of seasonal demand. With that validation, the team doubles down, continues to monitor competitor behavior, and refines messaging as conditions evolve.
The result is not just a short-term performance win, but a repeatable intelligence-driven approach that guides future decisions.
Next Steps to Get Started
Building a marketing intelligence framework does not require a full reorganization or a new tech stack. The most effective teams start small, focus on real decisions, and build momentum through iteration.
To get started, focus on a short, practical set of steps:
Inventory your current data sources and identify where intelligence gaps exist across performance, customer, product, and competitive signals.
Define two strategic questions you want answered this quarter, such as a competitor’s channel strategy or an underserved customer segment.
Choose one pilot initiative and gather only the data needed to answer those questions. Generate insights, adjust a campaign or strategy, and track impact through your attribution or reporting system.
Build a simple dashboard that combines core KPIs, intelligence signals, and a clear record of actions taken.
Establish a review rhythm with stakeholders, ideally monthly, to assess outcomes, refine questions, and decide next actions.
Marketing intelligence is about making better decisions, faster, by connecting insight directly to action. When done well, it turns marketing into a strong strategic advantage.
AOV, or Average Order Value, sits at the intersection of marketing efficiency, merchandising strategy, and customer experience.
In this guide, we break down AOV’s meaning, how to calculate AOV, why AOV matters, and the most common misconceptions teams make when trying to improve AOV.
Most importantly, we show how to increase AOV sustainably by treating it as a system-level metric, not a one-off upsell tactic.
What Does AOV Stand For and What Does It Actually Measure
AOV stands for Average Order Value. It measures the average dollar amount a customer spends each time they place an order on your site.
At its simplest, AOV answers one question: when someone buys, how much do they spend per transaction?
The basic AOV formula is straightforward: AOV = Total Revenue ÷ Number of Orders
Revenue typically includes all completed purchases within a defined period. Orders count each transaction, regardless of how many items are included.
Refunds, discounts, and taxes should be handled consistently so the metric reflects true commercial performance rather than accounting noise.
What AOV tells you is how effectively your pricing, merchandising, and cart experience encourage customers to add value to each purchase.
What it does not tell you is why customers bought, how many visitors converted, or whether those customers will return. A high AOV can coexist with low conversion, weak retention, or rising returns.
This is why AOV must be interpreted alongside related metrics. Without guardrails, AOV gains can mask deeper issues or create short-term wins at the expense of long-term growth.
Why AOV Matters for E-Commerce Growth and Profitability
AOV matters because it directly increases revenue without requiring more traffic. If conversion rate and traffic stay constant, even a modest lift in average order value translates into immediate revenue growth.
This makes average order value one of the most capital-efficient levers available to e-commerce teams, especially when paid acquisition costs are rising.
That efficiency becomes clearer when AOV is viewed alongside customer acquisition cost and margin. Higher AOV means each order absorbs fixed acquisition costs more effectively, improving contribution margin per transaction.
When margins are tight, increasing AOV can be the difference between scaling profitably and scaling losses, particularly in paid channels where CAC is difficult to control.
Small improvements compound over time. A 5% increase in AOV does not just raise short-term revenue: it can improve payback periods, allow more aggressive reinvestment in growth, and create room for better service or faster shipping.
Over thousands of orders, these gains add up quickly.
There are also situations where AOV matters more than conversion rate. For brands with strong intent traffic or loyal repeat customers, increasing order value can outperform marginal gains in conversion.
How to Calculate and Segment AOV
Using AOV correctly requires consistency, segmentation, and an understanding of how different customer behaviors affect the average.
1. Calculate a Clean Baseline AOV
Start with a clearly defined time period and a single source of truth for orders and revenue. Divide total completed purchase revenue by the number of orders in that same window.
Exclude canceled orders and handle refunds consistently, either netting them out of revenue or tracking them separately. The goal is a stable baseline you can trust before making changes.
2. Segment AOV by Channel, Device, and Product
Overall AOV hides important variation. Break it down by acquisition channel, device type, customer segment, and product category.
Paid social, email, and organic search often produce meaningfully different order values, as do mobile versus desktop shoppers. These differences help identify where AOV optimization will be most effective.
3. Compare First-Time vs Returning Customers
First-time customers usually have lower AOV due to trust barriers and limited familiarity. Returning customers often buy more per order and respond better to bundles or cross-sells.
Separating these groups prevents misleading conclusions and supports more targeted AOV strategies for e-commerce.
4. Analyze AOV by Cohort Over Time
Cohort analysis shows how AOV evolves for customers acquired in the same period or campaign. This reveals whether AOV gains are sustainable or driven by short-term promotions that fade quickly.
5. Avoid Common Data Pitfalls
Watch for distorted averages caused by heavy discounts, one-off bulk orders, or seasonality. Always interpret AOV alongside conversion, retention, and returns to avoid optimizing the wrong outcome.
Core Strategies to Increase AOV
The most effective AOV strategies increase order value by making the purchase more useful or more complete for the customer:
Product Bundling and Kits
Bundles increase AOV by grouping complementary products into a single, higher-value offer. The strongest bundles solve a specific use case or workflow rather than simply discounting multiple items.
Clear value framing matters. Customers should immediately understand why buying the bundle is better than purchasing items individually.
Upsells and Cross-Sells at the Right Moments
Upsells work best when they are contextually relevant and timed carefully. In-cart recommendations, post-add confirmations, and checkout-side suggestions tend to perform better than aggressive pop-ups. The goal is to extend the customer’s intent, not interrupt it.
Minimum Order Thresholds and Free Shipping
Free shipping thresholds are one of the most reliable ways to lift AOV. When set just above current average order value, they nudge customers to add one more item without feeling punitive. Thresholds should be tested and adjusted based on margin and fulfillment costs.
Volume Discounts and Tiered Pricing
Tiered pricing rewards customers for buying more while preserving margin at higher quantities. This approach works well for consumables and replenishable products, where customers already anticipate repeat use.
Personalization Based on Cart or History
Personalized recommendations tied to cart contents or past purchases feel more helpful than generic suggestions. Even simple rules-based personalization can outperform static cross-sells.
Guardrails to Protect Experience
Too many offers, discounts, or choices can overwhelm shoppers and suppress conversion. Track conversion rate, returns, and repeat purchase behavior alongside AOV to ensure gains are sustainable rather than extractive.
Advanced AOV Levers: Merchandising, UX, and Pricing Signals
Once foundational AOV tactics are in place, more advanced gains often come from subtle changes in merchandising, user experience, and pricing signals that shape how customers perceive value.
Anchoring and Price Framing
Anchoring influences how customers evaluate price by providing a reference point.
Showing a higher-priced option first, highlighting “most popular” tiers, or displaying original prices alongside bundled offers can make mid-range carts feel more reasonable without relying on deep discounts.
Strategic Placement of Higher-Value Items
Placement matters as much as pricing. Featuring higher-value products earlier in category pages, within collections, or as defaults in bundles increases their visibility and likelihood of inclusion.
This works best when placement aligns with customer intent rather than feeling promotional.
Subscriptions and Replenishment Models
Subscriptions can meaningfully increase AOV by converting single purchases into predictable, multi-item or multi-period commitments.
Replenishment prompts tied to usage patterns or reorder timing reduce friction and raise average order value while supporting retention.
Social Proof and Trust Signals
Larger carts increase perceived risk. Reviews, ratings, guarantees, and delivery transparency reduce hesitation and give customers confidence to spend more in a single transaction. Trust signals are especially important near cart and checkout.
Testing Cadence and Prioritization
Advanced AOV optimization requires disciplined experimentation. Prioritize tests based on potential impact and risk, and evaluate results alongside conversion, retention, and return rates to ensure gains reflect real value creation rather than short-term distortion.
When Not to Optimize AOV and What to Watch Instead
AOV optimization can backfire when it is pursued without regard for conversion, retention, or customer trust.
Aggressive upsells, forced bundles, or constant discounting may raise order value in the short term while suppressing conversion rates, increasing returns, or reducing repeat purchases.
These trade-offs often show up quietly: a rising AOV paired with declining customer satisfaction, longer payback periods, or higher refund rates.
Warning signs include first-time customers failing to return, post-promotion AOV collapsing, or customer support volume increasing around pricing and fulfillment.
In these cases, optimizing for revenue per visitor, repeat purchase rate, or customer lifetime value may be more appropriate than pushing order size.
The goal is not to maximize AOV at all costs, but to balance near-term gains with long-term value creation, ensuring higher orders reflect genuine customer benefit rather than friction or pressure.
AOV Measurement & Governance
Tracking AOV in isolation creates blind spots. Sustainable improvements require clear guardrails, supporting metrics, and shared standards across teams.
Core KPIs to Track Alongside AOV
Conversion rate to ensure higher order values are not suppressing purchases.
Revenue per visitor to connect AOV gains to traffic quality.
Repeat purchase rate to confirm higher orders support retention.
Customer lifetime value to measure long-term impact beyond single transactions.
Return and refund rate to catch friction or over-selling early.
Diagnostic Checks
Compare AOV for first-time vs returning customers to spot trust or onboarding issues.
Monitor AOV changes by channel after promotions to identify distortion from discounts.
Track cart abandonment trends following AOV initiatives to detect experience regressions.
Governance Considerations
Set discount controls and approval thresholds to prevent margin erosion.
Define experimentation standards and success criteria before launching tests.
Maintain data consistency and a regular reporting cadence so AOV insights remain trustworthy and actionable.
Recommended AOV Tools & Workflow
Improving AOV consistently requires the right data foundation, analysis capabilities, and cross-team coordination.
Data Layer
Order data with consistent revenue, discount, and refund handling.
Product data including pricing, bundles, and categories.
Customer attributes such as acquisition source, device, and purchase history.
Analytics
AOV calculation with clear definitions and time windows
Segmentation by channel, customer type, and product mix.
Trend tracking to monitor durability of AOV changes over time.
Experimentation
A/B testing for bundles, free shipping thresholds, and pricing structures.
Controlled rollouts to isolate AOV impact from seasonality or promotions.
Activation
On-site merchandising and cart-level recommendations.
Email and lifecycle triggers tied to cart contents or past behavior.
Workflow Handoffs
Marketing identifies opportunities and traffic context.
Merchandising and product implement offers and UX changes.
Analytics validates impact and monitors guardrails.
Treat AOV as a System, Not a Shortcut
Sustainable AOV growth comes from aligning pricing, merchandising, and experience in ways that genuinely serve the customer, not just squeeze out every last drop of value.
The most effective teams treat AOV as a system-level lever, informed by data, protected by guardrails, and tested continuously alongside conversion, retention, and lifetime value.
Spending an hour with leaders from Meta, TikTok, Pinterest, Snap, MNTN, and Vibe was a rare chance to compare notes on how people actually buy in 2026, and how far measurement still needs to catch up.
Here are the five takeaways I walked away with that matter most for performance teams trying to make sense of cross-channel reality.
Takeaway 1: The hard work is alignment between models and between marketing and finance
The last big theme was alignment on two levels:
1. Measurement stack alignment.
No one on the panel believes in a single, perfect source of truth anymore. The teams that feel most confident are triangulating:
MTA (including C+DV) for day-to-day trading and intra-channel decisions
MMM for longer-horizon allocation across channels, geos, and time
Incrementality testing for causal clarity when the stakes or spend justify it
In this stack, C+DV becomes the connective layer: verified platform signals (clicks + deterministic views) and real revenue living in the same place, so MMM and experiments can actually inform how you bid and budget tomorrow.
2. Org alignment between finance and marketing.
A recurring tension we heard:
CMOs and performance leads often know TikTok, Pinterest, Snap, and CTV are working because they see the lift in demand and new customers.
CFOs are staring at last-click or last-click reports that say the opposite.
Deterministic views help close that gap because they:
Reconcile to your actual ecommerce revenue, not a separate number
Deduplicate credit across channels, so you’re not double-counting
Make it clear why organic search and direct might lose credit when you reveal the upstream exposure that actually created demand
If you’re going to shift your attribution model, the most important work is up front: setting expectations that numbers will rebalance, agreeing on the windows that make sense by channel, and being transparent about why the story is changing, not just that it changed.
The brands I’m most bullish on are the ones willing to admit that clicks aren’t the full story, then do the hard work of operationalizing that truth in how they measure, report, and invest.
Takeaway 2: “View-through” only helps if the views are deterministic and reconciled to revenue
Most performance teams have a love/hate relationship with view-through metrics.
Everyone knows views matter. But “lift” that sits on top of already-inflated platform numbers is hard to defend in a room with your CFO.
What we discussed on the webinar was a more grounded approach: deterministic views joined to real orders.
In practice, that means:
Exposure captured by first-party data, and shared via what I like to think of as "cleanroom" integrations
Matched back to actual orders via order IDs and hashed emails
Joined at the user level, so each order has a concrete exposure history
That’s the core idea behind C+DV (Clicks + Deterministic Views):
Keep the deterministic click backbone. Clicks don’t go away.
Add only verified views from partner platforms. If we didn’t see it deterministically, we don’t use it.
Reconcile everything to ecommerce revenue, fully deduplicated. Each order can only be worth 100% of itself, no matter how many touchpoints it had.
Done right, this turns “view-through” from a fuzzy add-on into a second layer on top of clicks that closes the gap between how people behave and what your reporting shows.
Takeaway 3: Upper-funnel channels, like CTV, are more undervalued than most teams think
Once you start layering deterministic views on top of clicks, a consistent pattern shows up:
Campaigns that specialize in discovery and demand creation look meaningfully better than last-click tools suggested.
Channels that were already doing heavy lifting (Meta, TikTok, Snap, Pinterest) gain share once their view-driven impact is visible.
CTV moves from “brand line item” to measurable performance input. You can’t click your TV, so in a last-click system CTV basically didn’t exist. With deterministic views, you can finally see how often CTV shows up before the search, the retargeting impression, or the final click.
Lag analysis reinforces this. For CTV, one-day windows undercount badly; by the time you look at 7–30 day windows, CTV often shows up as one of the most important touchpoints in the path.
The exact lift will vary by brand and channel. The deeper point is structural: your current “single source of truth” is probably biased toward the bottom of the funnel. C+DV is one way of correcting for that by making genuine upper-funnel influence visible in the same framework as search and social.
Takeaway 4: When you see beyond the click, your creative and audience decisions change
A subtler, but important, impact is what happens to creative learning once you can see view-based influence.
In a last-click world, it’s rational to over-index on a small set of “safe”, hyper-clicky creatives and formats. Anything that does more upper-funnel work but fewer immediate clicks looks like a loser.
With deterministic views in the model, you can start to ask better questions:
Which creative themes reliably show up early in high-value paths, even if they don’t win the last click?
Which placements and environments (TikTok, Reels, Stories, Pinterest, Snap, CTV) are quietly seeding the audiences that later convert elsewhere?
How should we evaluate creators and content partners when we can see their upstream influence, not just CTR?
That doesn’t mean clicks stop mattering. It means you finally have permission to invest in true discovery and storytelling, because you can see how those exposures pay off downstream across channels.
Takeaway 5: Clicks still matter, but they systematically miss the moments that create demand
Everyone on the panel saw the same story from a slightly different angle.
A shopper sees a CTV ad, gets interested, and later searches the brand.
They scroll past a TikTok video, save an idea on Pinterest, or watch a Story on Snap without clicking.
Eventually they convert from a search ad or a direct visit days or weeks later.
If your source of truth only counts what got clicked, it’s going to:
Over-weight the last touch
Over-fund bottom-funnel
Underestimate the channels that actually generate net-new demand
That’s true whether you’re Meta, TikTok, Pinterest, Snap, or a CTV partner like Mountain and Vibe. The journeys they see in-platform simply don’t line up with what last-click tools are crediting.
The final, simple takeaway: clicks are necessary, but no longer sufficient, as the backbone of your measurement.
Marketing teams rely on analytics to make high-stakes decisions about budget allocation, campaign optimization, and attribution. But when marketing data is inaccurate, incomplete, or inconsistent, those decisions quickly become guesswork.
Data reliability is what turns dashboards into decision tools. It ensures that performance metrics reflect reality, attribution models assign credit correctly, and stakeholders can trust the insights guiding strategy.
In this article, we break down how to ensure data reliability in marketing analytics, where it commonly fails, and how teams can build systems and processes that support trusted, actionable insights at scale.
Why Data Reliability Matters in Marketing Analytics
Marketing teams make decisions every day based on what their data appears to be telling them. All of those decisions depend on one foundational assumption: that the data itself can be trusted.
Data reliability refers to the extent to which marketing data is accurate, complete, consistent, timely, and usable as a dependable basis for decision-making.
Reliable data reflects reality. It captures the full picture across channels, updates quickly enough to guide action, and uses consistent definitions so teams are not comparing mismatched numbers.
When those conditions are met, analytics becomes a decision engine rather than a reporting exercise.
But without reliable data, even sophisticated tools and models produce misleading outputs. This lack of trust is widespread. In one industry survey, more than a third of CMOs reported that they do not fully trust their marketing data.
When data reliability fails, the consequences compound quickly:
Budgets get misdirected toward channels that appear to perform well but do not actually drive incremental value
Different teams report conflicting numbers, leading to debates about whose data is “right” rather than what action to take
Over time, stakeholders lose confidence not just in the data, but in marketing’s ability to justify its impact
Reliable data is not a nice-to-have. It is the foundation of credible marketing analytics reliability.
Key Dimensions of Reliable Marketing Data
Reliable marketing data is the result of several foundational qualities working together. When one dimension breaks down, trust in the entire dataset weakens.
Understanding these dimensions helps teams diagnose where reliability issues originate and what needs to be fixed to restore confidence.
Accuracy
Accuracy answers a basic question: does the data reflect correct values? This includes spend, impressions, clicks, conversions, and revenue.
Inaccurate data often stems from tracking misfires, platform discrepancies, or flawed integrations. Even small inaccuracies can cascade into large decision errors when multiplied across campaigns and budgets.
Completeness
Completeness measures whether all relevant data points are captured. Missing channels, dropped conversion events, or partial funnel visibility create blind spots that skew performance analysis.
Data can appear clean while still being incomplete, which makes this one of the most deceptive reliability issues.
Consistency and Format
Consistency ensures that metrics mean the same thing everywhere they appear. A “conversion” should not be defined differently across platforms, dashboards, or time periods.
Inconsistent definitions make comparisons unreliable and erode trust between teams reviewing the same performance from different views.
Timeliness and Freshness
Timely data arrives quickly enough to inform decisions. Stale or delayed data forces teams to react to yesterday’s performance instead of today’s reality. Freshness is especially critical for pacing, optimization, and in-flight campaign adjustments.
Usability and Accessibility
Data must be easy to access, interpret, and act on. If insights require manual extraction, complex explanations, or technical translation, trust breaks down and adoption suffers.
Validity
Validity confirms that transformations, joins, and logic rules correctly represent real-world behavior. Even accurate and complete source data can become unreliable if the underlying data logic is flawed.
Common Sources of Unreliable Marketing Data
Understanding where marketing data typically breaks down makes it easier to prevent errors before they undermine reporting, attribution, and decision-making.
Inconsistent Definitions Across Platforms
One of the most common issues is inconsistent metric definitions.
As mentioned in the last section, a “conversion” might represent a purchase in one system, a form fill in another, and a modeled event elsewhere.
When teams compare or combine these numbers without alignment, the resulting analysis is fundamentally flawed.
Disparate Systems and Siloed Data
Marketing data often lives across multiple platforms, including ad networks, analytics tools, and CRM systems. Without proper integration, these systems produce mismatched views of performance.
Spend, clicks, and revenue fail to line up, leading to confusion and manual reconciliation that erodes trust.
Manual Data Handling
Copying and pasting data between spreadsheets, tools, or decks introduces human error and delays. Manual workflows also make it difficult to audit changes or understand how numbers were calculated, reducing transparency and repeatability.
Outdated or Stale Data
Making decisions based on stale or outdated data is especially damaging for campaign optimization, pacing, and budget reallocation, where timing directly impacts results.
Weak Data Governance
Without clear ownership, standardized processes, and validation checks, reliability becomes reactive rather than systematic.
Poor governance allows small inconsistencies to persist, making it harder to identify the source of errors and restore confidence once trust is lost.
Building a Framework for Reliable Marketing Analytics
Reliable marketing data requires intentional systems, clear ownership, and repeatable processes that make trust the default rather than the exception.
The following steps help teams move from ad hoc fixes to a durable reliability framework:
1. Define a Single Source of Truth
Establish a shared, governed view of core marketing metrics. This does not always mean one tool, but it does mean one agreed-upon pipeline, set of definitions, and reporting layer that everyone uses when making decisions.
2. Establish Strong Data Governance
Clear governance ensures consistency over time. Define metric definitions, naming conventions, ownership, access controls, and audit processes.
When responsibilities are explicit, issues are easier to trace and resolve.
3. Implement Data Validation and Cleaning
Regular validation prevents small issues from becoming systemic failures. Use automated checks to flag missing values, duplicates, unexpected spikes, or format mismatches. Schedule audits to confirm data remains accurate as systems evolve.
4. Automate and Standardize Pipelines
Automation reduces human error and improves repeatability. ETL or ELT pipelines help integrate data across platforms while enforcing consistent transformations, making outputs easier to trust and scale.
5. Set Up Monitoring and Alerting
Monitoring turns reliability into an ongoing process. Alerts for anomalies, failed data feeds, or sudden metric shifts allow teams to respond quickly before flawed data influences decisions.
6. Provide Access and Transparency
Dashboards should not just show numbers. They should help stakeholders understand where data comes from, how it is calculated, and when it was last updated.
7. Build a Culture of Data Trust
Technical fixes alone are not enough. Invest in training, involve marketing teams in data design, and reinforce data-informed decision-making across the organization.
Reporting & Attribution: Ensuring Trust in Your Marketing Metrics
Reporting and attribution outputs directly influence budget decisions, performance narratives, and executive confidence. If the underlying data is unreliable, even the most advanced attribution models will produce misleading results, giving teams a false sense of precision.
Here’s how to build trust in marketing data for attribution and reporting:
Confirm Reliable Inputs
Attribution models, whether last-click, multi-touch, or marketing mix modeling, are only as strong as the data they consume. Flawed channel data, missing conversions, or inconsistent definitions invalidate attribution outputs, regardless of the sophistication of the model itself.
Cleanly Link Spend to Outcomes
Reliable reporting depends on clean, validated connections between ad spend, impressions, clicks, conversions, and revenue.
When these elements are not accurately joined, channels receive too much or too little credit, leading to distorted performance assessments and misallocated budget.
Run Checks Before Attribution
Before running attribution analyses, teams should validate the underlying data. This includes comparing platform-reported metrics, checking funnel logic, and testing for anomalies or sudden shifts that signal data issues rather than real performance changes.
Surface Data Quality in Reports
Trust increases when stakeholders understand data quality. Including reliability indicators such as data completeness, refresh latency, known gaps, or confidence levels helps leaders interpret results appropriately and reduces friction when numbers change.
Validate Insights with Incrementality
Attribution outputs should be tested, not taken at face value. Incrementality metrics, hold-out tests, and controlled experiments help confirm whether attributed channels are truly driving growth, providing a reality check that strengthens confidence in reported results.
Challenges & Trade-Offs
Putting in place marketing data reliability best practices for analytics teams delivers long-term value, but it also introduces real trade-offs that teams need to plan for.
Understanding these challenges upfront helps set realistic expectations and prevents reliability efforts from stalling or losing stakeholder support:
Slower Reporting in the Short Term
Investments in data cleaning, validation, and governance can initially slow down reporting cycles. Teams may need to pause automation or delay insights while issues are corrected, which can feel uncomfortable in fast-moving environments.
Risk of Over-Optimizing for Perfection
Chasing perfectly clean data can lead to analysis paralysis. Not every decision requires flawless inputs. Teams must define what “good enough” looks like for different use cases and avoid blocking action unnecessarily.
Constant Platform and Privacy Changes
Marketing ecosystems evolve quickly. Privacy regulations, identity changes, and platform updates can break existing pipelines, making data reliability an ongoing effort rather than a one-time fix.
Stakeholder Impatience
Leaders and channel owners may push for immediate answers, even when data quality is uncertain. Without alignment, this pressure can undermine reliability initiatives and reintroduce manual shortcuts.
Balancing Speed with Trust
The core tension is between agility and rigor. Effective teams design systems that surface reliable insights quickly, while clearly signaling when data should be treated with caution.
How Clean Data Changes the Story
At first glance, a brand’s paid media performance looked strong. Cost per click was low, reported ROAS was high, and attribution dashboards suggested several channels were outperforming expectations.
Based on those numbers, the team began preparing to scale spend.
But after a deeper review, they discovered multiple data reliability issues. Duplicate conversions were inflating results, and delayed data refreshes were masking recent performance drops.
Once those integration errors were corrected and pipelines were cleaned, reported ROAS fell by 15%. The numbers looked worse, but they were finally accurate.
Rather than reverting to the old view, the team invested in reliability. They implemented clearer data governance, standardized pipelines, and daily validation alerts to catch issues early.
Dashboards were updated to include a simple data health score that surfaced completeness, latency, and error rates. Failed refreshes were annotated, and metric definitions were versioned so changes were transparent.
With trustworthy data in place, decision-making improved.
The team paused channels that had been benefiting from misattribution, reallocated budget toward channels that were genuinely driving incremental value, and scaled spend with confidence.
Growth stabilized, and stakeholder trust in marketing analytics was restored.
Turning Data Reliability Into Action
Building reliable marketing data starts with a focused set of actions that make reliability visible, repeatable, and accountable across teams.
Run a data reliability audit: Map your core marketing and attribution metrics end to end. Identify where data originates, how it flows through systems, and where it is transformed. Score each metric on accuracy, completeness, freshness, and consistency to surface risk areas.
Prioritize the biggest failure points: Focus on the top two or three issues causing the most damage, such as delayed refreshes, inconsistent definitions, or manual handoffs. Assign clear owners and timelines so fixes actually get implemented.
Add a data health dashboard: Make reliability visible. Track indicators like data freshness, error rates, missing fields, and completeness alongside performance metrics. This creates shared awareness and prevents silent failures.
Include reliability signals in stakeholder reporting: Help leadership interpret results by surfacing confidence indicators directly in reports. When data has known gaps or delays, flag them clearly so decisions are made with appropriate context.
Establish a regular review cadence: Data reliability is not a one-time project. Revisit definitions, pipelines, and checks monthly or quarterly to adapt to new platforms, campaigns, and measurement needs.
By operationalizing data reliability, teams can move faster with confidence and ensure performance insights truly reflect reality.
SaaS email marketing plays a critical role in how users activate, engage with, and continue using a product over time.
Unlike traditional email campaigns focused on promotions or announcements, effective SaaS email strategies are deeply connected to product usage and user behavior across the customer lifecycle.
In this guide, we break down how SaaS teams can design email programs that support onboarding, drive feature adoption, and improve retention.
You will learn how to build lifecycle-aware email flows, measure performance using product and revenue metrics, and use attribution to understand how email contributes to activation, upgrades, and long-term growth.
Why Email Marketing is Critical for SaaS
For SaaS businesses, growth does not end at acquisition. A new signup only becomes valuable when that user activates, adopts key features, and continues to use the product over time. This makes the post-signup experience just as important as demand generation, and email remains one of the most effective channels for influencing what happens after the first click.
Done well, email helps users onboard faster, guides them toward deeper usage, and supports retention and renewal, all at a relatively low cost compared to paid media or sales-driven outreach.
SaaS email marketing also looks fundamentally different from traditional email marketing. Instead of periodic newsletters or one-off promotional blasts, SaaS teams rely heavily on automation and behavior-based triggers.
Emails are sent in response to real user actions, or inaction, such as signing up for a free trial, skipping a key setup step, trying a feature for the first time, or going inactive for a set period.
Because of this tight connection to the product, email performance in SaaS must be measured differently as well. Open and click rates provide limited insight on their own. To understand impact, email campaigns need to be directly linked to user actions, product metrics, and value events like activation, feature adoption, upgrades, and retention.
When email is connected to these downstream outcomes, it becomes a measurable growth lever rather than just a communication channel.
Lifecycle Phases & Email Strategy Breakdown
Lifecycle Phase
Goal
Key Tactics
Onboarding & Activation
Help new users reach their first "aha" moment; reduce time-to-value and increase activation rate
Effective SaaS email marketing is built around the user lifecycle. By dividing the journey into clear stages, teams can design email strategies that meet users where they are, reinforce the right behaviors at the right time, and move them steadily toward long-term value.
Onboarding & Activation
Goal
Help new users reach their first “aha” moment quickly
Reduce time-to-value and increase activation rates
Key tactics
Welcome email or short welcome sequence
Setup checklists to guide initial configuration
Feature walkthroughs focused on core value drivers
Milestone emails that acknowledge progress and reinforce momentum
Structured sequences that move from welcome → activation → feature introduction → progress tracking
Email trigger examples
User signs up or starts a free trial
User does not complete a key setup step within a defined timeframe
User completes their first major product action
Metrics to track
Activation rate
Time to first value
Drop-off rate before the first key action
Engagement & Adoption
Goal
Deepen product usage
Encourage habitual behavior
Unlock premium features and grow value per user
Key tactics
Behavior-based emails suggesting next features or actions
“You haven’t tried X yet” nudges based on usage gaps
Educational content, tutorials, and best practices
Case studies or use cases aligned to the user’s role or plan
Milestone celebrations to reinforce progress and consistency
Email segmentation examples
Usage frequency (power users vs light users)
Plan tier or subscription level
Features used vs features not yet adopted
Metrics to track
Active user counts
Feature adoption rates
Session recency and frequency
Free-to-paid conversion rate, if applicable
Retention, Renewal, & Re-Engagement
Goal
Prevent churn
Drive renewals and expansions
Reclaim inactive or at-risk users
Key tactics
Win-back campaigns triggered by inactivity
Renewal reminders tied to subscription timelines
Loyalty or appreciation emails for long-term customers
Referral or advocate-driven campaigns
Upsell and cross-sell messages based on demonstrated usage
Metrics to track
Churn rate
Renewal rate
Aggregate lifetime value
Reactivation rate of dormant accounts
Email Strategy & Implementation Framework
This framework outlines the core steps teams should follow to design, measure, and scale lifecycle-driven email programs.
1. Define Your Goals and Success Metrics
Start by clearly defining what success looks like for each lifecycle stage. Set specific targets for activation rates, reductions in churn, increases in feature adoption, or improvements in conversion from free to paid.
These goals should be directly tied to business outcomes, not just email engagement metrics, so performance can be evaluated in terms of real impact.
2. Segment Your Audience
Segment users based on meaningful differences in their journey and behavior. Common segmentation dimensions include lifecycle stage, product usage patterns, plan tier, and features used.
Strong segmentation ensures that emails remain relevant and timely, which is critical for driving behavior change rather than fatigue.
3. Map Workflows and Triggers
For each lifecycle stage, define the email sequences, triggers, and conditional logic that determine when messages are sent. Identify primary triggers, such as signups or inactivity, as well as fallback flows for users who do not complete key actions.
Clear workflow mapping helps ensure users receive the right message at the right time.
4. Craft Value-First Content
Tailor email content to the user’s current needs and level of maturity. Onboarding users may need guidance and reassurance, while power users may benefit from advanced tips or new features.
Focus on delivering value and clarity rather than promotional messaging, especially in early and mid-lifecycle stages.
5. Set Up Tracking and Attribution
Connect email engagement data to downstream product events such as setup completion, feature usage, upgrades, or renewals. This linkage allows teams to attribute changes in activation, retention, and revenue directly to specific email flows and campaigns.
6. Measure and Test Continuously
Monitor performance across both email-level metrics and product outcomes. Track open and click rates alongside conversion events and drop-off points. Use A/B testing to refine subject lines, content, and workflows based on measurable improvements.
7. Iterate and Scale What Works
Use performance insights to refine segmentation, introduce dynamic content, and expand behavior-based triggers. As programs mature, teams can scale successful flows and explore AI-driven personalization to further optimize relevance and impact.
Attribution & Performance Metrics Specific to SaaS Email Marketing
To understand whether email is truly driving growth in SaaS, teams need to measure performance through the lens of product behavior and revenue outcomes, not just email engagement.
Focus on Business Outcomes, Not Vanity Metrics
Open and click rates offer limited insight on their own. In SaaS, email success should be measured by its impact on activation, feature adoption, upgrades, retention, and churn reduction.
These metrics reflect whether emails are influencing meaningful user behavior and long-term value.
Account for Multi-Touch Attribution Complexity
Email typically works alongside other channels such as in-product messaging, sales outreach, and paid acquisition. Attribution models should account for this by tracking how email contributes across the user journey over time, even when it is not the final touchpoint before conversion or renewal.
Use Funnel-Based Measurement
A funnel-based approach makes attribution more actionable. By tracking how email interactions lead to product actions and ultimately to upgrades or renewals, teams can identify where email is accelerating progress and where users are stalling.
Build Lifecycle-Aligned Dashboards
Dashboards should reflect the SaaS lifecycle rather than isolated email metrics.
Useful views include cohorts grouped by email flow entry, activation rates by cohort, time to first key action, 30-, 60-, and 90-day retention, and upgrade rate differences between engaged and non-engaged users.
Compare Outcomes to Prove Impact
Comparative analysis helps demonstrate email’s true value. For example, comparing users who completed onboarding email sequences with those who did not can reveal retention or activation uplift that can be attributed directly to email programs.
Common Pitfalls & What to Avoid
Even well-intentioned SaaS email programs can underperform if they fall into common traps that limit relevance, measurability, and long-term impact.
Sending Generic Email Blasts
Tailor your strategy to your specific goals, such as using email campaigns to reduce churn in SaaS. Relying on one-size-fits-all campaigns instead of lifecycle-aware, behavior-based flows leads to lower user engagement.
Over-Focusing on Opens and Clicks
Measuring success only through user engagement on SaaS emails obscures whether emails are driving activation, adoption, or retention.
Ignoring Segmentation and Product Behavior
Failing to tailor emails based on lifecycle stage, usage patterns, or plan tier results in irrelevant messaging and missed opportunities.
Not Linking Email Behavior to Product Events
When email activity is disconnected from product data, teams cannot accurately measure the impact on activation, upgrades, or retention from SaaS email marketing campaigns.
Neglecting Re-engagement Campaigns
Without investing in how to keep SaaS users engaged via email with proactive win-back and inactivity-triggered emails, users can disengage and churn without clear warning signals.
Overlooking Deliverability and Data Hygiene
Poor list management, outdated data, or deliverability issues can suppress open rates and undermine even well-designed email programs.
A Practical Example of Lifecycle-Driven SaaS Email
Consider a company implementing a SaaS email marketing strategy for onboarding free trial users in order to improve long-term retention.
Rather than relying on a single welcome message, the team designed a structured five-email onboarding sequence aligned to early product behavior:
Day 1: Users received a welcome email
Day 2: Users received a setup guidance email
Day 4: Users received an email introducing a core featured tied to the product’s primary value
Day 7: Users who had not yet tried the feature received a behavioral checkpoint email highlighting what they were missing
Day 14: Engaged users were presented with a targeted upgrade offer
By tracking product usage alongside email engagement, the team found that users who reached the day four feature email converted to paid plans at a 40% higher rate within 30 days compared to those who did not.
Beyond onboarding, the company applied similar logic to retention. Users who went inactive for 14 days triggered a re-engagement email offering a short webinar or feature tip, which ultimately reduced churn by 12%.
Measurement also informed iteration. Analysis showed that users from one acquisition source consistently activated at lower rates. In response, the team created a separate onboarding path tailored to that cohort’s needs and expectations.
Using product analytics combined with email tagging, they demonstrated that users who completed the full onboarding flow achieved 60% higher retention at 90 days versus the baseline, clearly linking lifecycle email strategy to measurable business impact.
Turning Email Into a SaaS Growth Engine
Email remains one of the most powerful levers SaaS teams have to influence user behavior after signup. When aligned to the lifecycle and informed by product data, email can accelerate activation, deepen engagement, and protect long-term revenue through retention and renewal.
By defining clear goals, segmenting thoughtfully, and continuously testing and iterating based on real user behavior, SaaS teams can build email programs that scale alongside the product and deliver lasting impact across the customer lifecycle.
Digital advertising often feels instantaneous, but every ad impression passes through a complex operational process before it ever reaches a user.
Ad operations ensures that ads are delivered correctly, tracked accurately, and measured reliably. Understanding ad ops is critical for attribution and marketing teams to diagnose performance issues, reduce wasted spend, and make more confident optimization decisions.
What Is Ad Operations & Why It Matters
Ad operations, often shortened to ad ops, is the behind-the-scenes function that makes digital advertising actually work. While media strategy defines who you want to reach and creative defines what they see, ad ops ensures those ads are delivered correctly in the real world.
It is the combination of systems, people, and processes responsible for trafficking campaigns, serving ads to the right audiences, applying the correct formats and specifications, and making sure every impression, click, and conversion is tracked as intended.
When ad operations break down, performance suffers in ways that are not always obvious at first. Ads may serve in the wrong sizes or placements, miss intended audiences, fail to pace properly against budget, or fire incomplete or incorrect tracking tags.
These issues can lead to under-delivery, wasted spend, or data gaps that distort performance reporting. Even strong creative and smart targeting cannot compensate for delivery or tracking errors introduced at the operational level.
For marketing and attribution teams, ad ops is foundational. Attribution models, conversion paths, and cost metrics are only as reliable as the delivery and tracking infrastructure beneath them.
Strong ad operations create the conditions for trustworthy measurement, better optimization decisions, and tighter control over acquisition costs.
Key Steps in the Digital Ad Delivery Workflow
Digital ads do not simply “go live” once a campaign is approved. Each impression passes through a defined operational workflow that determines where, when, and how an ad is delivered, as well as whether its impact can be accurately measured.
Campaign Setup & Trafficking
Intake of the campaign brief or insertion order, including targeting requirements, budget, flight dates, and performance goals.
Upload of creative assets into the ad server or buying platform, with correct sizes, formats, and specifications applied.
Configuration of targeting parameters, pacing rules, and scheduling.
Implementation of ad tags, click URLs, tracking parameters, and attribution identifiers to ensure downstream measurement.
Ad Serving & Delivery
Use of an ad server or ad-tech platform, such as a DSP or SSP, to serve ads when a user meets the defined targeting criteria.
In programmatic environments, real-time bidding and exchange auctions occur in milliseconds to determine which ad is shown.
Ongoing monitoring of delivery health, including impressions served, viewability, and pacing against budget.
Monitoring, Optimization, & Quality Assurance
Continuous review of delivery performance to identify under-delivery, over-delivery, placement issues, or creative errors.
Optimization actions such as refining audiences, swapping creatives, or reallocating budget to improve results.
Application of ad quality controls and fraud detection to maintain brand safety and eliminate invalid traffic.
Reporting & Data Integration
Flow of delivery and engagement data from ad servers and tracking tags into analytics and attribution systems.
Linking of impressions and clicks to downstream conversions for attribution analysis.
Use of delivery insights and tracking gaps to inform improvements in future campaigns.
Key Systems, Roles, & Technologies in Ad Ops
Ad operations sits at the intersection of technology and execution, relying on a complex stack of platforms and specialized roles to ensure campaigns run smoothly.
At the core are ad servers, which store creative assets, apply targeting rules, and govern when and how online ads get delivered.
Around them sit demand-side platforms and supply-side platforms, which connect advertisers and publishers through ad exchanges, enabling programmatic buying and real-time auctions.
Tag management systems, tracking pixels, and attribution tools then capture delivery and engagement data, feeding it into analytics and measurement stacks.
Supporting this technology are distinct operational roles:
Ad traffickers handle campaign setup, creative uploads, and tagging, translating media plans into executable configurations.
Ad operations managers oversee how digital ads get delivered step-by-step, troubleshooting issues and coordinating optimizations across platforms.
On the publisher side, yield or monetization managers focus on maximizing inventory value while maintaining quality.
Analytics and attribution teams rely on accurate ad ops process and execution to ensure delivery data aligns with conversion reporting and cost metrics.
This ecosystem also introduces ongoing technical challenges. Cross-device and cross-browser delivery make it difficult to consistently identify users and connect impressions to outcomes.
Programmatic complexity, including header bidding and multi-exchange paths, increases the number of systems involved in each impression. Integrating these platforms with analytics and attribution tools adds another layer of risk, where small configuration errors can lead to missing or misaligned data.
Strong ad operations practices help teams navigate this complexity and maintain reliable delivery and measurement at scale.
Common Ad Ops Challenges & Attribution Implications
Understanding these common challenges helps marketing and attribution teams recognize when performance problems stem from execution rather than strategy or creative:
Delivery Issues That Affect Measurement
Under-delivery, creative disapprovals, incorrect formats, or misconfigured targeting can prevent campaigns from reaching their intended audiences. Pacing issues may cause spend to accelerate or stall unexpectedly, distorting impression volume and reach.
When ads do not serve as planned, performance metrics are skewed, making it difficult to evaluate true effectiveness or compare results across channels.
Tracking & Attribution Gaps
If impressions, clicks, or post-click events are not consistently tagged and captured, attribution models break down. Missing or incomplete delivery data can lead to mis-credited channels, underreported conversions, or inflated costs.
These gaps create false signals in reporting and reduce confidence in ROI, CPA, and conversion path analysis.
Programmatic Complexity
Programmatic buying introduces multiple intermediaries, automated auctions, and dynamic inventory sources. While this enables scale and efficiency, it also increases the risk of ads appearing in unintended contexts or on low-quality inventory.
Without strong ad ops oversight, these placements can harm brand safety and introduce noise into attribution data.
Data Latency & Freshness
Delayed or slow data flows limit a team’s ability to optimize in near real time. When delivery and conversion data arrive hours or days later, optimizations lag behind performance, and attribution insights become less actionable.
Strong ad operations practices reduce these issues, supporting more accurate cost-per-acquisition, ROAS, and attribution modeling.
Ad Delivery in Action: From Impression to Attribution
To see how ad operations connect delivery and measurement, consider this example of a brand launching a display campaign via a demand-side platform.
The campaign begins with setup. Targeting criteria are defined, creative assets are uploaded in the required sizes, and tracking tags and attribution parameters are applied. Once the campaign goes live, the DSP begins bidding on available inventory through ad exchanges.
When a user who matches the targeting criteria visits a publisher’s site, an automated auction takes place in milliseconds. If the brand’s bid wins, the ad is served. At that moment, impression-level tracking fires, recording where and how the ad was delivered.
If the user clicks the ad, additional tracking captures engagement. If they convert later, whether that is a purchase, signup, or download, attribution systems connect that conversion back to the original impression or click.
Throughout the campaign, the brand’s ad ops team monitors delivery data alongside quality metrics. If a specific placement shows low viewability or a high bounce rate, they may pause or exclude it from the buy. That adjustment improves the quality of future impressions, reduces wasted spend, and strengthens the signal flowing into attribution reports.
In this way, ad operations does not just ensure ads are served. It actively shapes the accuracy of performance reporting and the effectiveness of ongoing optimization.
Actionable Next Steps for Marketing & Attribution Teams
Improving ad operations does not require rebuilding your entire tech stack. Small, deliberate changes to process, visibility, and collaboration can significantly improve delivery reliability and attribution accuracy.
1. Map Your End-to-End Delivery Workflow
Work with your ad ops or ad tech partners to map out the ad operations workflow for marketers. Document the entire lifecycle of a campaign, from initial brief and creative development through trafficking, delivery, tracking, and attribution. This exercise helps surface handoff points where errors or data loss are most likely to occur.
2. Audit Campaign Delivery Performance
Review recent campaigns for recurring delivery issues such as under-delivery, low pacing, creative rejections, or incorrect formats. Identify how often these problems occur and assess how they may have distorted impression volume, reach, or reported performance.
3. Connect Tracking to the Delivery Layer
Confirm that ad impressions, clicks, and tracking parameters are consistently passed from ad servers into analytics and attribution systems. Gaps between delivery data and conversion reporting often signal tagging or integration issues that undermine measurement.
4. Build Unified Delivery and Performance Dashboards
Create dashboards that combine delivery health metrics, including impressions delivered versus forecast, viewability, delivery latency, and creative failures, with core performance KPIs such as CPA and ROAS. Seeing these metrics together enables faster diagnosis of performance issues.
5. Establish Ongoing Cross-Team Reviews
Set a regular cadence for ad ops, media buying, and attribution teams to review delivery quality, optimization decisions, and measurement integrity. Consistent collaboration helps prevent small operational issues from becoming costly reporting problems.
Why Ad Operations Is an Advantage
Ad operations is often invisible when everything works, but it plays a decisive role in whether performance data can be trusted.
Every impression served, every click tracked, and every conversion attributed depends on the systems and processes that govern ad delivery. When ad ops is treated as a strategic function, teams gain clearer insight into what is actually driving results.
Customer acquisition is rarely a straight line. Prospects move across channels, touchpoints, and moments of intent, making it difficult to understand what actually drives conversion.
This customer acquisition funnel guide for marketers breaks down how to build a system so you can align channels, tracking, and attribution and turn growth into a repeatable, measurable system.
What a Customer Acquisition Funnel Is & Why It’s Critical
A funnel for customer acquisition is a structured way to map how potential customers move from first awareness of your brand to conversion, and in many cases beyond that initial purchase.
Instead of treating acquisition as a single moment or channel, the funnel breaks the journey into stages, each with a clear goal, message, and action.
Common stages include awareness, interest, consideration or evaluation, intent, and conversion. The exact labels may vary, but the purpose is the same: to make an otherwise messy, multi-touch journey understandable and actionable.
This structure matters because it brings alignment and focus to growth efforts. When teams agree on funnel stages, marketing and sales can align on what a qualified prospect looks like at each point and what should happen next.
It then becomes easier to spot where prospects drop off, where messaging is unclear, or where friction is slowing momentum.
Instead of optimizing in isolation, teams can improve funnel efficiency stage by stage, measuring conversion rates, drop-off, and cost at each step. That clarity is what turns acquisition from a guessing game into a repeatable system.
Invest in purpose-building your customer acquisition funnel for paid traffic and attribution. Every funnel stage implies specific touchpoints and behaviors, such as ad clicks, landing page views, form submissions, demo requests, or purchases.
When you map the funnel, you also define which events need to be tracked and how they connect across channels and systems. This makes it possible to understand not just which channel generated traffic, but which interactions actually contributed to revenue.
In practice, that leads to clearer ROI measurement, better budget allocation, and more confident optimization decisions across the entire acquisition motion.
How to Build a Customer Acquisition Funnel (Step-by-Step)
The steps below break the process of customer acquisition funnel building into practical, repeatable actions.
Define Your Ideal Customer & Journey Entry Points
Develop clear buyer personas, including demographics, firmographics where relevant, pain points, motivations, behaviors, and preferred channels.
Identify how and where prospects first encounter your brand, such as paid ads, search, referrals, social content, or partnerships.
Map initial entry points visually to understand how different audiences enter the funnel and where they should go next.
Define Funnel Stages & Corresponding Tactics
Establish your funnel stages, typically Awareness, Interest, Consideration or Evaluation, Intent, and Conversion.
For each stage, define the prospect’s goal, the action you want them to take, and the message or value proposition that supports it.
Align tactics to stage, for example paid search or display at Awareness, content or email nurture at Interest, and optimized checkout or signup flows at Conversion.
Select Channels & Tactics for Each Stage
Map which channels and tactics feed each funnel stage, including paid media, organic search, retargeting, referrals, and email.
Align creative format and messaging to stage, recognizing that early-stage education differs from late-stage decision support.
Consider how attribution will capture each channel’s contribution across stages, not just at the final conversion.
Set Up Tracking & Attribution for the Funnel
Define key events and micro-conversions at each stage, such as ad clicks, landing page views, form submissions, demo requests, or purchases.
Ensure proper instrumentation, including UTMs, unique identifiers, and integration between ad platforms, analytics tools, and CRM systems.
Align funnel events with your attribution model, whether first touch, last touch, or multi-touch, and establish baseline metrics like stage conversion rate, drop-off, CPA, and CAC.
Build the Funnel Flow & Experience
Design the end-to-end user path, such as ad to landing page, landing page to nurture, nurture to offer, and offer to conversion.
Optimize transitions between steps by reducing friction, clarifying value propositions, and using consistent messaging and CTAs.
Use automation and segmentation to tailor the experience based on funnel stage, behavior, or acquisition source.
Launch, Monitor & Optimize
Launch the funnel using a mix of paid and organic traffic to generate early data.
Monitor performance by stage, focusing on conversion rates, drop-off points, CPA, CAC, and time to conversion.
Identify bottlenecks and iterate through testing, including creative, landing pages, retargeting logic, offers, and segmentation.
Scale and Expand
Scale spend and effort on high-performing channels and tactics while refining or reducing lower-performing ones.
Extend the funnel beyond acquisition by adding retention, expansion, or upsell paths where appropriate.
Revisit tracking and attribution regularly to ensure measurement stays accurate as channels, tactics, and customer behavior evolve.
Metrics, Attribution, & Dashboarding
Once your acquisition funnel is defined and live, measurement is what turns it into a system you can actually manage.
The goal is not to track everything, but to track the metrics that show how prospects move through each stage, where value is created, and where friction or waste exists. At a minimum, this means monitoring top-of-funnel volume, mid-funnel progression, and bottom-of-funnel efficiency in a way that connects activity to outcomes.
Core metrics should map directly to funnel stages.
At the top of the funnel, track impressions and website visits to understand reach and demand generation.
As prospects move forward, monitor click-through rate, lead form or content conversion, and demo or trial sign-ups to measure engagement and intent.
At the bottom of the funnel, focus on purchase or activation conversion, customer acquisition cost, and time to conversion.
Looking at these metrics stage by stage makes it easier to isolate problems and avoid over-optimizing for traffic at the expense of revenue.
Because acquisition funnels span multiple channels and touchpoints, attribution is critical. Event tracking should be implemented consistently across stages so you can see the full conversion path, not just the final interaction.
Multi-touch attribution models are often better suited to this reality, as they help surface which channels introduce demand, which nurture it, and which close it. The objective is to understand contribution, not just credit, so budget and strategy decisions reflect real impact.
Dashboards should make these insights visible and actionable. Common views include a stage-by-stage lead-to-customer funnel conversion cascade, a drop-off heatmap to highlight friction points, and channel performance broken out by funnel stage.
Cost metrics layered against conversion rate and time to convert are especially useful for identifying efficiency gains. Where possible, use hold-out or control groups to test new channels, messages, or funnel changes, allowing you to measure incremental lift rather than assuming correlation equals impact.
Common Pitfalls & What to Avoid
The pitfalls below are some of the most common reasons funnels fail to deliver measurable, scalable results.
Vague or Undefined Funnel Stages
When funnel stages are loosely defined or inconsistently named, measurement quickly breaks down. Teams struggle to agree on what success looks like at each stage, which leads to unclear reporting and unreliable attribution.
Clear stage definitions are essential for understanding progression, drop-off, and performance.
Missing or Incomplete Tracking
If key events are not instrumented, especially at transitions between stages, you lose visibility into where prospects drop off. Gaps in tracking make it impossible to diagnose issues or prove impact.
Every meaningful action in the funnel should be tracked and connected across systems.
Over-Focusing on Top-of-Funnel Traffic
Driving traffic without equal attention to conversion and retention often leads to rising costs and disappointing returns. A healthy funnel optimizes volume and efficiency together.
Ignoring mid- and bottom-of-funnel performance creates the illusion of growth without sustainable results.
Misaligned Channels and Funnel Stages
Using the wrong tactic at the wrong stage creates friction and wastes budget. Messaging geared at awareness at the conversion stage, or aggressive sales CTAs too early in the journey, can push prospects away.
Channels, creative, and offers should always match the intent of the funnel stage.
Launching Without Attribution Logic
Launching a funnel before defining attribution rules makes optimization guesswork. Without a clear model in place, teams cannot reliably connect spend to outcomes or understand which tactics are driving value.
Attribution should be considered part of funnel design, not an afterthought.
Ignoring Funnel Drop-Off
Assuming that all traffic will eventually convert hides real performance issues. Drop-off is inevitable, but it should be measured and managed. Each stage deserves attention to reduce friction and improve progression.
Failing to Revisit and Evolve the Funnel
Customer behavior, channels, and platforms change over time. Funnels that are not revisited and adjusted gradually lose effectiveness. Regular review and iteration are necessary to keep acquisition strategies aligned with how people actually buy.
Case Study: Turning Funnel Visibility Into Lower CAC
A SaaS company selling a subscription product to small business owners wants to bring structure to its acquisition efforts. The team starts by defining a clear primary persona: owners who are cost-conscious, time-limited, and actively searching for tools to improve efficiency. From there, they design a funnel that reflects how this audience actually evaluates software.
At the top of the funnel, the company runs paid search and paid social campaigns to drive awareness and capture initial interest. Prospects who click through are offered a free downloadable guide, which serves as the primary interest-stage conversion.
For consideration, the funnel directs engaged users to case studies and a demo request. At the bottom of the funnel, qualified prospects are invited to start a free trial, with the goal of converting them into paid subscribers.
To measure performance, the team instruments key events across the funnel, including guide downloads, demo requests, trial starts, and paid conversions.
Early reporting reveals a significant drop-off between the guide download and demo request stages. While top-of-funnel traffic is strong, too few prospects are progressing into active evaluation.
In response, the team adjusts its messaging to better bridge the gap between education and product value. They introduce a targeted email nurture sequence that follows the guide download, reinforces the use case, and clearly positions the demo as the next logical step.
Within weeks, the conversion rate from guide download to demo request improves meaningfully.
Attribution analysis shows that paid search continues to drive the majority of trial starts, while paid social plays a stronger role in early discovery. Armed with this insight, the company reallocates budget to emphasize high-intent search while refining social retargeting.
As a result, cost per acquisition drops and overall customer acquisition cost improves, without sacrificing volume.
Actionable Next Steps & Implementation Checklist
Use the checklist below to translate your acquisition funnel strategy into action, ensuring each stage is clearly defined, measurable, and built for iteration and scale.
1. Define Your Customer and Entry Points
Map your ideal customer profile or profiles and list the primary channels where those audiences first encounter your brand, such as paid search, social, referrals, or content.
2. Establish Clear Funnel Stages
Define the funnel stages that reflect your buying journey, for example Awareness, Interest, Consideration, and Conversion. Ensure each stage has a clear purpose and outcome.
3. Assign Tactics and Success Metrics
For each stage, select one or two core tactics or channels and define a primary success metric, such as CTR, form completion, demo requests, or purchases.
4. Set Up Tracking and Attribution
List the key events and micro-conversions for each stage and confirm that UTMs, unique identifiers, analytics tools, and CRM systems are properly connected.
5. Launch a Pilot Funnel
Start with a modest budget and drive traffic through the full funnel. Monitor performance by stage to identify early bottlenecks or drop-off points.
6. Build a Practical Dashboard
Create a dashboard that shows stage conversion rates, channel performance by funnel stage, drop-off analysis, and cost metrics like CPA.
7. Optimize and Scale
Focus on improving one bottleneck at a time, measuring impact before moving on. Once performance stabilizes, scale budget on the strongest tactics and revisit your attribution model to ensure accurate measurement of acquisition cost and value.
By clearly defining each funnel stage, instrumenting the right metrics, and iterating based on real attribution data, teams can turn customer acquisition from a collection of disconnected tactics into a scalable, measurable growth engine.
After a prospect shows initial interest, they rarely convert right away. Instead, they research, compare options, and look for proof that a solution will actually deliver.
This mid-funnel consideration stage in marketing is where many teams lose momentum, not because demand isn’t there, but because nurturing, measurement, and attribution are unclear or underdeveloped.
This guide breaks down how to approach mid-funnel strategy with intention. You’ll learn what the mid-funnel really represents, why it plays such a critical role in driving intent, and which tactics help prospects move forward without being rushed.
We’ll also cover how to track and attribute mid-funnel performance, common pitfalls to avoid, and practical steps your team can take to improve progression from consideration to action.
What the Mid-Funnel Stage Represents & Why It’s Critical
The mid-funnel (often referred to as MOFU) is the stage where prospects move beyond simple awareness and begin actively evaluating their options.
At this point, they know who you are and what you offer, but they are not yet ready to convert. Instead, they are comparing solutions, weighing trade-offs, validating claims, and looking for proof that your product or service is the right fit.
This is where interest becomes informed consideration, and where many buying decisions are quietly shaped.
Without thoughtfully nurturing leads mid-funnel, prospects often stall, disengage, or choose a competitor who does a better job answering their questions and earning trust.
Unlike top-of-funnel efforts, which focus on visibility, or bottom-of-funnel tactics, which push for action, the mid-funnel is defined by information-seeking and confidence-building.
Buyers want clarity, relevance, and reassurance, not pressure. Brands that rush this stage risk pushing prospects away before they are ready to act.
From an attribution and marketing operations perspective, the mid-funnel is also where complexity increases.
Progression typically involves multiple touchpoints across channels and over longer time frames, making it harder to connect activity directly to revenue.
When mid-funnel interactions are measured and attributed properly, teams can see where value is being created, invest more efficiently, and avoid underestimating one of the most critical phases of the buyer journey.
Key Strategies & Tactics to Nurture from Consideration -> Intent
Moving prospects through the mid-funnel requires a coordinated set of tactics that educate, build trust, and respond to real buying signals.
Content and Education
Assets like case studies, comparison guides, product deep dives, and ROI or cost calculators address common questions and objections while clearly articulating value.
The goal is to support informed decision-making, not to force a conversion, by providing clarity at the moments buyers are actively seeking it.
Retargeting and Behavioral Ads
Retargeting reinforces relevance by building on prior engagement. By serving ads based on behaviors such as site visits, content consumption, or product views, teams can tailor messaging to what prospects already care about.
This approach keeps your brand top of mind while advancing the conversation in a way that feels contextual rather than repetitive.
Social Proof and Trust Signals
Trust is a key barrier at the mid-funnel stage. Reviews, testimonials, customer stories, and user-generated content help reduce perceived risk by showing real-world success.
Social proof validates your claims and reassures prospects that others like them have achieved meaningful outcomes, which can accelerate movement toward intent.
Email and Nurture Sequences
Email nurture programs help orchestrate the mid-funnel journey over time. Effective sequences are segmented and behavior-driven, delivering content that aligns with a prospect’s interests and level of readiness.
Instead of pushing for a sale too early, strong nurture flows guide prospects with helpful information that builds confidence and keeps engagement high.
Segmentation and Personalization
Not all mid-funnel leads are equal. Prospects vary by source, use case, product interest, and engagement level, and messaging should reflect those differences.
Segmentation and personalization allow teams to tailor content, format, and timing so that each interaction feels relevant and useful, increasing the likelihood of progression.
Lead Scoring and Sales Handoff Alignment
As prospects move closer to intent, it’s critical to recognize and act on the right signals. Lead scoring helps identify behaviors that indicate readiness, such as repeated high-value content engagement or demo requests.
Clear alignment between marketing and sales on these criteria ensures timely, informed handoffs and prevents momentum from stalling at a critical point.
Tracking, Metrics, & Attribution Considerations
Measuring mid-funnel performance requires signals that reflect progression, engagement, and readiness.
Because this stage sits between awareness and conversion, the right measurement approach focuses on movement and influence rather than immediate revenue.
Focus on Mid-Funnel-Relevant Metrics
Mid-funnel success is best measured through engagement and progression metrics.
This includes signals like time spent on content, number of meaningful interactions, content downloads, and movement from one action to the next, such as from a guide download to a demo request.
Qualification metrics like MQL to SQL conversion rates are also more informative than top-of-funnel reach or bottom-funnel sales alone, as they indicate whether nurturing is effectively advancing prospects toward intent.
Attribution Nuance Across Channels and Time
Mid-funnel touchpoints often occur weeks or months before a conversion and span multiple channels, which makes attribution more complex.
Capturing these interactions accurately requires consistent use of UTMs, tracking content consumption, and accounting for view-through and assisted engagements from paid media.
Multi-touch or time-decay attribution models are typically better suited to reflect mid-funnel influence than last-touch models, which tend to undervalue earlier interactions.
Dashboarding and Reporting Best Practices
Dashboards should be designed to visualize progression, not just volume.
Useful views include the percentage of leads moving from interest to intent, segment-level performance by source or campaign, cost per qualified lead, average time to conversion, and engagement rates across nurture programs.
These views help teams understand where momentum is building and where prospects are getting stuck.
Addressing Measurement Challenges
Because mid-funnel activity does not always tie directly to immediate revenue, teams should rely on proxy metrics to demonstrate impact.
Indicators like reduced sales cycle length, improved close rates, and higher conversion from MQL to SQL help show the downstream value of nurturing.
Framing mid-funnel measurement in terms of efficiency and acceleration makes its contribution clearer to stakeholders and supports smarter investment decisions.
Common Pitfalls & What to Avoid
Even teams with strong top- and bottom-funnel programs often struggle in the mid-funnel due to misaligned expectations or incomplete measurement.
Avoiding the following pitfalls can help preserve momentum and ensure effective marketing tactics for intent:
Pushing for Conversion Too Early
One of the most common mistakes is treating the mid-funnel like a lighter version of the bottom funnel. Prospects at this stage are still evaluating and building confidence, and aggressive sales messaging can feel premature.
Underinvesting Due to Measurement Challenges
Because mid-funnel impact is less immediately visible than top-funnel reach or closed revenue, it is often underfunded. This can lead to gaps in content, inconsistent nurture programs, and stalled prospects.
Relying on One-Size-Fits-All Messaging
Ignoring segmentation and personalization is another common issue. Mid-funnel leads vary widely in intent, use case, and engagement level, and generic messaging rarely addresses their specific concerns.
Poor Marketing and Sales Alignment
When marketing and sales are not aligned on what qualifies a lead or what signals intent, prospects can fall through the cracks. Marketing may pass leads too early, or sales may lack the context needed to follow up effectively.
Incomplete Tracking and Attribution
Failing to capture mid-funnel interactions properly leads to undervaluing the role this stage plays in driving conversions. Without visibility into content engagement, assisted touchpoints, and nurture performance, teams may misallocate spend or deprioritize strategies that are quietly driving progress.
Use Case: How a SaaS Nurtured Prospects from Consideration to Intent
To see how mid-funnel strategies work together in practice, consider a B2B SaaS brand focused on shortening its sales cycle and improving lead quality.
A prospect first enters the funnel through a top-of-funnel blog article addressing a common industry challenge. After consuming that content, they are identified as a mid-funnel lead and presented with a comparison guide that outlines different solution approaches and clearly positions the brand’s strengths.
This asset is designed to support evaluation, not push a sale, and becomes the prospect’s first deeper engagement signal.
Following this interaction, the brand activates retargeting ads tailored to the comparison content the prospect viewed. These ads highlight a relevant customer case study, reinforcing credibility and addressing common objections for that use case.
Rather than introducing new messaging, the ads build on what the prospect has already shown interest in.
In parallel, the prospect enters a segmented email nurture sequence triggered by the comparison guide download. The emails provide additional context, including short product explainers and a deeper customer success story, before eventually introducing a demo invitation once engagement crosses a predefined threshold.
Lead scoring tracks behaviors such as content downloads, retargeting ad engagement, and email clicks to identify rising intent.
All interactions are tracked across channels, with attribution models capturing how content, ads, and email contributed over time. As a result, when the prospect requests a demo, the handoff to sales is timely and informed.
Through this coordinated mid-funnel approach, the brand reduced time to conversion by 25% and increased MQL to SQL conversion rates by 15%, demonstrating the measurable impact of thoughtful mid-funnel nurturing.
Actionable Next Steps for Your Team
These steps can help teams start improving consideration-to-intent progression in a structured, measurable way:
1. Audit Your Current Funnel
Begin by reviewing where prospects tend to stall after initial interest. Look for high drop-off points in the mid-funnel, such as after a content download or product page visit, and identify where engagement slows or stops. This audit provides a baseline for prioritizing improvements.
2. Map a Mid-Funnel Nurture Strategy for One Segment
Rather than trying to fix everything at once, choose a single audience segment to focus on and then put in place mid-funnel marketing tactics for lead nurture.
Define the triggers that move prospects into mid-funnel nurture, the content types they should receive, the retargeting logic that reinforces messaging, the email sequence that guides them, and the lead scoring criteria that indicate rising intent.
3. Strengthen Tracking and Attribution
Confirm that your tracking infrastructure captures key mid-funnel interactions, including content downloads, ad impressions and clicks, and email engagement.
Ensure these signals are tied to lead progression metrics so you can understand how nurturing activities influence movement toward intent.
4. Build a Mid-Funnel Performance Dashboard
Create a dashboard that surfaces the metrics that matter most at this stage. Common KPIs include content engagement rates, retargeting click-through rates, lead score progression, average time to intent, and cost per qualified lead.
These views make it easier to spot trends and optimize over time.
5. Launch a Pilot and Iterate
Run a pilot campaign for your selected segment over 60-90 days. Measure progression to intent against your baseline, identify what is working, and refine content, sequencing, or targeting as needed.
Small, controlled tests help teams learn quickly without overcommitting resources.
6. Align with Sales on Intent Signals
Finally, meet with your sales team to align on what truly signifies intent in your category. Agree on MQL to SQL criteria, required context for handoff, and follow-up expectations.
Strong alignment ensures that when prospects are ready to engage, momentum is maintained through a smooth, measurable transition.
Bringing the Mid-Funnel Into Focus
Treating the mid-funnel as a distinct, measurable phase of the overall marketing funnel allows teams to move beyond surface-level engagement and focus on what actually drives progression from consideration to intent.
When content, nurture, and retargeting are aligned to buyer needs, and when performance is tracked across touchpoints and time, mid-funnel programs become a powerful driver of efficiency and growth.
By implementing best practice consideration to intent strategies for marketers in 2025, and connecting mid-funnel activity to attribution and outcomes, marketing teams can reduce friction, shorten sales cycles, and deliver more qualified opportunities to sales.
Brand performance is one of the most important drivers of long-term growth, yet it is often the least clearly measured.
This guide breaks down how to measure brand performance in a structured, practical way, covering the key metrics for awareness, perception, and loyalty, how to track them consistently, and how to connect brand health to real business outcomes like retention, customer lifetime value, and acquisition efficiency.
What Brand Performance Is & Why It Matters
Brand performance describes how well a brand is known, trusted, liked, and ultimately preferred in the market.
Strong brand performance does not live only in sentiment surveys or social buzz. It translates into tangible business outcomes like higher conversion rates over time, stronger retention, increased customer lifetime value, and the ability to command premium pricing.
But for many marketing teams, brand is still treated as a “top-of-funnel” concept. Awareness campaigns run, impressions are counted, and success is often measured in reach alone.
But value compounds. A customer who recognizes and trusts a brand is more likely to choose it repeatedly, forgive small missteps, and recommend it to others.
Without a way to measure these effects, brand investment can feel hard to defend, especially when compared to channels with immediate, trackable conversions.
Measuring brand performance gives marketers a way to quantify long-term impact, justify brand spend, optimize creative and channel mix, and connect brand activity to downstream business results.
Brand performance can be measured across three connected dimensions:
Awareness, which measures how many people know or recognize your brand and whether it enters their consideration set.
Perception, which reflects how people feel about your brand, including trust, quality, and the associations they hold.
Loyalty and advocacy, which show whether those perceptions convert into repeat purchases, recommendations, and long-term relationships.
Together, these dimensions provide a structured way to track brand health beyond sales, as well as its role in driving sustainable growth.
Key Metrics & Methods for Each Dimension
Brand performance emerges from a set of complementary signals that, together, show how visible your brand is, how it is perceived, and whether those perceptions translate into real customer behavior.
Brand Awareness Metrics
Metrics:
Aided and unaided brand recall
Branded search volume
Share of voice
Social mentions
Reach and impressions
Direct traffic
Methods:
Brand-tracking surveys
Social listening tools
Search analytics
Web traffic analysis
Example indicators:
“What brands come to mind when you think of X?” (unaided recall)
“Which of the following brands have you heard of?” (aided recall)
What this reveals:
Whether your brand is entering the consideration set
How visible it is compared to competitors
How strong or consistent your presence is in the market over time
Perception & Brand Health Metrics
Metrics:
Brand sentiment (positive, neutral, negative)
Brand associations or attributes
Perceived quality
Brand trust
Net Promoter Score (NPS)
Methods:
Attitude-focused surveys
Social sentiment analysis
Review ratings
Brand attribute mapping
Example indicator:
NPS asks, “On a scale from 0 to 10, how likely are you to recommend our brand to a friend?”
What this reveals:
Whether consumer perceptions align with your intended brand position
Where gaps exist between how you want to be seen and how you are actually experienced
Loyalty, Retention & Advocacy Metrics
Metrics:
Repeat purchase rate
Retention rate
Customer lifetime value (CLV)
Share of wallet
Advocacy indicators such as referrals or brand ambassadors
Methods:
Behavioral data from CRM and transaction systems
Loyalty program performance
Surveys of promoters versus detractors
Referral source tracking.
What this reveals:
How your brand’s awareness and perception translate into tangible outcomes like sustained relationships, higher spend, and organic growth through recommendation.
Building a Brand Performance Tracking Framework
Measuring brand performance is most effective when it follows a clear, repeatable framework.
Step 1: Define Brand Goals & Competitive Position
Start by clarifying what success looks like for your brand.
This might mean increasing unaided awareness, becoming the most trusted option in your category, or improving brand loyalty measurement and metrics among a specific audience segment.
Clear goals ensure your measurement strategy is purposeful, not generic.
Step 2: Select Metrics Aligned to Those Goals
Choose metrics that directly reflect your objectives.
For example, if trust is a priority, brand trust scores, sentiment, and NPS should take precedence over reach or impressions.
Avoid tracking everything and focus on the signals that matter most.
Step 3: Establish Baselines & Benchmarks
Before making changes, understand where you are today. Run surveys, analyze historical data, and gather competitive or industry benchmarks where possible.
Baselines allow you to measure progress and avoid misinterpreting short-term fluctuations.
Step 4: Set Up Data Collection Systems
Ensure you have reliable processes in place to collect data consistently. This often includes brand-tracking surveys, social listening tools, web and search analytics, and CRM integrations.
Consistency is key for trend analysis.
Step 5: Connect Brand Metrics to Business Outcomes
Link brand metrics to outcomes like sales growth, retention improvements, margin lift, or changes in acquisition cost. This connection is what turns brand measurement into a decision-making tool.
Step 6: Dashboard, Review, and Iterate
Create dashboards that show awareness, brand perception tracking, and loyalty metrics alongside business KPIs. Review them on a regular cadence and use insights to adjust creative, spend, and channel mix over time.
Attribution & Time-Horizon Considerations
Brand performance requires a different approach to attribution than direct-response marketing. Both the timing of impact and the number of contributing touchpoints make brand effects harder to isolate, but not impossible to measure.
Here’s what to keep in mind:
Brand Impact Takes Longer to Materialize
Brand metrics tend to move on a longer time horizon than clicks or conversions.
Awareness lifts, perception shifts, and trust gains often appear weeks or months before they influence purchase behavior, retention, or lifetime value.
This lag means brand impact can be underestimated if it is evaluated using short reporting windows or last-touch logic.
Attribution Is Inherently Multi-Touch
Brand performance is shaped by many touchpoints working together, including paid media, PR, social content, organic exposure, word-of-mouth, and product experience.
Because influence is distributed, brand metrics cannot be attributed as precisely as direct-response channels. That said, they still merit structured attribution modeling focused on incrementality rather than perfect precision.
Use Hybrid Attribution Approaches
A practical solution is hybrid attribution. This approach links changes in brand metrics, such as improvements in recall, trust, or sentiment, to subsequent lifts in behavioral outcomes like conversion rate, repeat purchase, or retention.
Techniques such as uplift tests, geographic or audience hold-out groups, and pre- and post-campaign analysis help isolate the incremental impact of brand activity.
Measure Consistently to Prove Incremental Value
Ongoing measurement is critical. One-off studies can show correlation, but only consistent tracking reveals whether brand-driven changes create sustained value.
Regular brand measurement allows teams to separate true impact from short-term noise or external events and make more confident, data-informed decisions about brand investment over time.
Common Pitfalls With Brand Measurement
Even well-intentioned brand measurement efforts can fall short if they focus on the wrong signals or lack consistency.
The pitfalls below can undermine the value of brand performance tracking if left unaddressed:
Chasing Vanity Metrics
Metrics like follower counts, likes, or mentions can look impressive but often lack strategic meaning.
Without a clear connection to brand goals or business outcomes, vanity metrics offer little guidance for optimization or decision-making.
Ignoring Context and External Factors
Not every spike in branded search or direct traffic reflects improving brand health. Short-term campaigns, press coverage, or external events can temporarily inflate metrics. Without context and trend analysis, teams risk overestimating the impact of brand activity.
Assuming Awareness Equals Sales
Increases in awareness do not automatically lead to revenue. If perception is weak or trust is low, awareness may fail to convert. Sustainable growth requires alignment across awareness, perception, and loyalty, not progress in one dimension alone.
Inconsistent Measurement Over Time
Changing survey questions, methodologies, or data sources mid-stream makes it difficult to compare results over time. Consistency is essential for identifying real trends and understanding long-term brand impact.
Siloed Brand Data
When brand tracking lives outside the broader analytics and attribution stack, insights are rarely acted on. Integrating brand metrics with performance data ensures brand insights inform campaign planning, optimization, and budget allocation.
From Brand Lift to Business Impact: A Practical Example
A mid-sized consumer brand set out to better understand whether its brand investments were driving real business value.
Rather than tracking awareness alone, the team focused on three core metrics:
Unaided brand recall
Brand sentiment
Repeat purchase rate.
After launching a new emotional brand campaign, the brand tracked performance over six months. During that time:
Unaided recall increased from 22% to 30%
Overall brand sentiment improved by 12%
Repeat purchase rate rose by 8%
These changes were not viewed in isolation. The team linked improvements in recall and sentiment to downstream outcomes, including higher customer lifetime value and a lower cost of acquisition.
To measure brand awareness and loyalty perception, among other metrics, the brand combined quarterly online brand-tracking surveys, a social listening tool to measure sentiment, and CRM data to track repeat purchases. They then ran a cohort analysis comparing customers who recalled the brand to those who did not.
Customers with brand recall showed higher repeat purchase rates and stronger long-term value, helping establish a clearer link between brand perception and behavior.
These insights directly informed budgeting decisions. The brand increased investment in brand-led channels such as video and PR while keeping direct-response spend stable.
Ongoing monitoring of loyalty and retention metrics allowed the team to justify the shift and continue optimizing their brand strategy based on measurable impact.
Turning Brand Measurement into Action
Brand performance KPIs for marketers in 2025 only create value when insights are actively used. The steps below help teams move from tracking metrics to making better strategic and budget decisions.
Conduct a Brand Performance Audit
Start by listing all the brand-related metrics you currently track across awareness, perception, and loyalty. Identify where data is missing, inconsistent, or disconnected from business outcomes.
This audit clarifies what you truly understand about your brand today.
Define a Small Set of Core Brand KPIs
Select three to five brand KPIs that align directly with your strategic goal. For example, this might include unaided brand recall, Net Promoter Score, and repeat purchase rate.
Set clear targets for the next twelve months so progress can be measured consistently.
Build or Refine Your Brand Dashboard
Create a dashboard that brings brand metrics and business KPIs together. Include baselines, targets, and trends over time, as well as segmentation by audience cohort or geography where relevant.
Most importantly, show how brand movement connects to outcomes like retention, conversion, or acquisition cost.
Establish a Clear Tracking Cadence
Consistency matters more than frequency. Set a regular schedule, such as quarterly brand surveys, weekly social listening, and monthly repeat purchase analysis.
A defined cadence makes it easier to spot trends and avoid overreacting to short-term noise.
Test, Learn, and Review Regularly
Use test and hold-out approaches for brand campaigns by exposing one audience to brand activity and comparing results against a control group.
Review brand metrics alongside performance and attribution KPIs in your regular strategy meetings so brand insights directly inform campaign planning and investment decisions.
Measuring What Makes Brands Last
Brand performance is about understanding how awareness, perception, and loyalty work together to create durable business value over time.
When brand metrics are defined clearly, measured consistently, and connected to real outcomes like retention, lifetime value, and acquisition efficiency, they become powerful decision-making tools.
With effective brand measurement, teams can justify brand investment, optimize creative and channel mix, and move beyond short-term thinking toward sustainable growth.
The tension of fitting in enough to be trusted, while standing out enough to be chosen is at the heart of building a strong brand position in any market.
This guide breaks down the two pillars of modern positioning: Points of Parity (POPs), the essential attributes your brand must share with competitors, and Points of Difference (PODs), the meaningful advantages that set you apart.
You’ll learn what they are, why they matter, how to balance them, and how to convert the framework into a clear, defensible strategy.
Whether you’re refining a mature brand or defining a new one, this article will help you find your competitive sweet spot: the space where credibility meets differentiation and customers feel confident choosing you.
What are Points of Parity (POPs) and Points of Difference (PODs)
Here’s how to define points of parity and points of difference for your brand:
Points of Parity (POPs) & Why They Matter
Points of Parity, or POPs, are the baseline attributes your brand must share with competitors to even qualify for consideration in a category.
These are the features customers assume you offer. POPs don’t make you unique, and they’re not meant to. They simply signal that your brand belongs in the competitive set and can deliver the core value the category promises.
POPs establish credibility. A brand that fails to meet category expectations doesn’t just underperform, it gets dismissed entirely. If you don’t meet the non-negotiables, your unique strengths never get a chance to matter.
Most categories have clear POPs:
For streaming services, the minimum expectations might include a wide selection of content, multi-device support, and an ad-free premium plan.
In banking, POPs might include mobile access, security standards, and customer support.
In fitness apps, users expect progress tracking, personalized workouts, and basic integration with wearables.
This baseline changes over time as standards evolve, but the idea remains: POPs are the price of entry. Without them, even strong differentiators can’t keep you from being filtered out early in the decision process.
Now let’s discuss brand parity vs differentiation:
Points of Difference (PODs) & Why They Matter
Points of Difference, or PODs, are the distinctive attributes, benefits, or associations that give customers a clear reason to choose your brand over others.
Where POPs establish that you belong in the category, PODs define why you deserve preference. They’re the elements that make your brand memorable, compelling, and worth talking about.
Effective PODs create meaningful differentiation that customers value. When they’re strong, relevant, and credible, PODs can drive higher conversion rates, support premium pricing, and deepen long-term loyalty.
Examples show how PODs work in practice:
A smartphone might position a revolutionary camera system or breakthrough battery life as its signature difference.
A budget airline might win by leaning into ultra-low pricing and radical simplicity, offering a stripped-back experience that aligns with what its specific customers care about most.
In both cases, the POD gives buyers a concrete reason to prefer one brand over another.
But if your brand doesn’t meet basic category expectations (POPs), even the strongest differentiator won’t save you. Differentiation only drives value when the foundation is already in place.
Finding the Sweet Spot Between POP and POD in Brand Strategy
Focus too much on POPs, and you become interchangeable, competing on price or convenience instead of value.
Focus too much on PODs without meeting the basics, and you risk confusing customers or falling outside the category frame entirely.
The goal is to find the balance: credible enough to belong, distinct enough to win.
Here’s how to find your brand’s competitive sweet spot when it comes to points of parity vs difference:
1. Audit Your Category’s Required POPs
Start by clarifying the non-negotiables in your category. What does every credible brand have to offer?
These expectations may come from customer research, competitor analysis, or emerging industry standards. Until your POPs are consistently met, differentiation won’t land.
2. Map Your Brand’s Existing PODs
Next, identify what genuinely sets your brand apart. Look for strengths that are not only unique, but also desirable and believable. A POD must be rooted in something real (product quality, a signature experience, a proprietary capability) or customers won’t adopt it as meaningful.
3. Check Overlap, Clarity, and Defensibility
Some PODs can unintentionally conflict with POP expectations or become too easy for competitors to copy.
Validate that your differentiators reinforce, rather than undermine, the baseline attributes customers expect. And pressure-test whether your PODs can hold up over time.
4. Communicate in the Right Order
Messaging should reassure first and differentiate second. Show that you meet category standards, then layer on the unique value only you provide. This sequencing makes your PODs more persuasive.
5. Monitor and Evolve
POPs shift as yesterday’s innovations become today’s baseline, and PODs erode as competitors imitate. Revisit your brand positioning regularly to stay ahead of those shifts and protect your advantage.
Applying This to Brand Strategy: Steps for Marketers
Turning POPs and PODs into a usable positioning strategy requires moving from analysis to execution.
These steps help teams translate insights into a narrative the market can understand and act on:
Start With a Category Reality Check
Before defining your own position, ground yourself in the category’s truth. Review competitors, study their claims, and catalog the expectations customers already hold.
This gives you a working map of the “must-haves:” the POPs your brand cannot afford to miss.
Listen for What Actually Matters
Next, gather the customer perspective. Look at reviews, interview users, and analyze behavioral data to learn which attributes drive trust, trial, and repeat use. This helps you separate superficial differences from the things people genuinely value.
Name Your Advantage
Once you understand both the market and the customer, articulate the strengths your brand can uniquely and credibly claim.
Focus on differentiators you can back up consistently; not aspirational qualities, but real advantages that can serve as durable PODs.
Build the Positioning Narrative
This is where strategy becomes story. Craft messaging that first establishes credibility through your POPs, then sharpens into your differentiators. This should cascade across website copy, product pages, sales materials, and campaigns.
Create a System for Ongoing Refinement
Build a quarterly or biannual checkpoint to reassess what has become baseline, what still differentiates you, and where new opportunities are emerging.
Common Pitfalls & Strategic Traps
Even well-positioned brands can lose their footing if they mismanage the balance between POPs and PODs.
These are the mistakes that most often undermine otherwise solid strategies:
Ignoring POPs and Losing Credibility
Some brands focus so heavily on innovation that they overlook the basics. When you fail to meet core category standards, customers won’t consider you, even if your differentiators are strong.
Claiming PODs That Aren’t Credible or Relevant
A POD only works if customers believe it and value it. Differentiators that are exaggerated, niche, or internally beloved but externally meaningless won’t create preference.
Over-Differentiating and Drifting Out of the Category
Brands sometimes push uniqueness so far that customers can’t tell what category they’re in. When you fall outside the expected frame of reference, the market doesn’t see you as innovative, it sees you as confusing.
Not Anticipating Shifts in Category Expectations
What starts as a compelling POD often becomes a baseline expectation over time. If you don’t update your positioning when competitors catch up, your once-unique advantage turns into table stakes.
Muddled Messaging That Blends POPs and PODs Poorly
Trying to communicate everything at once is a quick route to brand confusion. When POPs and PODs aren’t clearly separated, or are presented in the wrong order, your message can sound contradictory.
How POPs and PODs Play Out in the Real World
To see how POPs and PODs work in practice, it helps to look at two very different categories; one saturated with global competition, the other hyper-local.
Brand A: Standing Out in the Smartphone Market
Brand A starts by meeting every baseline expectation in its category.
It delivers the standard hardware package customers assume: a responsive touchscreen, reliable internet access, and a competitive camera. These POPs ensure the brand feels credible next to better-known competitors.
From there, Brand A defines its POD: an industry-leading battery life paired with an AI-driven camera system.
These differentiators are both desirable and believable, grounded in real product performance. In its messaging, the brand first reassures customers it checks every essential box, then highlights the advantages that make it a smarter choice for power users and mobile photographers.
Brand B: Differentiating a Local Coffee Shop
A neighborhood coffee shop begins with familiar POPs: quality coffee, seating, and free WiFi. These basics signal that it provides the experience customers expect from any modern café.
Its PODs come from what makes it uniquely local: house-roasted beans, community-driven events, and sustainability certifications.
These differentiators resonate with customers who value craft, connection, and ethical sourcing. The shop’s communication emphasizes warmth, transparency, and neighborhood identity, creating distinction in a crowded market of similar offerings.
Both brands succeed by auditing their POPs, defining credible PODs, and communicating in a way that reinforces category fit while showcasing what makes them stand out.
Actionable Next Steps for Your Team: POPs and PODs
Here are practical steps your team can take to put POPs and PODs into action:
Map Your Top 5 POPs
List the key category expectations every credible competitor meets. Then, mark where your brand stands: met, partially met, or not met. This reveals any foundational gaps that could undermine your positioning.
Pinpoint 1-2 Strong POD Candidates
Identify the unique benefits or features your brand can credibly claim. Focus on differentiators that align with customer insight and are desirable, believable, and hard to replicate.
Write a Clear Positioning Statement
Craft or refine a positioning statement that explicitly pairs your POP baseline with your POD value proposition. This helps unify internal teams and anchors future messaging.
Refresh Your Messaging and Assets
Audit your website, sales collateral, ad campaigns, and social presence. Ensure each channel first establishes category credibility, then reinforces your differentiators with clarity and consistency.
Review and Adjust Quarterly
Revisit your POPs and PODs regularly. Ask whether any existing PODs have become category norms, and whether emerging strengths could replace them. This cadence keeps your advantage fresh and defensible.
Bringing Your Positioning Into Focus
Effective brand positioning is about understanding how POPs and PODs work together to create a foundation your audience trusts, and a reason they prefer you.
When you meet category expectations and layer on meaningful differentiation, you give customers both confidence and clarity: the two ingredients that drive real choice.
As your market evolves, so will the line between what’s expected and what’s unique. Revisit your POPs, refine your PODs, and keep your messaging disciplined and intentional.
The sweet spot is always shifting, but with a structured approach, your brand can stay grounded, distinctive, and unmistakably itself.
Performance marketing has become the backbone of modern growth strategies. Channels like PPC, paid social, affiliates, and email give marketers the ability to reach the right audiences and pay only when a measurable action occurs.
But with rising costs, shifting privacy rules, and more platforms than ever, it’s harder to know which channels to prioritize and how to evaluate what’s actually working.
This guide breaks down the best performance marketing channels for ROI, how to select PPC vs affiliate vs other channels, and the metrics you should use to measure them. You’ll learn how to choose the right mix for your goals, avoid common pitfalls, and build an attribution framework that gives you confidence in where your budget goes.
By the end, you’ll have a practical, end-to-end playbook for building a performance channel strategy that scales.
What Is Performance Marketing?
Performance marketing is a digital advertising approach where you pay only when a specific, measurable action occurs.
Instead of buying broad exposure or impressions, you tie spend directly to outcomes like clicks, lead submissions, or completed purchases.
The model is built on trackable events, clear KPIs, and rapid feedback loops, which makes it easier to understand what’s working and adjust quickly.
This is different from traditional brand marketing, which focuses on long-term awareness and sentiment. Brand campaigns can influence performance, but their results are often indirect or slower to quantify.
Performance marketing, by contrast, demands tighter measurement, attribution clarity, and accountability. Every dollar spent should map back to a defined action.
That distinction matters more than ever: rising media costs, stricter privacy rules, and shrinking budgets mean teams can’t afford guesswork.
Marketers are under pressure to prove ROI, understand which channels drive incremental conversions, and shift spend efficiently.
Performance marketing gives teams a way to do that by connecting tactics to measurable results, enabling smarter optimization and faster testing across channels.
Overview of Major Performance Channels
Performance marketing spans several channel types, each with a distinct role in the funnel and its own measurement considerations.
Here is a practical breakdown of the core channels teams rely on when building a scalable mix.
PPC / Search Engine Marketing (SEM)
PPC advertising campaigns on platforms like Google Ads and Bing capture demand from users who are actively searching for solutions.
Strengths:
High-intent traffic
Clear, fast feedback loops
Strong alignment with lower-funnel conversions
Considerations:
Rising CPCs and keyword competition
Overlap with other channels in last-click models
Requires careful query-level optimization to maintain ROI
Affiliate Marketing
Affiliate partners promote your product and earn commission only when they drive a conversion, typically via CPA or CPS models.
Strengths:
Low-risk, pay-for-outcome structure
Access to external audiences you may not reach directly
Useful for scaling incremental revenue
Considerations:
Limited control over creative and messaging
Essential to maintain clean tracking (UTMs, partner pixels, attribution windows)
Potential attribution disputes if PPC or social also touch the user
Social Media/Paid Social Advertising
Platforms like Meta, TikTok, LinkedIn, and Instagram offer targeted campaigns based on interests, demographics, and behaviors.
Strengths:
Wide and scalable reach
Flexible formats for awareness, retargeting, and conversion
Effective for creative storytelling and testing new audiences
Considerations:
Harder to measure true incremental conversions
Creative fatigue can rise quickly, increasing CAC
Attribution windows vary across platforms and can skew reporting
Email / Owned Channel Performance
Email becomes a performance channel when tied directly to measurable outcomes like sign-ups or purchases.
Strengths:
High ROI and low marginal cost
Ideal for nurturing, retention, and repeat conversions
List hygiene and audience quality heavily impact performance
Must connect email conversions to broader attribution models
Can mask other channels’ contributions if not deduped properly
Emerging & Hybrid Channels
This includes native advertising, programmatic display, connected TV (CTV), digital out-of-home, and expanded affiliate marketing channels.
Strengths:
Diversifies reach beyond standard digital channels
Opens access to upper- and mid-funnel audiences
Potential for strong incremental lift when combined with PPC or paid social
Considerations:
Measurement can be complex, especially for non-clickable formats
Requires multi-touch or modeled attribution for accurate assessment
Higher production needs for video and display creative
Choosing & Prioritizing Channels for Your Business
Selecting the right performance channels starts with understanding where each one fits in your customer journey and how reliably you can measure its impact.
A clear selection framework helps teams avoid scattered spending and build a channel mix that’s both scalable and efficient.
Start with Core Selection Criteria
Evaluate channels based on audience fit, funnel role, cost structure, and how easily you can track conversions.
If a channel reaches your customers but can’t be measured accurately, it becomes difficult to optimize or justify spend.
Likewise, channels with low cost but weak audience fit often fail to drive meaningful outcomes.
Map Channels to Funnel Stages
Each channel has a natural strength:
Paid social is strong for upper-funnel discovery and prospecting.
PPC is ideal for high-intent, bottom-funnel conversions.
Email and affiliate channels shine in nurturing, retargeting, and retention.
Mapping prevents teams from expecting one channel to carry the full funnel, which usually leads to inflated CPAs and misaligned optimization.
Test and Learn
Pilot one channel at a time, define a clear measurement window, and track performance through metrics like CPA, ROAS, and LTV.
Testing helps you understand not only raw efficiency but also incremental value: whether a channel drives net-new conversions or merely intercepts existing demand.
Avoid Over-Diversification
Running too many channels at once spreads budgets thin and limits your ability to generate statistically meaningful insights.
Most strong performance mixes rely on a small set of proven channels, supplemented with controlled tests.
Build a Portfolio Mindset
Think of your channel mix for performance marketing as a balance of:
Proven channels that consistently drive conversions
Experimental channels for structured testing
Seasonal or opportunistic channels that support short-term goals
This approach keeps your mix both stable and adaptable as market conditions shift.
Attribution & Measurement Across Channels
Accurate attribution is the backbone of performance marketing. Without reliable tracking and crediting, it becomes nearly impossible to understand which channels are actually driving results.
Build a Strong Tracking Foundation
Invest in consistent UTM tagging, properly implemented pixels, event tracking for key conversions, and identifier stitching to connect user journeys across devices and channels. Clean data upfront prevents major reporting issues later.
Choose the Right Attribution Model
Last-click attribution is simple and commonly used, but it often overweights lower-funnel channels like PPC and email.
Multi-touch and data-driven models distribute credit across the journey, offering a more balanced view of how channels work together.
For deeper accuracy, hold-out tests can reveal true incremental lift by comparing exposed vs. unexposed audiences.
Track Core Metrics by Channel
CPC, CPA, CPL, CPS, ROAS, and conversion rate provide a baseline understanding of efficiency. But metrics should be compared within context: a channel with a higher CPA may still deliver stronger LTV or incremental value than one with a lower CPA but limited scale.
Watch for Overlap and Cannibalization
When multiple channels touch the same user, misattribution becomes common. Paid social may introduce a prospect, but PPC may capture the conversion, and last-click reporting will obscure the social contribution.
Identifying these interactions helps teams avoid over-investing in channels that intercept demand rather than generating it.
Use Dashboards to Evaluate Performance
Centralized reporting enables teams to compare scalability, efficiency, and incremental lift across channels. This makes it easier to spot trends, shift budget confidently, and optimize toward the channels that drive the most meaningful outcomes.
Common Pitfalls & Challenges
Even strong performance strategies can go off track if teams overlook issues. These are the challenges that most often undermine channel efficiency and distort measurement.
Over-Relying on a Single Channel
Depending too heavily on one performance channel creates vulnerability. If costs rise or platform policies change, your results can weaken overnight. A balanced mix protects against volatility.
Poor Attribution Clarity
Mis-tagged campaigns, inconsistent UTMs, or disconnected data sources lead to mis-credited conversions and flawed reporting. Without attribution clarity, optimization becomes guesswork.
Misaligned Funnel Expectations
Channels don’t all perform the same role. Using upper-funnel tactics to drive direct conversions usually inflates CPA, while expecting PPC to solve awareness gaps limits reach. Matching channel to funnel stage is essential.
Underestimating Creative Requirements
Social, native, and emerging channels demand ongoing creative refreshes. Without a steady pipeline of assets, performance drops quickly due to fatigue and declining engagement.
Ignoring Cost Escalation & Fatigue
Auction-based channels become more expensive over time, and creative wear-out raises acquisition costs. Teams need to monitor trends, rotate assets, and revisit budgets before efficiency erodes.
Case Study: How a Balanced Channel Mix Improved ROI
A mid-sized ecommerce brand wanted to scale revenue but felt constrained by rising acquisition costs.
Their marketing team relied heavily on PPC for bottom-funnel conversions and used paid social only for remarketing. Affiliates were active but underfunded and rarely analyzed for incremental value.
To clarify performance, the team evaluated each channel’s CPA, ROAS, and attribution path.
PPC continued to deliver the highest conversion volume, but attribution modeling uncovered an important insight: affiliate partners were responsible for low-volume, low-cost conversions that disproportionately lifted overall efficiency.
When users first discovered the brand through paid social, clicked an affiliate link later, and finally converted through PPC, last-click reporting gave PPC all the credit, masking affiliate impact.
After reviewing multi-touch and assisted-conversion data, the team rebalanced its mix.
They increased affiliate investment, refreshed creative for paid social prospecting, and tightened keyword matching in PPC to protect efficiency. They also tested a small programmatic campaign to expand reach and measured its contribution through hold-out tests.
Within one quarter, blended CPA dropped, incremental conversions increased, and the team gained a clearer view of how each channel contributed to growth.
Next Steps & Implementation Plan
A strong performance marketing strategy comes from consistent evaluation and structured iteration. These steps help teams move from theory to a measurable, scalable channel mix.
1. Audit Your Current Channel Mix
List every performance channel in use and document spend, CPA, ROAS, and how clearly each channel is attributed. Identify gaps in tracking or areas where multiple channels may be competing for the same conversions.
2. Prioritize Two Channels to Optimize, and One to Test
Choose channels with clear potential for improved efficiency or scale, then select one new or emerging channel to pilot. Define success metrics and a timeline before launching any tests.
3. Strengthen Your Attribution Framework
Map each channel to its funnel role, the metrics that matter most at that stage, and the events required for accurate tracking. Ensure UTMs, pixels, and identifiers are consistent across platforms.
4. Set up Centralized Dashboards
Use dashboards to track channel performance weekly or bi-weekly. This makes it easier to catch trends early and shift spend before inefficiencies grow.
5. Establish an ongoing optimization cadence.
Regularly review creative, audience targeting, budgets, and channel-level performance. Scale the channels showing strong incremental return and refine or pause those that consistently underperform.
Bringing Your Channel Mix Together
Building an effective performance marketing strategy is about choosing the channels that align with your audience, mapping them to the right funnel stages, and measuring their impact with clarity.
With strong tracking, thoughtful attribution, and a disciplined test-and-learn approach, teams can uncover which channels truly drive incremental value and scale them with confidence.
Marketers are feeling the impact of a changing privacy landscape. Browser restrictions, tracking prevention, and stricter consent rules have made it harder for the Meta Pixel to capture the full picture of what happens after someone clicks an ad.
As a result, attribution becomes blurry, optimization signals get weaker, and teams waste spend on conversions that never make it back into their reporting.
The Meta Conversions API (CAPI) helps solve that problem. Instead of relying on the browser, CAPI sends events directly from your server to Meta. This gives you a more complete, consistent view of your funnel, even when users block cookies or use privacy focused tools.
When Meta receives higher quality data, it can optimize more efficiently, build stronger audiences, and return more accurate performance insights.
In this guide, we’ll walk through:
What the Meta Conversions API is and why it matters
How CAPI differs from pixel based tracking
How to implement Meta Conversions API
The impact on attribution, data quality, and privacy
Actionable next steps for your team
Let’s dive in.
What the Meta Conversions API Is & Why It Matters
The Meta Conversions API (CAPI) is a server-to-server method of sending conversion events directly from your backend to Meta’s advertising platform.
Instead of relying solely on the browser-based Meta Pixel to capture actions like purchases, leads, or add to carts, CAPI transmits those events from your server environment, giving Meta a more stable and complete view of what actually happened after someone clicked or viewed an ad.
This shift matters because the traditional pixel model is under increasing pressure.
Third party cookies are being deprecated, browsers are tightening default privacy settings, regulations like GDPR and CCPA require stricter consent handling, and ad blockers routinely prevent client side scripts from firing.
CAPI helps close those gaps: when your server sends verified events with hashed identifiers, Meta can match more conversions back to ad interactions, improve attribution accuracy, and strengthen delivery optimization.
Higher event match quality leads to better targeting, clearer measurement of your funnel, and more efficient use of paid media budgets.
How the Meta Conversions API Differs from Client-Side (Pixel) Tracking
The Meta Pixel is a browser-based script that fires when a user takes an action on your site. It is simple to install and still valuable, but it depends on the user’s device and browser environment.
If a customer blocks cookies, clears their cache, uses a privacy focused browser, or runs an ad blocker, the pixel may never fire.
This means missing or incomplete data, especially for lower funnel events where accuracy matters most.
The Meta Conversions API works differently. Instead of relying on the browser, CAPI sends events from your server to Meta.
Server-side events are not affected by ad blockers, cookie deletion, or tracking prevention features, so they tend to be more reliable and consistent. They also allow you to include richer, privacy safe data like hashed email or phone numbers, which improves event match quality.
Meta recommends running both the Pixel and CAPI together. A dual setup provides maximum coverage, and Meta can deduplicate events when both versions fire, ensuring clean reporting without double counting.
By pairing both methods you gain more dependable conversion tracking, stronger match rates, the ability to capture offline or cross device events, and a more complete attribution picture across your funnel.
Implementation Steps & Technical Considerations
Getting the Meta Conversions API up and running requires a bit more setup than installing a pixel, but the payoff is higher quality data and stronger attribution.
The steps below outline the core pieces teams should plan for before integrating CAPI into their marketing and analytics stack:
1. Complete Pre-Requisites
Before you begin, confirm that your tagging and analytics setup is in good shape. You will need access to Meta Events Manager, a server environment or a supported partner platform, and clarity on which conversion events matter most for your funnel.
Many teams also loop in engineering early to support authentication, data formatting, and endpoint configuration.
2. Define Events and Parameters
CAPI requires specific fields to be sent with each event. At minimum, you will define the event_name, event_time, user_data such as hashed email or phone number, and custom_data like value or currency for revenue based events.
The richer and cleaner these parameters are, the higher your event match quality will be.
3. Establish Integration Methods
You can send events directly through the API, use a partner integration, or set up server-side tracking for Meta ad attribution.
The best option depends on your internal resources and how much control you want over data handling.
4. Institute Deduplication Logic
If you run both Pixel and CAPI, you must assign an event_id to each event so Meta can match and deduplicate them. This prevents double counting while still giving you full coverage.
5. Measure Success
After implementation, monitor Event Match Quality (EMQ), compare pixel versus server event volumes, and verify that values and timestamps are consistent.
Strong EMQ and stable event counts indicate a healthy CAPI setup.
Attribution, Data Quality, & Privacy Implications
Implementing the Meta Conversions API has meaningful effects on how your team measures performance, manages data, and maintains compliance in a privacy first environment.
CAPI strengthens attribution, improves the quality of optimization signals, and gives you greater control over how user information is handled. At the same time, it introduces technical considerations that teams should plan for before rollout.
How CAPI Improves Attribution
Server side events are more reliable than browser based pixel events, which are easily disrupted by tracking prevention tools.
When Meta receives more complete conversion data, it can match a higher percentage of events back to ad interactions. This leads to clearer attribution paths, stronger audience building, and more efficient budget allocation across your campaigns.
Data Governance and Privacy
Because CAPI sends events from your server, you control which information is collected, how it is hashed, and when it is transmitted.
This makes it easier to enforce consent requirements and limit personal data exposure, helping you stay aligned with frameworks like GDPR and CCPA.
Key Trade Offs
CAPI increases accuracy, but it does not eliminate the need for client-side tracking. Some signals still require the pixel, and the combined setup introduces technical overhead.
Teams should expect ongoing maintenance, monitoring, and coordination across marketing and engineering to keep the system healthy.
Actionable Next Steps for Marketing Teams
Implementing the Meta CAPI does not need to be done all at once. Most teams see the best results by approaching CAPI in structured phases, starting with an audit and moving toward measurement and ongoing optimization.
The steps below offer a practical roadmap any marketing, analytics, or growth team can use.
Audit Your Current Tracking
Begin by identifying where pixel-only tracking is falling short.
Look for gaps caused by ad blockers, discrepancies between platform conversions, or offline events that never reach Meta.
This baseline will help you understand the size of the opportunity and prioritize which events matter most.
Evaluate Your Infrastructure
Decide how you plan to approach conversion API implementation. Options include direct API integration, partner platforms, or a server side tagging setup through Google Tag Manager.
Run a Pilot
Start small. Choose one high-value event such as Purchase or Lead, implement CAPI alongside your pixel, and monitor performance. Early pilots help validate your setup before scaling.
Define Your Measurement Plan
Track Event Match Quality (EMQ), matched conversion counts, ROAS changes, and reductions in untracked conversions. These metrics show whether CAPI is improving optimization signals and attribution accuracy.
Document Governance and Consent
Ensure your consent policies are enforced consistently. Hash identifiers properly, secure data flows, and keep clear documentation of what information is being sent to Meta.
Schedule Ongoing Reviews
Plan monthly or quarterly check-ins to review data health, deduplication accuracy, and attribution impact. CAPI is most effective when it is maintained as an active part of your analytics and optimization workflow.
The Path to Reliable, Privacy-First Attribution
As privacy standards evolve, the days of relying solely on pixel-based tracking are behind us.
The Meta CAPI enables marketers to maintain privacy-first tracking and accurate measurements while giving Meta the high-quality signals it needs to optimize campaigns.
When paired with the pixel, CAPI fills the gaps left by browser limitations, improves attribution clarity, and strengthens the link between ad spend and real business outcomes.
But you don’t need to overhaul your stack overnight.
Start with an audit, run a focused pilot, and build a clear measurement framework. From there, refine your governance and revisit performance regularly.
With the right setup, CAPI becomes a foundation for reliable data, smarter targeting, and more efficient growth in a privacy-first world.
Gamification has moved far beyond loyalty apps and mobile games.
Today, paid media teams are deliberately placing game mechanics in ads to capture attention, boost engagement, and generate richer data.
Instead of asking users to watch an ad, gamified formats invite them to participate in it, and every tap, swipe, or completion event becomes a measurable signal.
In this article, we break down what paid advertising gamification is and how it strengthens attribution by creating more behavioral touchpoints.
You’ll learn which game mechanics matter most, how to map them to attribution metrics, and how to design a gamified campaign that is both fun for users and cleanly trackable for analysts.
We’ll also explore key trade-offs such as tracking complexity, novelty fade, spill-over effects, and the difference between interaction and true incrementality. Finally, we wrap with actionable steps your paid media and attribution teams can use to launch a pilot, measure lift, and scale gamification responsibly.
If you’re looking for a clearer way to understand which ad interactions drive real value, gamified formats offer a promising path.
Why Gamification Works in Paid Ads & Attribution
Ad engagement gamification in advertising means adding game elements like points, badges, challenges, or leaderboards directly into an ad to make it more engaging, rewarding, and interactive.
Instead of watching or scrolling past an ad, users get something to do. That small shift turns a passive impression into an active, measurable interaction.
These mechanics work because they naturally increase ad engagement. A playable ad or short interactive challenge keeps users on the creative longer, which improves dwell time, boosts click-through rates, and often leads to higher completion rates.
Some studies, such as the Oppizi benchmark, show engagement lifts of up to roughly 48% for gamified formats.
When users stay longer and do more inside the ad, platforms receive stronger behavioral signals that help the system optimize delivery.
Gamification also enriches event data. Every tap, swipe, choice, or level completion becomes a micro-interaction you can track.
Instead of attributing value to a single click, paid media teams gain a full sequence of user actions inside the ad.
These additional touchpoints clarify how users move from exposure to exploration to conversion, which improves the accuracy of multi-touch attribution models.
In short, gamified ads don’t just make creatives more fun. They generate more data, better signals, and clearer behavioral pathways.
This gives attribution systems more to work with, helping marketers understand which interactions actually drive value and where to invest next.
Core Game Mechanics for Ad Campaigns & How They Map to Attribution Metrics
Gamified ads use familiar game mechanics to turn standard creatives into interactive experiences.
Each mechanic creates its own behavioral signals, which can be tracked as micro-conversions and linked directly to downstream outcomes. The more intentional the mechanic, the stronger the attribution clarity.
Here’s how to use gamification in paid advertising campaigns:
Playable Ads, Interactive Quizzes, and Mini Games
Playable formats give users something to do rather than something to watch. When someone completes a quiz or finishes a 30-second mini-game, they generate high-quality events such as completed game, earned badge, or claimed reward.
These signals usually correlate with stronger intent, which can make your attribution model more confident when assigning value to later clicks or conversions. They also boost KPIs like interaction count, completion rate, and time spent in the ad.
Points and Rewards Systems
Points create a lightweight incentive loop. Users earn points for behaviors like viewing the ad, completing an action, or clicking through.
Because each point-earning action is trackable, these become micro-conversions that map neatly into multi-touch attribution models.
Reward mechanics can also highlight which steps of the funnel are most motivating, improving attribution across metrics like completion depth, click-through rate, and eventual purchase rate.
Badges, Levels, and Status Icons
These mechanics recognize milestones, such as a user’s first interaction, first purchase, or fifth referral.
Badges and levels produce identifiable behavioral segments that matter for attribution. For example, “badge earners” may show a meaningfully higher likelihood to convert or return later.
Tracking these milestones allows paid media teams to understand repeat value and tie long-term outcomes back to earlier gamified interactions.
Leaderboards and Social Competition
Leaderboards encourage users to share, invite friends, or replay the ad to improve their score. This introduces referral and social-spillover touchpoints that your attribution stack must capture.
When tracked well, these mechanics reveal incremental lift from virality, highlight paid versus organic spill-over, and expose cross-channel influence that standard ads would miss.
Why These Mechanics Strengthen Attribution
Each mechanic generates additional, high-signal behavioral data. Instead of a single click, your attribution model receives a chain of interactions: time in ad, completion status, share events, referral visits, and repeat plays.
These signals reduce noise, improve precision, and help you understand not just who converted, but why.
In many cases, this leads to clearer attribution paths and more confident investment decisions compared to non-gamified formats.
Designing a Gamified Paid Media Campaign With Attribution in Mind
A successful gamified campaign starts with clarity. Before building the interactive experience, define both your paid media objective and the attribution question you want to answer.
1. Set Your Goals
Identify the primary outcome you want to drive, such as conversions or qualified traffic.
Specify how you will measure success, for example incremental lift compared to a non-gamified baseline.
2. Choose Mechanics That Fit the Channel
Display / RTB: short playable ads or tap-based mini-games.
Social: quizzes, challenges, or reward-based formats.
Video: choose-your-path or interactive narrative elements.
Select the mechanic that matches user behavior and attention patterns for that platform.
3. Instrument Your Tracking
Add event triggers inside the ad, such as badge earned, level complete, or reward unlocked.
Ensure all events flow into your attribution system with clean identifiers.
Set up ad-touch tracking so micro-events connect to downstream funnel activity.
Use these metrics to understand how users progress through the experience.
5. Feed Data Into Your Attribution Model
Map micro-events to later actions, such as micro-interaction → click → conversion.
Compare performance to your baseline to measure true contribution.
6. Optimize for Both Engagement and Accuracy
Test reward types, difficulty levels, and incentives.
Confirm that attribution logic handles additional touchpoints, including referrals or repeat plays, without double-counting.
This structure keeps the campaign creative while ensuring every interaction is measurable and attribution-ready.
Attribution-Specific Considerations & Paid Media Trade-offs
Gamification can strengthen ad performance and provide richer data, but it also introduces new complexities that paid media and attribution teams need to manage carefully.
Tracking Complexity
Gamified ads trigger many micro-events, such as level completions or reward claims.
These events increase data volume and must integrate cleanly with your attribution system.
Without proper tagging and ingestion, valuable signals can be lost or misattributed.
Interaction Does Not Equal Incrementality
High engagement inside the ad doesn’t guarantee higher conversion lift.
To understand true impact, use controlled experiments like A/B tests or hold-out groups.
This separates meaningful behavioral change from surface-level interaction.
Creative Costs vs. Return
Playable and interactive formats can be more expensive to produce or run.
Measure incremental lift and ROAS rather than relying on vanity metrics like time-in-ad.
Focus on the net value added, not just the engagement spike.
Spill-Over and Attribution Noise
Gamification can generate shares, referrals, and organic traffic spikes.
Attribution models must distinguish paid influence from organic behavior to avoid mis-crediting.
This is essential when virality or social competition elements are involved.
Attribution Windows and Event Linking
Define attribution windows that can accommodate both micro-events and delayed conversions.
Ensure each event ties cleanly to downstream actions in your model.
Novelty Fade
Gamification effects often diminish as users become familiar with the mechanic.
Continuous iteration and long-term measurement help maintain performance and accuracy.
Managing these factors ensures that gamification strengthens your attribution strategy rather than complicating it.
Case Study Example: DTC Brand
Imagine a DTC wellness brand that wants to test game mechanics in digital advertising to boost ROAS.
Instead of a static video, the team launches a 30-second playable experience where users swipe to “catch” product ingredients and earn a completion badge. Finishing the mini-game unlocks a small coupon code, which can be redeemed at checkout.
Every part of this journey fires a micro-event: game start, game completion, badge earned, coupon revealed, and click-through. These events flow into the brand’s attribution system and are linked to purchases made within a seven-day window.
When compared to a non-gamified control ad, the difference is clear. The interaction rate doubles, but the more important lift is in downstream performance. After controlling for creative cost and impression volume, incremental ROAS rises by 15 percent.
The attribution model connects the sequence (completion of the mini-game leading to a click, and then to a purchase) with far more confidence than it could with a single CTR metric.
The brand also segments users based on in-ad behavior. Badge earners show a higher likelihood of redeeming offers later, so the team builds a remarketing audience specifically around this group. That segment ultimately becomes one of the brand’s highest-LTV cohorts.
Finally, the attribution team uncovers an unexpected spill-over effect. Some users complete the mini-game, skip the click-through, and later navigate directly to the site to buy.
Because each badge earned is tied to a unique identifier, the attribution model correctly captures those conversions as influenced by the gamified ad rather than treating them as organic traffic.
Actionable Next Steps for Paid Media & Attribution Teams
To turn gamified ads into measurable results, teams can follow a structured rollout that balances creative experimentation with attribution discipline:
Start with an audit of your current paid campaigns. Identify ads with low interaction, high bounce, or unclear attribution paths. Prioritize formats that would benefit most from richer behavioral data.
Define a focused pilot campaign. Choose one game mechanic and build the interactive experience around it. Specify the micro-events you want to track and confirm your attribution system can ingest them. Establish a clean control or hold-out group to measure incremental lift.
Create dashboards that connect in-ad interactions to downstream outcomes. Track how events like level complete or coupon unlocked lead to clicks, visits, and conversions. Ensure the data flows smoothly across your multi-touch attribution model.
Compare performance against your baseline. Evaluate engagement rate, completion rate, conversion rate, incremental ROAS, and attribution share. Look not just at volume but at the quality and predictability of behavioral signals.
Plan for scale once the pilot proves its value. Refine game mechanics, expand to new channels, and adjust attribution logic to handle additional touchpoints such as shares, referrals, or repeat plays. Continue validating incrementality to ensure you are measuring true impact rather than inflated engagement.
By following these steps, teams can turn gamification into a repeatable growth and measurement lever rather than a one-off creative experiment.
Turn Engagement Into Measurable Growth
Gamification gives paid media teams a powerful way to boost engagement while generating clearer, higher-quality data for attribution.
When every interaction becomes a measurable signal, it becomes easier to understand what truly drives conversions and where to invest next.
If you’re exploring gamified ads for the first time, start small. Run a focused pilot, measure incremental lift, and refine from there.
With the right structure in place, gamification can become a scalable part of your paid strategy.
Winning marketing campaigns don’t just measure performance, they dig deeper to understand who is engaging and converting.
On LinkedIn, segment reporting makes this possible by turning professional demographics and company information into actionable data for targeting, creative, and budget.
In this context, segment reporting means analyzing campaign performance by specific audience slices: job function, seniority, industry, company size, company list membership, skills, and more.
Instead of relying on aggregate metrics that mask which roles or industries are truly performing, marketers can identify high-value segments and eliminate wasted spend. The result is a clearer picture of which audiences drive real pipeline and revenue.
This article will walk you through how to access LinkedIn’s audience analysis, interpret what the data is telling you, and build a repeatable workflow for segment-level analysis.
By the end, you’ll know not only how to read the reports but also how to act on them: refining targeting, sharpening creative, reallocating budgets, and ultimately delivering better ROI from every campaign.
What Is LinkedIn Segment Reporting & Why It Matters
LinkedIn segment reporting is a structured approach to analyzing campaign performance through the lens of professional demographics and firmographics.
Instead of looking at aggregate results alone, you break performance down by audience slices like job function, seniority, industry, company size, company list membership, skills, and more. This level of granularity shows you not just how campaigns perform overall, but which specific audiences are driving value.
The result is more informed decisions about where to focus your targeting, creative, bids, and budgets.
Why LinkedIn segment reporting matters:
Precision targeting: Analyze LinkedIn ad audiences by job titles, seniority levels, and industries that generate efficient reach and higher-quality conversions.
ABM enablement: Validate whether priority accounts and lookalike audiences are truly engaging, then double down where coverage or resonance is strongest.
Budget clarity: Go beyond broad CPA averages. Reroute dollars toward segments with greater down-funnel impact and ROI.
Creative guidance: Compare segment-level performance to align headlines, value propositions, and formats with the unique needs of decision-makers.
Segment reporting is the bridge between LinkedIn’s audience analysis data and practical media decisions. By using demographics to optimize LinkedIn ads, you can refine targeting, sharpen creative, and reallocate budgets to the audiences that actually move pipeline and revenue.
Core Tools & Access Paths
If you’re wondering how to segment LinkedIn ad performance, the platform provides multiple ways to dig into audience insights and segment performance.
Whether you’re planning a campaign, optimizing live ads, or scaling through automation, these tools give you the data you need to make informed decisions.
Campaign Manager, Audience Insights (pre-launch and always-on)
Audience Insights helps you understand the makeup of your target audiences before you even launch.
You can explore LinkedIn’s matched audiences reporting, and save audiences by job function, titles, seniority, industry, company size, skills, interests, and more.
This view validates whether your audience definition matches your goals and highlights adjacent segments worth testing.
Campaign Manager, Demographics (live campaigns)
Once campaigns are live, the Demographics tab in Campaign Manager analytics shows which professional cohorts are actually driving performance.
You can break down impressions, engagement, and conversions by job title, seniority, industry, and other factors.
Filters for objective, date range, and campaign hierarchy (account, group, campaign, or ad) make it easy to pinpoint where performance diverges.
Reporting Dashboards & Exports
For deeper analysis, saved reporting views let you track segment-level KPIs such as impressions, CTR, leads, cost per lead, and conversions.
Exporting CSVs makes it simple to compare cohorts across campaigns or over time. Cohort-style tables, broken out by week or month, help you spot durable segment winners rather than short-lived spikes.
APIs (high-level, vendor-neutral)
For advanced teams, LinkedIn Audiences and Linkedin Ad Reporting API endpoints offer programmatic access to demographic breakdowns and performance data.
This is especially valuable for organizations running account based marketing (ABM) programs, frequent experiments, or multi-market rollouts, where standardized dashboards and recurring QA checks are critical.
API reporting for LinkedIn audiences enables automation, ensuring you can scale insights across regions, campaigns, and teams without manual effort.
How to Read & Interpret Segment Reports
Segment reports are most useful when you know which dimensions to focus on and how to separate signal from noise.
By looking beyond surface-level metrics, you can uncover which audiences are actually driving qualified engagement and pipeline.
Key dimensions to monitor include:
Job function vs. job title: Broad functions reveal trends, while titles give you role-specific nuance.
Seniority: Helps distinguish between influencers and decision-makers.
Industry and company size: Pinpoint where your message resonates most strongly.
Location and account list membership: Ensure coverage in priority markets or ABM lists.
Skills and interests: Identify emerging pockets of relevance you might not have considered.
Patterns to look for include:
High engagement, low conversion: Creative resonates but the offer or landing page doesn’t. Test clearer CTAs or deeper content.
High conversion, low reach: Indicates a strong but narrow segment. Expand with lookalikes, audience growth, or broader title nets within the same function/seniority.
Seniority spread: If independent contributors click but managers convert, split creative streams to address each layer’s specific challenges and motivations.
Industry outliers: When certain verticals overperform, spin up industry-specific ads or landing pages to capitalize on momentum.
Common pitfalls to watch out for include:
Over-segmentation: Tiny cohorts can run into privacy thresholds or lack statistical reliability.
Mismatched attribution windows: Comparing metrics tracked on different goals or timeframes skews interpretation.
Short-term spikes: Press coverage or promos can inflate a segment temporarily. Don’t mistake anomalies for durable performance.
The takeaway is that you should read segment reports with both precision and context, with the goal of building a reliable picture of which audiences consistently deliver value.
Strategies & Plays Enabled by Segment Reporting
Segment reporting isn’t just about understanding what happened; it’s about using insights to shape smarter campaigns.
With the right read of audience-level performance, you can refine targeting, sharpen creative, and plan budgets with far more confidence.
Targeting Refinement
Trim underperforming segments, even when their CPAs look “cheap” on the surface. Low-quality leads waste sales resources.
Expand into adjacent roles within high-performing functions. For example, if finance directors perform well, test controllers or VPs with layered seniority filters.
ABM Coverage & Density
Map segment performance against your ABM account lists to see which departments and seniorities are underrepresented.
Build micro-plays tailored to different buying group roles (e.g., economic buyer vs. champion) with distinct messaging and offers.
Creative and Message Testing
Run side-by-side ads for top job functions: leadership messaging vs. operator-level messaging.
Use segment metrics to reveal which value propositions (efficiency, risk reduction, revenue growth) resonate most before scaling spend.
Offer and Format Strategy
If technical roles engage but don’t convert, test deep-dive assets like comparison guides or product sheets.
For executives, emphasize outcomes and ROI. Match ad formats to preferences: document ads for procurement, short videos for time-strapped execs.
Forecasting & planning
Apply historical segment performance to estimate reach, clickthrough rate (CTR), and cost per lead (CPL) at different budget levels by market or vertical.
Prioritize testing in areas with the highest modeled impact on pipeline, not just the cheapest clicks.
Automation & QA
Create a recurring dashboard view (or API-powered workflow) to monitor top segments weekly.
Automate alerts for performance deterioration, surface new opportunities, and track lift after creative refreshes.
Segment reporting transforms guesswork into a repeatable playbook so you can ensure budget and creative energy go where they’ll have the most impact.
Tools, Data, and Setup
The value of segment reporting depends on the quality of your data and the discipline of your setup. Without clean tracking, well-constructed audiences, and a repeatable workflow, insights can easily get muddled or misapplied.
Tracking and Data Hygiene
Confirm that website and conversion tracking is properly implemented via the LinkedIn pixel or Conversions API.
Ensure offline conversions and CRM leads sync reliably so that segment learnings tie back to real revenue.
Standardize UTM parameters across campaigns to reconcile top-of-funnel engagement with down-funnel metrics.
Audience Construction
Define clear inclusion and exclusion rules for saved and matched audiences. Document naming conventions and change history to maintain transparency.
Align account lists directly with sales priorities, refreshing them regularly through uploads or connected integrations.
Workflow
Plan: Validate audiences with Audience Insights and set hypotheses for top segments.
Launch: Build campaigns with segment-aware ad sets and creative variants.
Measure: Conduct weekly segment reviews and monthly trend analyses via dashboards or exports.
Iterate: Promote winners, pause laggards, and adjust creative or landing pages for segment fit.
Governance
Respect LinkedIn’s privacy thresholds and avoid micro-segmentation that produces unreliable data.
Standardize definitions of key metrics (clicks, leads, pipeline stages) and attribution windows across marketing and sales teams to ensure consistency.
By combining clean data, disciplined audience design, and a structured workflow, segment reporting becomes a reliable engine for ongoing optimization rather than a one-off analysis.
Implementation Considerations
Even the best tools and workflows won’t deliver results without clear standards and disciplined execution.
To make segment reporting sustainable, marketers need shared definitions, a consistent cadence, and guardrails that keep analyses meaningful.
Shared Definitions
Establish canonical lists for job functions, titles, and industries, with clear rules for how to roll up smaller cohorts.
Standardize audience and campaign naming conventions, and keep version logs whenever targeting or creative changes occur.
Cadence
Run weekly optimization cycles using segment-level reads.
Conduct monthly or quarterly reviews of audience mix by market and vertical to spot broader trends.
KPIs by Goal
Awareness: Reach, frequency, and qualified engagement rate by segment.
Acquisition: CTR, CPL, conversion rate, and cost-per-qualified-lead by segment.
Pipeline: Lead-to-opportunity rate and cost-per-opportunity by segment.
Efficiency: Incremental lift after creative or offer changes, plus payback period trends.
Risk management
Set guardrails for minimum audience sizes to avoid over-fragmentation and unreliable data.
Account for seasonality and macro events when comparing month-over-month segment performance.
With disciplined definitions, cadence, and guardrails, segment reporting becomes a reliable source of insights and consistent performance gains.
Turning Insights Into Action
Segment reporting cuts through the noise of aggregate metrics to show which professional audiences (by title, seniority, industry, and beyond) are truly driving performance.
These insights aren’t just descriptive; they enable smarter targeting, sharper creative, and more efficient budget allocation across every stage of the funnel.
To make the most of LinkedIn’s ad reporting capabilities, keep it simple:
Commit to a weekly routine. Review your top-performing segments, refresh one creative or targeting variable, and adjust budgets accordingly.
Save a go-to Audience Insights view. For example, segment by job function and industry, and dedicate 20 minutes each week to check in on performance.
By treating segment reporting as an ongoing habit rather than a one-off analysis, you build a repeatable system for turning LinkedIn’s demographic depth into measurable growth.
This article, our associated whitepaper, and the webinar I hosted, are based on Northbeam’s internal proprietary Clicks-Only data.
For DTC brands, Cyber Week 2025 wasn’t just “another record year.” It was a clear signal that peak season is now a strategy game, not a “spend big and hope for the best” game.
Across Northbeam’s customer base, we saw ad spend rise by just over 9% year over year, while revenue grew more than 13%, lifting MER even as first‑time CAC climbed about 8%. In other words: shoppers are still willing to buy, but they’re a lot more selective about when and how they convert.
This blog is a high‑level walkthrough of what we saw across thousands of DTC brands during Cyber Week 2025, and how the best teams are already adjusting their playbooks for 2026.
If you want the full channel‑by‑channel, industry, and company‑size breakdowns, grab the report this blog is based on:
1. The window keeps widening: seed early, harvest late
Long gone are the days of lining up outside a big‑box retailer at midnight. The in‑store moment may have faded, but the online punch of Black Friday/Cyber Monday is absolutely still there, it just starts earlier and stretches longer.
When we looked at the 21‑day run‑up to Cyber Week (Nov 4–24), a few things stood out:
Brands increased spend in the lead‑up; roughly mid‑single digits overall.
Revenue in that same period grew a bit faster than spend.
First‑time CAC jumped sharply, into the mid‑teens percentage‑wise.
Put simply, advertisers were priming the pump. They were willing to pay more to get in front of new customers before the sale, knowing that a meaningful chunk of that demand would only convert once the “real” offers went live.
That’s conversion lag in practice:
Dollars spent early in November convert during the sale itself, sometimes days or even weeks later.
If you only look at 1‑day click ROAS in‑platform, this early‑month spend can look terrible. When you look at it through a multi‑touch, cross‑channel lens, it suddenly becomes clear that your “expensive” pre‑BFCM dollars are actually doing a lot of the heavy lifting for peak week performance.
In the full report: we break down the Nov 4–24 period in more detail and show exactly how spend, revenue, MER, and first‑time CAC moved during those three weeks.
Watch the full webinar I hosted right here!
2. Cyber Week is a week‑long arc, not a single spike
From a distance, Cyber Week still looks like what you’d expect: spend ramps into Black Friday, stays elevated through Cyber Monday, then cools off. But when you zoom in day‑by‑day, the pattern is a lot more instructive.
Here’s the simplified version of what we saw:
Tue–Thu (pre–Black Friday) Brands ramped into the peak. Spend and revenue both moved up double‑digits vs. last year, MER improved modestly, and CAC started to climb as consumers browsed and waited.
Black Friday Still the single biggest spend day of the week, but also the point where efficiency compressed the most. MER dipped relative to 2024 while CAC jumped by the mid‑teens as auctions got crowded and everyone piled in at once.
Saturday & Sunday Many brands pulled back on spend on Saturday, and MER actually rebounded as the most motivated shoppers kept converting. By Sunday, both spend and revenue started to pick up again heading into Monday.
Cyber Monday Spend re‑accelerated and efficiency improved vs. Black Friday, MER climbed, and CAC rose more modestly. This is the “I’ve been browsing all weekend and I’m finally pulling the trigger” behavior we heard anecdotally across accounts.
This is why we’re pushing clients to stop thinking in terms of “What’s my Black Friday budget?” and start thinking in terms of “What’s my Cyber Week arc?”
Some practical implications:
You don’t need to front‑load 100% of your aggression into Friday. If you’re not seeing the results you expected, there’s a strong case for shifting some budget into Sunday/Monday next year, where competition can be slightly softer and intent is still high.
When you evaluate performance, judge the week as a system, not as isolated days. A “weak” Thursday on a click‑only view might be exactly what you need to make Monday a record day.
3. Meta and Google still own the spine, but diversification is real
Despite all the noise about new platforms, Meta and Google are still the foundation of DTC performance marketing during Cyber Week.
Across our dataset:
Together, Meta and Google represented roughly 80% of Cyber Week digital ad spend.
Both platforms delivered strong revenue growth year over year and improvements (or stability) in short‑window ROAS, even as overall auctions remained competitive.
In other words: if you’re trying to build a BFCM media plan without Meta and Google as your budget spine, you’re swimming upstream.
That said, we did see meaningful wallet‑share movement elsewhere:
TikTok: Brands significantly increased spend here, and revenue kept pace, especially for those willing to lean into video‑heavy prospecting and measure impact with Clicks + Deterministic Views (C+DV) instead of click‑only.
Pinterest: Also saw solid budget growth. Performance was more mixed across accounts, but for visual, giftable categories, Pinterest remained a strong discovery and conversion assist channel.
YouTube: Interestingly, we saw a slight decline in wallet share but much stronger revenue growth and ROAS among brands that stuck with it — alongside significantly cheaper CPMs. To us, that’s a classic sign of a channel that looks “meh” in last‑click, but quietly delivers revenue when you look at it through a multi‑touch lens.
The core takeaway:
Use emerging channels to diversify reach, not to “replace” Meta/Google.
Your job is to build a spine (Meta + Google), then layer in TikTok, Pinterest, YouTube, Snap, etc. in a way that actually adds incremental lift rather than just cannibalizing what you already have.
In the full report: we show detailed wallet share shifts, CPM/ROAS changes by platform, and a simple set of “rules of thumb” for how much budget most brands are safely putting into each non‑core channel today.
4. Size and category dictated who actually “won” Cyber Week
On aggregate, Cyber Week looks great. Under the hood, performance diverged sharply by company size and industry.
By company size
When we segmented brands by annual revenue, three different stories emerged:
Large brands (>$100M)
Spend up high single digits.
Revenue up mid‑teens.
MER up meaningfully, with only a small CAC increase.
These teams tended to have their BFCM strategy dialed in — from promo structure to channel mix to upper‑funnel measurement.
Mid‑market brands ($10M–$100M)
Biggest increase in investment.
The sharpest spike in CAC.
MER roughly flat year over year.
Growth was there, but it often came without efficiency gains. These are the brands that felt the most pressure from auction dynamics.
Smaller brands (<$10M)
More cautious on spend.
Modest revenue lift.
Small MER improvements, but less ability to simply “buy their way” into the peak.
By industry
We also saw clear category‑level winners and laggards:
Strong performers:
Beauty & Personal Care — grew spend and revenue, while actually improving first‑time CAC, making it one of the standout categories this year.
Fashion & Accessories — consumers rewarded increased investment with outsized revenue growth; still a core Q4 gifting engine.
Home & Furniture — solid returns suggest that considered, higher‑ticket purchases still respond well to strong seasonal offers.
Sporting Goods & Fitness — brands that pulled back spend actually improved unit economics significantly, creating room to re‑invest smarter in 2026.
Slower performers:
Food & Beverage — advertisers pulled back, which helped stabilize MER, but rising CAC and modest revenue growth point to a tougher environment.
Health & Wellness — arguably the most challenged category in the dataset: heavy investment, softer revenue, sharply higher CAC. Many brands tried to pull forward their usual Q1 “new year, new me” demand into Cyber Week — and it didn’t fully pay off.
The key message:
Your BFCM strategy has to be category‑specific. Elasticity, gifting dynamics, purchase frequency, and payback windows are wildly different by vertical — your benchmarks and budget ladders should be, too.
5. What to do now: four moves before you plan BFCM 2026
Coming out of Cyber Week, here’s how I’d recommend you operationalize these learnings.
1. Reset your benchmarks
Your “peak week” numbers are already out of date. Update:
MER and CAC targets back to a more business‑as‑usual range.
Channel‑level ROAS guardrails so your teams don’t keep optimizing to Black Friday conditions in January.
If you’re with Northbeam, this is a great time to align your Benchmarks/Stoplights with what “good” actually looks like post‑BFCM.
2. Use model comparison to understand what really drove 2025
If you only look at click‑only models, you’ll wildly underestimate:
Upper‑funnel channels like TikTok, YouTube, Snapchat.
Video‑heavy and creator/UGC campaigns.
The early‑November “seed” campaigns that set up your Cyber Week harvest.
Swap between Clicks‑Only and Clicks + Deterministic Views (C+DV) in your reporting to see where net‑new demand actually came from in 2025, and use that to inform where you test harder in 2026.
3. Test smarter with MMM and incrementality
Media mix modeling (MMM) and incrementality testing have gone from “nice to have” to non‑negotiable:
Use MMM to re‑forecast your 2026 channel mix based on what worked in 2025 — especially if you’re trying to push into new channels without blowing up CAC.
Use incrementality tests to validate that your “growth channels” are actually adding incremental revenue, not just shifting credit around. Northbeam’s new incrementality solution also feeds those learnings back into MMM, so your model gets smarter, faster.
4. Lean back into creative and asset experimentation as CPMs cool
CPMs almost always cool off post‑BFCM, especially in Q1. That’s your window to:
Launch new hooks, formats, and UGC concepts across your highest‑elasticity categories.
Refresh offers and funnels for categories that struggled this year (looking at you, health & wellness and F&B) before you repeat 2025’s spend levels.
Systematically log top‑performing creative from 2025 so you’re not reinventing the wheel next November.
Want the full data and all the charts?
This blog barely scratches the surface of what we saw in the data.
If you’re planning budgets for 2026 — or just trying to sanity‑check how your brand stacked up — I’d strongly recommend digging into the full whitepaper:
Channel mix and wallet‑share shifts, including TikTok, Pinterest, YouTube, Snap, and more
Performance by company size and industry, with detailed MER and CAC changes
A deeper dive on how to use C+DV, MMM, and incrementality together
Cyber Week 2025 made one thing clear: the demand is still there — but the brands that win are the ones that plan, measure, and adapt smarter than everyone else.
As privacy rules tighten and third-party cookies fade out, marketers are looking for reliable ways to reach the right people and measure what actually drives results; second-party data offers a practical path forward.
In this guide, we break down what second-party data is, why it matters, and how to integrate it into your performance and measurement workflows.
You’ll learn the core benefits, the steps to build a data partnership, how second-party data affects attribution accuracy, and the potential risks to watch for as you scale.
Most importantly, you’ll learn how to use partnerships to unlock better second-party audience data, clearer insights, and stronger returns with privacy top of mind.
What Is Second-Party Data & Why It’s Relevant
Second-party data is essentially another organization’s first-party data that you access through a direct, trusted partnership.
It sits in the middle of the data ecosystem:
First-party data is what you gather directly from your own customers.
Third-party data is purchased or aggregated from multiple sources and often loses fidelity along the way.
Second-party data combines the best of both worlds.
You gain access to new audiences and behavioral signals, but with the clarity and provenance that third-party datasets typically lack.
This matters more than ever. With third-party cookies fading out and privacy regulations raising the bar on data governance, marketers need scalable ways to target effectively without compromising on accuracy.
From an attribution standpoint, the impact is even more meaningful. Higher match rates and cleaner identifiers reduce “unknown” traffic, improve funnel visibility, and help performance teams measure campaign outcomes with greater confidence.
In short, better data leads to better decisions, and second-party data delivers both the quality and context needed to get there.
Key Benefits & Use Cases of Second-Party Data
The benefits of second-party data partnerships center around new opportunities for marketers who want to scale intelligently while keeping data quality and governance front and center.
Here’s how to use second-party data for marketing campaigns:
Expanded Reach with Quality
Second-party partnerships give you access to high-intent audiences you wouldn’t reach on your own, without sacrificing signal quality.
Partner data comes with stronger identifiers, clearer consent, and more reliable behavioral signals. That makes it easier to target efficiently and avoid wasted spend.
Audience Enrichment and Lookalike Building
Because partner data often includes detailed attributes or engagement insights, it can enrich your existing segments and strengthen lookalike modeling. When your seed audiences improve, so does the performance of your acquisition campaigns.
Better Attribution and Journey Insight
Second-party data can fill critical gaps in your first-party view. If your customers interact with adjacent brands, publishers, or ecosystems before reaching you, partner data helps surface those touchpoints and clarify the full funnel path.
Co-Marketing and Shared Assets
Marketing data partnerships don’t stop at targeting. They enable joint campaigns, shared audience insights, and collaborative creative strategies. This can lead to lower acquisition costs, expanded brand equity, and a more unified customer experience across channels.
Competitive Advantage
Because many second-party relationships are exclusive or bilateral, they offer differentiation competitors can’t easily replicate. Access to unique, high-fidelity audiences often translates into better performance and a more resilient acquisition engine.
Framework for Acquiring & Deploying Second-Party Data
Here are the steps teams should follow to evaluate, integrate, and activate partner data with confidence.
Identify Complementary Partners
Look for organizations whose audiences overlap with or complement your target customers, without being direct competitors.
Clarify what types of data they hold (purchase behavior, loyalty activity, interest signals) and how those signals support your marketing objectives.
Negotiate Data Sharing Terms and Governance
Establish clear contracts outlining what data will be shared, how it can be used, the duration of access, and all compliance requirements.
Use clean rooms or secure data exchange frameworks when needed to preserve privacy and identity safety.
Validate the partner’s data sources, formats, identifiers, segmentation logic, and match rates.
Combine partner data with your first-party datasets (when privacy and consent allow) or use it to build new segment pools.
Update your tracking and attribution systems so new data layers flow into audiences, measurement, and modeling.
Activate and Measure
Build campaigns using second-party segments for bidding, enrichment, or lookalike expansion.
Track performance against baseline first-party audiences to understand lift.
Feed results back into attribution models to measure incremental conversions, ROAS impact, and reach extension.
Review and Iterate
Continuously monitor how partner segments perform and whether their quality changes over time.
Renew, expand, or replace partnerships based on results.
Adjust your activation strategies (such as lookalike modeling or channel mix) using learnings from the pilot and ongoing performance.
Attribution & Performance Implications
Second-party data and attribution strategy can have an outsized impact on overall performance because they add clarity to parts of the customer journey that are normally opaque.
When you introduce partner-sourced identifiers and behaviors into your stack, match rates typically rise and audience definitions sharpen. That means fewer users fall into “unknown” buckets, giving your attribution models a more complete and accurate picture of how people move through the funnel.
This additional visibility matters most when partner data captures behaviors outside your usual domain.
If a customer engages with an adjacent brand or publisher before finding you, those touchpoints often disappear in a standard first-party view. With second-party data, you can track that interaction through to conversion and understand its true contribution to the path to purchase.
These new signals can also reshape how channels perform in your models. When previously invisible users become identifiable, conversion curves may shift, and channels that looked ineffective may reveal stronger influence.
It’s important to interpret these changes carefully. Not all improvements are inherently incremental, and partner audiences can introduce bias if left untested.
The most reliable approach is to use control groups, lift studies, or holdout tests to confirm the real, incremental impact of second-party data on performance.
Challenges, Risks, & Best Practice Considerations
Without valid consent frameworks, partner alignment, and proper documentation, even high-quality data can introduce legal and reputational risk.
Here are some challenges and best practices to consider:
Data Quality and Freshness
Partner data isn’t automatically reliable.
Audits are still essential to check for outdated records, inconsistent identifiers, or misaligned segmentation. Stale or noisy data can derail targeting, inflate acquisition costs, and lead to misleading insights.
Technical Integration Complexity
Integrating partner datasets requires precise matching logic, consistent identifiers, and alignment across schemas.
Attribution and analytics pipelines may need updates to properly ingest and model new data layers. Small mismatches can create major downstream errors.
Partner Misalignment
If the partner’s audience doesn’t overlap meaningfully with your target customers, performance will lag. Alignment should be evaluated early through audience comparisons, shared goals, and a clear understanding of what behaviors or attributes each side contributes.
Over-Dependence on a Single Partner
Relying heavily on one data partner can create operational and strategic vulnerability. Diversification reduces the risk of data degradation, partnership changes, or cost escalations affecting performance.
Elevated Governance Requirements
Second-party data should be governed with the same rigor as your first-party data. Maintain clear documentation, lineage tracking, quality standards, and dashboards that monitor performance and usage.
Strong governance ensures long-term reliability and trust across teams.
Case Study: Second-Party Data in Practice
Let’s see the value of second-party data by looking at how a consumer brand could partner with a publisher in an adjacent, non-competitive niche.
In this case, the publisher had a highly-engaged subscriber base with strong behavioral signals like content interests, purchase intent markers, and consistent on-site activity.
Through a direct data partnership, the brand gained access to a curated segment of these engaged users and used it to build targeted ad campaigns.
Once activated, the difference was immediate.
Match rates increased because the partner’s identifiers were clean and consented, and cost per acquisition dropped compared to the brand’s standard prospecting audiences.
In attribution reporting, the partner-derived segment converted at a materially higher rate and incremental ROAS outperformed baseline audiences by a meaningful margin. Those early signals gave the team confidence to scale spend and shift more budget toward the high-performing segment.
The full funnel told an even clearer story.
Partner data informed the audience build, the audience powered targeted ads and remarketing flows, and those campaigns fed clean, trackable conversions back into the attribution model.
That closed loop from data to activation to insight allowed the brand to refine strategy, allocate budget more efficiently, and grow performance with far greater precision.
Get Started with Second Party Data
To help your team move from theory to execution, here are clear next steps for evaluating, piloting, and scaling a second-party data partnership:
Map your current data inventory. List all first-party, third-party, and potential second-party sources to understand where partner data can add the most value.
Identify 2-3 potential partners. Look for organizations whose audience signals align with your marketing objectives, and begin exploratory conversations about partner data sharing.
Define the data-sharing agreement. Clarify what data will be exchanged, how it can be used, compliance requirements, usage rights, and the success metrics you’ll measure against.
Set up a pilot campaign. Select one partner dataset, build an audience segment, activate it across your media stack, and measure performance relative to your baseline segments.
Build a performance dashboard. Track metrics like match rate, conversion rate, CPA, incremental lift, and ROAS to understand real impact.
Review and decide on next steps. After the pilot, determine whether to scale the partnership, refine the approach, or test a new partner, and document learnings to guide future work.
Taken together, these steps create a structured path for safely testing second-party data while building confidence in its long-term value.
Elevate Your Data Strategy
Second-party data gives marketing and attribution teams a powerful way to boost targeting accuracy, improve measurement, and expand reach without sacrificing governance or data quality.
By partnering with trusted organizations, you gain access to high-fidelity signals that strengthen modeling, clarify the customer journey, and unlock new performance opportunities. The key is to approach these partnerships with intention: from evaluating alignment and defining governance to testing, measuring, and iterating your way into a scalable strategy.
Used well, second-party data can become a durable advantage in an increasingly privacy-first world.
Many marketing teams talk about being “data-driven,” but data maturity is a spectrum.
Some teams rely on basic reports pulled at the end of the month. Others operate with real-time insights, automated decisioning, and predictive models shaping strategy.
Understanding where your team falls on that spectrum, and how to move forward, is the core purpose of a marketing data maturity model.
In this guide, we’ll break down what “data maturity” means, the key dimensions that shape it, and the five stages most teams move through when moving from an ad-hoc to a data-mature marketing organization.
What “Data Maturity” Means for Marketing
In a marketing context, data maturity reflects how effectively your team uses data to guide strategy, personalize outreach, optimize campaigns, and tie activity to measurable business outcomes.
Mature organizations don’t treat data as a reporting function. They use it as the connective tissue of their entire growth engine, from segmentation and creative testing to budgeting and lifecycle strategy.
The impact is tangible. Research from Boston Consulting Group (BCG) found that more digitally mature brands increased sales by 18% and improved cost efficiency by 29% compared to less mature peers.
In other words, data maturity is one of the clearest competitive advantages a marketing team can build.
Most teams can also recognize the early warning signs that they aren’t as data-mature as they’d like to be:
Data lives in disconnected tools and channels
Dashboards are isolated, inconsistent, or manually maintained
Reporting depends on spreadsheets and heroic effort rather than repeatable systems
Campaigns rely on gut instinct because insights are incomplete or hard to access
Instead of measuring the full customer journey, teams focus only on surface-level metrics like clicks or impressions.
These patterns aren’t failures. They’re indicators of where you are today, and a starting point for building a more integrated, data-mature marketing organization.
Key Dimensions of a Data Maturity Model for Marketing Teams
To understand your level of data maturity, it helps to break the concept into core dimensions:
Strategy & Alignment
Data maturity starts with strategy and alignment. This means marketing KPIs are explicitly tied to business objectives, not created in isolation.
Mature teams work within a culture where data-informed decision-making is expected, shared, and reinforced, from leadership priorities to campaign planning.
Without this alignment, even the most sophisticated tools fail to drive meaningful outcomes.
Data Governance & Quality
Governance covers how your data is collected, cleaned, standardized, and maintained. High-maturity teams invest in master data management, ensure data accuracy and accessibility, and define processes that keep customer data trustworthy over time.
Data & Technology Architecture
Your marketing tech stack is only as powerful as the connections between its components.
Modern architecture enables cross-channel data flow, unifies event and customer data, and supports real-time analytics.
If tools operate in silos or require heavy manual intervention, insights will lag and opportunities will be missed.
Analytics Capability
Analytics maturity evolves in stages:
Descriptive (what happened)
Diagnostic (why it happened)
Predictive (what will happen)
Prescriptive (what to do next)
As teams progress, data becomes less about reporting and more about foresight. Mature organizations incorporate modeling, forecasting, and experimentation into everyday workflows.
People & Culture
Technology can only take an organization so far. Data-mature teams cultivate literacy across their marketing organization and beyond, encouraging experimentation, and promoting tight collaboration among marketing, analytics, product, and engineering.
On these types of teams, curiosity and cross-functional problem-solving become cultural norms.
Execution & Activation
Ultimately, maturity shows up in how well data fuels daily marketing activity.
This includes smarter segmentation, personalization at scale, rigorous measurement, consistent optimization, and campaigns that adapt in real time.
The most mature teams close the loop between insight and action quickly and repeatedly.
These six dimensions form the framework for assessing your current state and identifying where maturity improvements will have the highest impact.
Typical Stages of Marketing Data Maturity
Every marketing team sits somewhere on the data-maturity spectrum. Understanding your stage helps you benchmark your current capabilities and set realistic expectations for growth.
Most organizations fall into one of five broad categories:
Stage 1: Ad Hoc / Emerging
Teams at this stage rely on basic campaign metrics like impressions and clicks.
Data lives in channel-specific tools, reporting is inconsistent, and insights rarely influence strategy.
Most decision-making happens through intuition or past experience, making this the least scalable stage.
Stage 2: Developing / Building
Some unification begins to take shape in Stage 2. Teams might connect a few data sources or build early cross-channel dashboards.
Segmentation and personalization are possible but limited, often requiring manual effort. This is the stage where teams recognize the value of data but haven’t yet operationalized it.
Stage 3: Connected / Proactive
Data infrastructure becomes more robust and reliable in Stage 3. A shared customer view emerges, analytics begin driving decisions, and campaigns are optimized across channels rather than within silos.
Teams at this stage shift from reactive reporting to proactive insight generation.
Stage 4: Strategic / Data-Mature
In Stage 4, marketing becomes deeply integrated with predictive analytics, real-time optimization, and full-funnel measurement aligned to business outcomes.
Testing is systematic, insights flow quickly into activation, and data literacy is strong across the organization. This is where real competitive advantage becomes visible.
Stage 5: Optimized / Transformational
In Stage 5, marketing operates as a serious growth engine. AI, automation, and advanced modeling shape strategy at scale. Data is embedded into culture, not just tooling. Teams move with agility, adapting to customer behavior in real time and continuously learning from every interaction.
By identifying your stage, you can build a roadmap that’s ambitious but achievable. But how do you know exactly what stage you’re in now?
How to Assess Marketing Data Maturity
To assess your team’s data maturity, start by conducting a data-driven marketing maturity audit.
This involves scoring your team across each key dimension listed above:
Strategy and alignment
Data governance and quality
Data and tech architecture
Marketing analytics maturity
People and culture
Execution and activation
Give yourself a score for each dimension. The goal isn’t to produce a perfect number, it’s to reveal patterns and help you decide where to focus first.
Many teams pair this internal audit with external benchmarking. Frameworks from Gartner and other industry leaders offer structured questionnaires and maturity rubrics that help you calibrate your results against peers and best-in-class organizations.
To bring your assessment to life, visualize your scores. A radar chart or similar diagnostic view makes maturity gaps immediately visible and highlights where investment will drive the biggest lift.
Often, you’ll see that one or two lagging dimensions disproportionately drag down your overall maturity level.
Most importantly, conduct the assessment collaboratively. Involving stakeholders from marketing, analytics, IT, data engineering, and even leadership ensures you’re capturing the full picture.
It also creates early buy-in for the roadmap you’ll build next, because maturity transformation is rarely a marketing-only project. It succeeds when the entire organization is aligned around the same goals, the same metrics, and the same definition of “data-driven.”
Building the Roadmap to Data-Mature Marketing
An assessment becomes meaningful once it turns into action.
After identifying your strengths and gaps, the next step is to build a phased roadmap that moves your organization toward greater data maturity in a realistic, sustainable way.
Start with Quick Wins
These are low-effort, high-impact improvements that unlock immediate value.
Examples include:
Unifying a few high-value data sources
Standardizing naming conventions or taxonomies
Building or refreshing core dashboards
Implementing basic segmentation or journey mapping
Eliminating manual reporting bottlenecks
Move to Medium-Term Initiatives
These require more coordination and investment, but fundamentally upgrade your marketing operations.
Common examples:
Expanding or modernizing your marketing tech stack
Implementing a customer data platform (CDP)
Enabling real-time analytics pipelines or event tracking
Formalizing experimentation processes
Upskilling teams and reinforcing a data-driven culture
Improving collaboration between marketing, analytics, and engineering
Plan for the Long-Term
These initiatives help you reach full data maturity and create a scalable, self-optimizing marketing engine.
They often include:
Predictive modeling and forecasting
Advanced attribution and measurement
Automated decision-making
Personalization at scale across channels
AI-driven optimization
Tie Initiatives to Business Outcomes
Align your roadmap with measurable company goals such as:
Lower customer acquisition cost (CAC)
Improved retention or customer lifetime value (LTV)
Faster experimentation cycles
Increased marketing-influenced revenue
Establish Governance and Operating Models
To ensure maturity isn’t a one-off project, define:
Data ownership and stewardship
Processes, documentation, and QA standards
Recurring maturity audits and alignment rituals
Measure Maturity Improvements Over Time
Track indicators like:
Percentage of campaigns using data-driven segmentation
Time from insight to action
Cost per test or experiment
Percentage of decisions backed by data
Challenges & Considerations for Marketing Leaders
Even with a clear roadmap, progressing through the stages of data maturity isn’t always straightforward.
Most teams encounter predictable obstacles that can slow down momentum or create false signals of progress. Being aware of these challenges upfront helps leaders navigate them with more intention and fewer surprises.
Data Silos
When channels, tools, or business units operate in isolation, insights stay fragmented and teams lose the ability to understand the full customer journey.
Resource Constraints
Many organizations underestimate the talent required to operationalize data maturity. It’s not just the tools, it’s analysts, engineers, and marketers who can translate data into meaningful action.
Tech Before Culture
A shiny new tool can’t compensate for misaligned processes or a culture that doesn’t value experimentation and data-driven decision-making. Culture and operations must mature alongside the stack.
Misaligned Expectations
Moving from Stage 1 to Stage 4 isn’t a quick win. It requires sustained investment, organizational alignment, and a willingness to change how decisions are made.
Privacy & Ethical Considerations
As personalization becomes more sophisticated, compliance, consent, and responsible data use become non-negotiable components of maturity.
“Maturity Theater”
It’s easy to claim advanced capabilities without the underlying systems to support them. True data maturity is demonstrated through measurable impact, not labels.
With these challenges in mind, leaders can approach maturity work with clearer expectations and a more grounded path forward.
Case Study: How One eCommerce Brand Leveled Up Its Data Maturity
Consider a mid-size ecommerce brand operating squarely at Stage 2 of data maturity.
Their marketing efforts were spread across disconnected tools, each channel measured in isolation. Without a unified customer view, the team relied on surface-level metrics like clicks, opens, and last-touch attribution.
Their campaigns performed reasonably well, but growth had stalled; they couldn’t see what was working across the full customer journey.
To move forward, the team implemented a customer data platform (CDP) and consolidated cross-channel tracking.
They defined a shared set of KPIs, aligned stakeholders around a unified measurement framework, and rebuilt core dashboards to reflect customer-level behavior rather than channel-level outputs.
Within a quarter, the shift was clear.
Reporting evolved from “clicks only” to dark-funnel attribution and multi-touch insights. Campaigns moved from one-off initiatives to journey-based optimization, and tests scaled faster because the team could finally see which touchpoints drove incremental value.
These improvements lifted ROI, shortened the time from insight to action, and moved the organization firmly into Stage 3: Connected and Proactive.
Start Your Maturity Journey Today
Reaching higher levels of data maturity doesn’t require a massive transformation from day one. Small, structured steps can create meaningful momentum and help your team build confidence along the way.
Here is a quick checklist to help you get started:
Host an internal workshop with stakeholders from marketing, analytics, IT, and data engineering. Use the maturity dimensions outlined in this guide to score your current state and surface gaps or inconsistencies.
Identify three priority initiatives: one quick win, one medium-term improvement, and one long-term capability. Assign clear owners, timelines, and success metrics so each initiative has a path to execution.
Build a maturity dashboard to track your progress. Useful indicators like the percentage of campaigns using insights, time from insight to activation, or the share of marketing spend influenced by data.
Establish a quarterly review cadence to revisit your maturity score and evolve your roadmap.
Marketing data maturity isn’t static. It’s a continual process of strengthening foundations, refining processes, and expanding capabilities.
Every step forward compounds, and the brands that invest steadily unlock a clear advantage.
We’re pleased to announce Incrementality, by Northbeam.
Now you can run the trifecta of marketing measurement in one platform, all on the same data: multi-touch attribution, media mix modeling, and incrementality testing.
Northbeam’s incrementality seeks to solve the major downfalls of most incrementality testing solutions while providing easy, automated, and self-service test design.
Modern marketers need causal truth they can act on daily, not one‑off reports that sit in a silo.
Incrementality by Northbeam automates lift testing end‑to‑end and unifies results with your MTA and MMM so you can scale what works and cut what doesn’t, with confidence.
Finally, lift testing you can trust.
But first, let’s understand the problem.
Incrementality, defined
In a marketing context, incrementality is defined as “a metric that measures the additional growth, revenue or other outcomes directly caused by a specific marketing action, beyond what would have happened naturally.”
The “incrementality” of an ad campaign is how much revenue is attributed to that campaign, rather than the natural momentum of your business or brand awareness.
Incrementality measures what results marketing actually generates, rather than what marketing just claims credit for.
This type of experiment design has been around a long time. While incrementality has roots in analysis of variance (ANOVA) testing, modern incrementality testing emerged with the rise of the advertising industry in the mid-20th century.
Alongside media mix modeling, incrementality testing was a way for marketers (with access to much less data than we have today) to statistically prove the impact of their campaigns and marketing budgets.
Incrementality has come back into vogue in the last few years. As marketing continues to take more ownership over revenue generation rather than pure marketing metrics, incrementality is an excellent analysis to bridge the gap between finance and marketing.
Incrementality also fits perfectly into the process of testing and launching new advertising channels in the social media age – this analysis lets you easily understand if a new channel is working or not.
Of course, nothing is ever so simple. Just like with multi-touch attribution and media mix modeling, incrementality has strengths and weaknesses.
As the great statistician George Box said: “All models are wrong, but some are useful.”
The problems with modern incrementality
Here’s what nobody talks about: most marketing incrementality tests are a mess.
You spend weeks designing the test. More weeks waiting for results. Then you get a spreadsheet full of numbers and zero clarity on what to actually do next.
Was the test even valid? Did you account for conversion lag? Did regional bias skew everything? Is the lift statistically significant, or are you just looking at noise?
And the worst part: you can't tell if you just wasted a month and thousands in budget on a test that doesn't actually answer your question.
This is the reality of incrementality testing in 2025.
We’ve heard from hundreds of marketers who struggle with incrementality. If you’re reading this, perhaps you’ve struggled as well.
Here’s the problems we noticed with most incrementality testing:
Fragility and drift. Small changes in targeting, pacing, or regional volatility can quietly invalidate results. Most tools or agencies (who get paid for each test) don’t tell you when a test has gone off the rails.
Siloed outputs. Reports live outside your daily source of truth, making it hard to reconcile lift with MTA reads, MMM guidance, and budget workflows.
Manual orchestration. Spreadsheet‑driven setup and monitoring slow teams down and increase the risk of human error, especially when running multiple tests in parallel.
All of this results in hundreds of thousands of wasted spend, endless frustration, and a cycle of suffering for marketers.
We feel that marketers deserve better. So we built Incrementality, by Northbeam.
Incrementality, by Northbeam
Incrementality by Northbeamis a fully self‑service, automated system that runs inside Northbeam to quantify true causal lift across channels, from test design through to actionable guidance.
You can design, launch, and monitor incrementality tests across channels directly within Northbeam, while ensuring your tests stay statistically significant from start to finish.
The outputs give you clear strategic action directly inside Northbeam, ensuring you can take your learnings and apply them alongside your MTA and MMM analyses.
It replaces vendor handoffs, ad‑hoc geo matching, manual pacing/cooldowns, and disconnected readouts that never make it back into daily optimization.
This is the future of incrementality: automated, accurate, and actionable.
Why Northbeam is different
We designed Incrementality by Northbeam to automatically solve for the major challenges marketers face when working with other incrementality vendors, agencies, or self-built tools.
Here’s what makes Northbeam’s incrementality different:
End‑to‑end automation. From design to platform setup, pacing, cooldowns, validation, and teardown, all inside Northbeam, no spreadsheets required. Just pick your goals and launch.
Bias control (geo + MTA depth). Region selection and balancing leverage conversion lag, Clicks + Deterministic Views, and behavioral patterns to reduce hidden bias versus geo‑only methods.
Continuous validation. The system keeps validating pre‑launch and daily, alerting you before drift undermines test integrity — it won’t let you run a bad test.
Unified measurement. Incrementality feeds back into MTA, MMM, and benchmarks, closing the loop between experimentation, attribution, and planning.
How to use incrementality
As part of your three-part marketing analytics toolbox, incrementality is best for understanding the high-level impact of a campaign or channel.
Validate new channels or tactics (e.g., a new Meta bid strategy) before scaling. Use the results of your lift tests to calibrate targets for the future.
Test if you should recalibrate ROAS/iROAS targets when you suspect diminishing returns. Set incrementality tests to re-evaluate even your most established channels and campaigns.
Diagnose performance fatigue and decide where to reallocate. Confirm whether a channel’s incremental value still justifies budget.
Quantify halo across Amazon/retail/offline so you don’t underfund demand creation that doesn’t show up fully in clicks-only reads.
Plan monthly budgets with confidence by reconciling lift with daily MTA and MMM to avoid analysis paralysis.
How it fits your stack
Because Incrementality lives inside Northbeam, incrementality becomes part of your day‑to‑day operating system.
Alongside C+DV‑enhanced MTA and MMM forecasts, incrementality’s causal truth can help inform both tactical day-to-day bidding and long‑term budget.
Since all three analyses are in the same tool, operating on the same data, you never have to worry about conflicting perspectives or challenges incorporating one analysis into another.
Get Incrementality today
Incrementality by Northbeam launches in Q1 2026. However, we’re putting together a cohort of founding members.
Request a demo here to chat with our team about incrementality.
We are offering founding member bonuses for a limited time. Advertisers who join now can lock in 50% off unlimited tests for an entire year.
Incrementality by Northbeam turns lift testing into an operational advantage: automated design and execution, rigorous validation, clear readouts, and a direct line into MTA and MMM so you can act daily on causal truth.
Marketing automation is supposed to make everything easier: fewer manual emails, more consistent follow-ups, smoother nurture flows.
But once a drip campaign is launched, it often becomes a black box. Is it working? Which emails actually matter? Is it driving pipeline or just sending emails into the void?
Without clear reporting, you’re guessing. And guessing is expensive.
Modern growth teams need visibility, attribution, and proof, not just consistent activity. Reporting on your drip campaigns is the only way to know whether your automations are generating real business value.
This article walks through reporting on marketing automation performance and providing the ROI of drip email campaigns.
You’ll learn which metrics matter (engagement, conversion, revenue, attribution, efficiency), how to build a reporting framework and dashboard, how to connect drip activity to real conversions, and how to optimize sequences based on data.
You’ll also see a sample case study of how better reporting can improve conversion rates and ROI. By the end, you’ll have clear steps you can take to audit, measure, and improve your automated drip campaigns.
Why Reporting Matters for Drip Campaigns
Marketing automation is powerful. It saves time, scales your outreach, and ensures every lead gets the right message at the right moment.
But automation alone doesn’t guarantee impact. Without strong reporting, you have no way of knowing which sequences are actually moving people through the funnel and which ones are just running in the background.
Good marketing automation reporting turns drip campaigns from “set it and forget it” workflows into measurable growth levers.
A major reason reporting matters is attribution. Drip campaigns rarely operate in isolation; they touch the same users who are seeing ads, visiting your website, talking to sales, or signing up for demos.
If your reporting can’t connect drip activity to downstream conversions and revenue, you’ll never truly understand the incremental value your automations are generating.
Linking enrollment, engagement, and follow-through actions back to revenue outcomes is how you move from email metrics to business metrics.
There’s also a growing business expectation: stakeholders want proof of performance.
“We sent five emails” isn’t enough anymore. Leadership teams want to see which flows influence pipeline, shorten time-to-value, or reduce manual follow-up.
Yet research shows that only approximately 10% of marketers said they could effectively measure program impact. That’s the difference between teams who can defend their automation investment and teams who can’t.
Strong reporting closes that gap. It gives you visibility, attribution clarity, and credibility, the three ingredients that turn drip campaigns into provable ROI engines.
Defining the Reportable Metrics for Drip Campaigns
Before you can prove the impact of your drip campaigns, you need clarity on which metrics actually matter.
Strong reporting frameworks blend engagement, conversion, revenue, attribution, and efficiency indicators into a single view that shows not just what users did, but how that activity influenced the business.
Engagement Metrics
Engagement metrics like open rate, click-through rate (CTR), and interactions with individual steps in the sequence help you understand whether the content resonates and where users fall off.
These metrics diagnose friction and message relevance, serving as early signals of sequence health.
Conversion Metrics
Conversion metrics translate engagement into outcomes: lead-to-MQL progression, MQL-to-SQL movement, demo requests, purchases, or any downstream action directly tied to the drip.
These metrics reveal whether the sequence actually influences pipeline, not just inbox behavior.
Revenue & Cost Metrics
To quantify ROI, you need a clear view of both sides of the equation: the cost of the campaign (platform spend, content creation, staffing) and the incremental revenue it generates.
Without these metrics, reporting risks overemphasizing activity rather than profitability.
Attribution & Incrementality Metrics
Drip campaigns sit inside complex, multi-touch customer journeys.
Drip campaign attribution and reporting best practices (supported by tracking parameters, unique identifiers, and closed-loop reporting) help isolate the drip’s actual contribution to conversions.
Incrementality testing (such as holdout groups) strengthens confidence that results didn’t happen by chance or via other channels.
Efficiency & Time Metrics
Efficiency metrics capture how automation improves operations. Think time-to-conversion, reductions in manual follow-ups, and the total scale of users enrolled in the sequence.
These metrics show the internal value of automation, not just the external performance.
Across all categories, the golden rule is this: your reporting must ultimately roll up to business outcomes.
CAC, LTV, and ROI are the numbers stakeholders care about. The more directly your metrics connect to these outcomes, the stronger your case for continued investment in marketing automation.
Building a Reporting Framework & Dashboard for Your Drip Campaigns
A strong reporting foundation turns your drip campaigns from opaque workflows into measurable, optimizable growth assets.
These are the core components your framework needs:
1. Map Campaign Flows and Touchpoints
Start by visualizing the full journey: where the drip sits in the funnel, what triggers entry, and which actions you expect users to take.
Clear mapping ensures your reporting aligns with the actual user path and helps you understand which moments matter most.
2. Set Up Attribution Linking
Connect drip enrollments to downstream behaviors like ad clicks, site visits, demo requests, and purchases.
Attribution depends on clean tracking (unique identifiers, synced platforms, structured tagging, etc.) so you can isolate the drip’s influence instead of losing it in the noise of multichannel activity.
3. Define Baseline and Control Groups
To measure true incrementality, build a control cohort that does not enter the sequence.
Comparing performance between exposed and unexposed groups allows you to attribute lift confidently, instead of guessing whether a conversion would have happened anyway.
4. Select Key Visualizations
Strong dashboards rely on visual clarity. Include funnel drop-off charts (enrollment → email 1 → email 2 → conversion), revenue-over-time by cohort, cost-versus-revenue curves, and efficiency indicators like time-to-conversion.
These visuals quickly show what’s working and where users fall away.
5. Automate Data Flows
Centralize data from your CRM, email automation platform, analytics tools, and attribution system.
Automated pipelines ensure your reporting stays timely, accurate, and consistent; no more manual CSV exports or out-of-date dashboards.
6. Build Stakeholder-Ready Reports
Finally, translate your metrics into insights. Stakeholder-facing reports should include segmented views (by audience, channel, or campaign), KPIs tied to business outcomes, and a simple narrative: what worked, why it worked, and what you’re doing next.
This is where reporting becomes operationally useful, not just informational.
Ensuring Drip Campaigns Deliver ROI & Attribution Clarity
To prove the value of your automated sequences, your drips need more than good content. They need to be engineered for measurable performance from the start.
That means clear triggers, thoughtful segmentation, consistent follow-ups, and reporting systems designed to track the full economic impact of every touch.
Make sure to follow these best practices:
Set Up Drip Campaigns With Performance in Mind
High-performing drips start with strong architecture. Define who enters the sequence, why they enter, and what actions you expect them to take.
Use segmentation to ensure the right users get the right message at the right time, and build timely follow-ups that encourage progression instead of passive engagement.
When the structure is intentional, the reporting becomes meaningful.
Attribute Conversions in Multi-Touch Journeys
In reality, users interact with ads, pages, emails, and sales outreach long before they convert, which means attribution rules matter.
Decide whether you’ll use first-touch, last-touch, linear, or weighted multi-touch models, and make sure your system recognizes drip events as legitimate touchpoints.
Proper tagging and consistent identifiers keep your drips from disappearing in attribution black holes.
Compare Cost vs. Benefit, Not Just Engagement
Opens and CTRs can make a campaign look successful, but they don’t prove ROI. What matters is the incremental revenue generated by the drip compared to the cost of building, running, and maintaining it.
Calculating ROI forces you to evaluate whether the sequence is an essential growth engine or simply an automated expense.
Build Continuous Optimization Loops
Strong reporting isn’t static, it’s a feedback system. Use your dashboard to identify where users drop off, which segments underperform, and which emails fail to convert.
Then iterate: adjust content, tweak timing, refine targeting, or split test your highest-friction steps. Continuous optimization keeps the drip aligned with user behavior and business goals.
Account for Cross-Channel Interactions
Drip campaigns rarely operate alone. A user might click an ad, join a drip, revisit your site, and schedule a demo all within the same journey.
Your reporting should reflect this reality. Map how paid media, website activity, and lifecycle messaging interact, and ensure your attribution model credits the drip for the role it plays in driving users toward conversion, not just the final click.
Challenges & Pitfalls in Reporting Drip Campaign Performance
Even the most sophisticated automation programs run into obstacles when it comes to capturing accurate, actionable performance data.
Here are some of the most common challenges teams face and why they matter:
Data Silos and Disconnected Systems
Your email platform, CRM, analytics tools, and attribution software don’t always communicate cleanly. When data sits in separate systems, it becomes difficult to stitch together a full-funnel view of how users move from drip engagement to conversion.
This disconnect often leads to incomplete reporting or misleading conclusions.
Attribution Complexity
Drip campaigns rarely operate in isolation. Isolating the incremental value of a drip campaign in this multichannel environment is inherently tricky, especially without clear attribution rules and consistent identifiers.
Difficulty Measuring Long-Tail or Indirect Impact
Not every drip exists to drive an immediate purchase. Some sequences influence retention, expansion, onboarding success, or long-term product adoption.
These outcomes can be harder to quantify, and they don’t always map neatly to traditional ROI formulas, which creates a risk of undervaluing high-impact lifecycle flows.
Over-Focus on Soft Metrics
Open rates and click-through rates can provide useful directional insights, but they don’t prove business impact. Teams that rely too heavily on soft metrics risk optimizing emails instead of optimizing revenue.
Manual Reporting and Stale Data
When reporting requires manual exports, spreadsheet cleanup, or piecing together metrics from multiple tools, insights arrive too slowly to guide real optimization.
Delayed or inconsistent data means missed opportunities to fix drop-offs, refine segmentation, or improve timing.
Implementation Cost vs. Perceived Value
Robust reporting requires tracking, integrations, and aligned data structures, all of which take time and resources. Teams may hesitate to invest in this infrastructure unless the value is clear.
Without adequate setup, though, automation programs often underperform simply because their impact isn’t visible.
Example Case Study: B2B SaaS Company
To illustrate how effective reporting transforms drip campaigns, let’s look at a sample scenario from a B2B SaaS company running a 14-day product trial.
The team built a welcome-drip sequence triggered at signup: a five-email flow designed to guide new users toward activation milestones and, ultimately, a demo request.
Once the sequence had been live for a few weeks, the reporting dashboard surfaced a clear issue: a steep drop-off between enrollment and the second email.
Most users opened the first message but ignored the follow-ups, which meant they weren’t progressing toward activation.
By layering in segmentation (separating enterprise prospects from smaller teams), and adjusting the timing of the second and third emails, the team was able to deliver more relevant content to each group at the right time.
Attribution also played a critical role here. By tagging every email interaction with a unique user ID and syncing activity into the CRM, the team connected drip engagement to downstream actions like demo requests and closed deals.
A multi-touch attribution model clarified the drip’s role in the broader journey, showing that users who completed at least three emails converted at a meaningfully higher rate.
The impact of this tightening-up process was immediate. After the segmentation and timing updates, the conversion rate from trial to demo increased by 30 percent among users who entered the drip.
The improved sequence also delivered stronger economics: the campaign’s cost-to-revenue ratio improved by 25 percent once optimization efforts kicked in.
A glance at the reporting dashboard told the story visually. Funnel drop-off charts revealed where attention declined. A cost-versus-revenue graph illustrated the financial lift over time. A heatmap of segment performance showed which audiences responded best, helping the team prioritize future improvements.
This example shows how a well-instrumented drip campaign becomes a measurable growth lever, and how the right reporting framework can surface the insights needed to unlock real ROI.
Actionable Next Steps for Your Team
Once you’ve built a clear reporting foundation, the final step is putting it into practice.
This checklist can help you operationalize your insights, improve drip campaign performance metrics quickly, and make drip campaigns a measurable contributor to growth:
Audit your current drip reporting setup: List every active sequence, its data sources, the metrics you track today, how attribution works, and whether you have an up-to-date dashboard.
Choose one drip to improve and map its attribution path: Ensure you can connect enrollment → engagement → action → revenue in your analytics or attribution system. If any link in that chain is missing, fix it.
Calculate ROI for at least one existing campaign: Define the campaign’s full cost (platform, content, staffing), measure its incremental revenue, and compute ROI using (Revenue – Cost) / Cost. This becomes your baseline.
Create or refine a fully automated dashboard: Set it to update without manual intervention and surface the essentials: enrollments, drop-offs, conversions, revenue, ROI, and segment-level differences.
Establish a recurring optimization cycle: Add a weekly or bi-weekly review to evaluate performance, identify drop-offs, run experiments, and continuously refine content, timing, and segmentation.
Share results with stakeholders in a business-focused format: Highlight pipeline influence, revenue lift, and efficiency gains, not just email engagement. Keep the narrative concise and tied directly to business impact.
Bringing It All Together
Strong drip campaigns need to perform, and the only way to prove that performance is through clear, consistent reporting.
When you track the right metrics, build attribution into your workflows, and use dashboards to surface what’s actually driving conversions, your automations become powerful and measurable rather than background noise.
By investing in thoughtful reporting now, you give your team the clarity to optimize confidently, the credibility to demonstrate ROI, and the insight to scale what works.
If you haven’t audited your automations in a while, now is the perfect time to start.
Marketing funnels are more complex than ever, and manual workflows can’t always keep up with how people move between channels and touchpoints.
Automation offers a way to guide prospects through the journey with consistent, timely interactions while generating the structured data you need to measure what works and scale efficiently.
This guide walks through how to automate your marketing funnel for scale in order to give you a practical framework you can use immediately, whether you’re building a new automated funnel or improving one already in place.
Why Automate Your Marketing Funnel & What It Enables
Marketing funnel automation strategy involves using tools and workflows to move prospects from awareness to retention with as little manual work as possible while still giving each person a personalized experience.
The core benefit is efficiency. An automated marketing funnel supports growing demand without requiring your team to scale at the same pace.
It handles repetitive tasks, routes leads based on rules, and delivers nurture messages automatically. That frees your team to focus on strategy, testing, and high value interactions.
Automation also brings consistency and faster response times. When workflows trigger based on behavior, there is no lag between a prospect taking an action and your system responding. The result is a smoother customer experience and fewer missed opportunities.
Finally, automated engagement funnels for paid media and attribution also improve tracking and clarity. Automated funnels generate reliable, structured touchpoints that feed into your measurement stack. Every form submit, nurture step, retargeting trigger, and workflow event is captured and tied to downstream conversions.
For teams using attribution or multi touch modeling, this level of completeness matters. It provides a more accurate picture of how users move through the funnel, what actually influences conversion, and where to invest next.
Mapping the Funnel & Identifying Automation Opportunities
A well-automated funnel starts with a clear understanding of how people move from first touch to long-term engagement.
Map Your Core Funnel Stages
Start by outlining the major steps in your marketing journey. A common structure might be: Paid Ad, Lead Capture, Nurture Sequence, Conversion, and Retention.
Define Behaviors and Triggers at Each Stage
Identify which meaningful actions happen within each stage. Examples include ad clicks, form submissions, content downloads, email opens, and product page views.
Add entry and exit criteria so you know when someone has truly progressed. Micro conversions like scroll depth or repeat visits help you understand intent.
Identify What Can Be Automated Immediately
Pinpoint the workflows that can run without human involvement. For example:
Lead capture and qualification rules can send new contacts straight to your CRM
Nurture sequences can trigger based on behavior and deliver timely email, in-app, or retargeting messages
Cross-channel retargeting can reflect real funnel movement or drop off patterns.
Identify Where Humans Still Add Value
Not every step benefits from automation. High intent demos, complex outreach, or sales conversations require context and judgment. Mark these moments clearly so automation supports your team rather than replaces essential human touch.
Align Automation to the Full Customer Journey
Once stages, triggers, and responsibilities are in place, ensure your systems pass users smoothly from one step to the next.
Automated actions should set up human interactions, and human interactions should feed back into automated tracking. This keeps your funnel coherent, measurable, and ready for scale.
Building the Automation Workflow & Measurement Plan
Once your funnel stages are clear, the next step is building an automation system that responds to real user behavior and produces reliable, measurable data.
Choose the Right Tools and Integrations
Start by selecting the platforms that will power your automated funnel. Most teams rely on a marketing automation platform, CRM, ad platforms, analytics tools, and an attribution system.
Design Clear Workflow Logic
Define the rules that determine how users move through your automation:
Set behavior triggers like page visits, ad clicks, or content downloads
Add segmentation and branching rules that adjust messages based on source, engagement, or lead value
Map out automated messaging and retargeting sequences
Establish lead scoring thresholds so high intent users can be handed to sales at the right moment
Instrument Your Funnel Events
List the key events that matter for both automation and attribution. This usually includes impressions, clicks, form submits, nurture engagements, and conversions. Ensure each event is captured in a consistent, structured way so it can power both your workflows and your reporting.
Align Automation with Your Attribution Model
Connect each workflow step to your attribution system. This allows you to trace a user from their first touch through the automated journey and view the full impact of your funnel on pipeline and revenue.
Build Dashboards for Visibility
Create dashboards that highlight funnel flow, drop off points, workflow engagement, lead progression, conversion rates, and ROAS. These help you monitor performance at a glance.
Plan for Continuous Optimization
Schedule regular reviews to identify bottlenecks, test timing or sequencing changes, and compare variations of nurture content. Your automation should evolve based on data, not remain static.
Scaling Engagement With Marketing Funnel Automation Without Losing Personalization
Here’s how to expand your reach without sacrificing the personal touch that moves users through the funnel.
Segment Your Audience With Intention
Start by defining meaningful segments based on behavior, source, needs, or lifecycle stage. Tailor each workflow to the user’s context so they receive messages that reflect what they care about, not a generic sequence.
Use Behavior-Based Triggers and Dynamic Content
Trigger workflows based on real actions like repeat visits, content engagement, or product exploration. Dynamic content lets you adjust messaging automatically, showing different offers or creative to users based on their past behavior.
Orchestrate Cross Channel Automation
Automate touchpoints across ads, email, in app interactions, and SMS so that each channel reinforces the others rather than competing.
When a user downloads a guide, for example, they might receive a follow up email, and see aligned retargeting ads. The experience should feel coordinated, not chaotic.
Automate Retention and Upsell Flows
Personalization should continue after conversion. Build sequences for onboarding, loyalty rewards, engagement automation, and upsell opportunities. These workflows help you extract more lifetime value and keep users active.
Monitor and Refresh Content to Avoid Fatigue
At scale, even strong sequences can start to lose impact. Watch for falling open rates, rising unsubscribes, or declining conversion rates. Refresh creative, rotate offers, and update triggers regularly so your automated funnel stays relevant.
Attribution & Performance Monitoring in an Automated Funnel Context
Automation adds scale and consistency to your funnel, but it also creates more touchpoints that must be tracked accurately.
Strong performance monitoring ensures you know which automated steps are driving real results, not just activity.
Capture Every Automated Interaction
Make sure your attribution system records all relevant events, including automated nurtures, retargeting impressions, message sends, and workflow steps.
Define Micro Conversions and Link Them to Outcomes
Identify the moments that signal meaningful progression, such as completing a nurture step, clicking a key CTA, or returning to your site.
Map these micro conversions to downstream outcomes like trial starts, demo requests, or purchases. This helps you understand not just who converts, but how they convert.
Use Holdouts to Prove Incremental Lift
Automation can create correlation without real causal impact. Introduce holdout or control groups that do not receive a specific workflow or message. Compare performance to determine whether your automation is truly adding lift or simply engaging users who would have converted anyway.
Measure Incremental ROAS Across the Funnel
Track improvements in cost per lead, conversion rate, and time to conversion for users who move through automated flows. Tie these gains to spend and revenue so you can quantify how automation affects return on ad spend and overall pipeline efficiency.
Monitor for Anomalies or Workflow Misalignment
High engagement but low conversion often signals an issue. You may have an ineffective message, a confusing branching path, or a retargeting loop that keeps users stuck.
Build automated reporting that flags these anomalies early so you can adjust before your funnel performance suffers.
Common Pitfalls & Automation Risks
Automation can accelerate your funnel, but it also introduces new risks.
Being aware of common pitfalls helps you build systems that scale cleanly rather than create more problems to solve:
Avoid Irrelevant or Poorly Segmented Flows
Automation only works when it speaks to the right people. Broad or mismatched segments lead to low engagement, higher drop offs, and unsubscribes.
Review your logic regularly to ensure each workflow matches user intent and lifecycle stage.
Balance Automation With Human Context
Too much automation can feel cold or scripted. Prospects will notice if every message feels templated.
Identify the moments where human judgment matters, such as high intent outreach or complex questions, and ensure workflows hand off gracefully to real people.
Connect Automation to Your Attribution Stack
If automated events are not tracked, you lose visibility into what actually drives conversions. This turns optimization into guesswork.
Make sure every workflow step is captured as a touchpoint so your attribution model can reflect the real user journey.
Break Down Data and System Silos
Disconnected tools create blind spots in your funnel. When your CRM, automation platform, analytics tools, and ad channels don’t share data, you can’t see where users are progressing or getting stuck. Integrations keep your workflows cohesive and your reporting accurate.
Monitor Scale Before Problems Grow
As you scale traffic and automation, small misalignments can become expensive quickly. Watch for rising CPL, stagnant conversion rates, or sudden drop offs that signal bottlenecks.
Refresh Content to Prevent Fatigue
Automated sequences lose effectiveness over time. Declining open rates or engagement indicate that messaging has gone stale.
Update creative, rotate offers, and adjust timing to keep your funnel feeling fresh and relevant.
Case Study: How a DTC Brand Scaled Engagement With An Automated Funnel
A DTC brand wanted to improve conversions from its paid social campaigns without adding more manual work.
They promoted a free downloadable guide as their main acquisition offer, and every download fed into a new automated workflow that combined a short email sequence, targeted retargeting, and a limited time purchase incentive.
Each step in the journey was tracked as a structured event. This gave the team a clear view into how users moved through the funnel and which touchpoints actually influenced conversions.
Leads who entered the automated workflow converted at twice the rate of standard leads and cost 30 percent less to acquire, since nurtures and retargeting were triggered based on real behavior.
Because workflow events were synced with their attribution model, the team could see which ad channels delivered the most engaged leads.
They reallocated budget toward audiences that consistently fed high quality traffic into the workflow and improved ROAS without increasing spend.
As volume grew, they expanded the automation into a simple multi-channel sequence using ads, email, and SMS.
The larger dataset revealed a bottleneck in the nurture journey, where many users dropped off around the third email.After testing new timing and creative, they recovered a meaningful share of those lost conversions.
The combination of personalized automation and attribution visibility gave the brand a scalable, measurable path to growth, and a framework they could continue improving as they expanded.
Actionable Next Steps for Your Team
If you want to build a funnel that scales consistently and improves over time, start with a few high-impact steps:
1. Map Your Current Funnel
Outline each stage and the key behaviors within it.
Mark where automation already exists.
Identify manual gaps that create delays or inconsistent follow up.
2. Improve One Funnel Segment First
Choose a narrow portion of the journey, such as lead capture to nurture.
Define its behavior triggers, messaging sequences, and exit criteria.
Clarify the metrics that matter so you can measure progress.
3. Connect the Workflow to Your Attribution Stack
Ensure every automated event (form submit, nurture step, retargeting trigger) is recorded as a touchpoint.
Create a dashboard that shows workflow engagement, drop offs, conversion rates, and ROAS.
4. Expand Automation Across Channels
Add coordinated touchpoints across ads, email, in app channels, and SMS.
Make sure users experience a consistent journey even when switching devices or platforms.
Track how each channel contributes to movement through the funnel.
5. Set a Regular Review Cadence
Review automation performance monthly.
Refresh creative, adjust timing, and test new variations.
Scale what works and retire what does not.
6. Share Results With Stakeholders
Communicate outcomes in business terms, not just engagement metrics.
Highlight conversion improvements, cost per acquisition changes, and overall funnel velocity.
By improving one part of the funnel at a time and aligning automation with your attribution model, you create a system that gets smarter with every cycle.
Marketers love tidy numbers, and few are tidier than a single, blended ROAS.
But if growth is the goal, blending acquisition and retention into one metric is exactly how you end up scaling the wrong things.
In my work in media strategy, the simple truth is this: the lines that grow your business are the lines that grow new customers, and the only way to hold yourself accountable to that is to optimize to New Customer ROAS (nROAS) and New Customer CAC (nCAC) every day.
To see the full webinar I did on optimizing your ads, click the video below:
Defining the Metrics
Let’s get precise. Blended ROAS is total revenue divided by total spend; useful for broad reporting, but it’s blind to mix quality.
New Customer ROAS (nROAS) is new customer revenue divided by spend; the cleanest lens for whether your dollars are generating net-new buyers.
Why does the distinction matter?
Because blended ROAS often looks great when returning customers are carrying your dashboard.
You can show “profitability” while your top-of-funnel is starving. I showed this live in our webinar: in a real account, Meta read 0.36 ROAS in our Northbeam view while the platform reported 2.7 — not because one tool is “wrong,” but because a multi-touch, acquisition-first lens shows contribution differently and surfaces whether those dollars actually created new demand.
Why nROAS Drives Growth
Northbeam is built to help teams scale new customer acquisition; full stop.
Retention absolutely matters, but acquisition comes first; you can’t retain a customer you never reached, and you can’t compound LTV if your pipeline of first purchases dries up.
That’s why I call nROAS + nCAC the “golden metrics” for decisioning — they anchor your daily calls on profitability and growth velocity, not vanity efficiency.
Operationally, our daily framework steers budget on nROAS/nCAC at the campaign and ad set levels, then uses supporting metrics (CTR, CPM, eCPC, conversion health, RPV) to diagnose bottlenecks after the scale/trim call.
This keeps acquisition front and center and prevents the classic mistake of falling in love with retargeting math that doesn’t expand the pie.
The Blended-ROAS Traps I See Most
Blended ROAS isn’t “bad” — it’s just insufficient for growth decisions. Here are the pitfalls it hides in plain sight:
You scale campaigns that mostly convert returning customers. Blended looks green; New Customer% tells you the truth about mix quality and whether you’re actually growing the base.
You misread lower-funnel channels as demand drivers. In acquisition views, our Clicks-Only model intentionally removes branded search, email, SMS, and direct from taking credit they didn’t create — those touchpoints capture demand, they don’t typically generate it.
You think you’re “profitable,” but you’re underinvesting in prospecting. Retargeting lines often plateau on volume. Prospecting is the growth engine; resource it accordingly and measure with nROAS and New Customer% to keep yourself honest.
You trust siloed platform ROAS over a multi-touch source of truth. Platforms over-credit themselves by design; you need an omnichannel view that fractionalizes credit across touchpoints to understand true contribution to new customer growth.
How I Operationalize nROAS (Daily)
Here’s how my team runs this in practice — simple, fast, and repeatable.
Start with the primary decision: Are we at/above our nROAS target or under our nCAC ceiling with sufficient data to trust the signal? If yes, scale with intent; if no, trim and fix.
Keep everything in a standardized decision window (I like 1D Clicks-Only for fast reads; use 7D CO for week-level calls) so benchmarks, stoplights, and budget moves all speak the same language in accrual accounting — which aligns revenue to the touchpoint date and avoids mid-week bias toward older assets.
Then, use your tools to compress time-to-decision. Benchmarks & Stoplights turn profitable-day profiles into green/yellow/red signals at every level (channel → campaign → ad set → ad).
Crucially, evaluate first-time (new customer) performance separately from blended so you don’t confuse retention wins for acquisition momentum.
Finally, diagnose precisely and ship a fix:
Weak nROAS, high nCAC, low New Customer% → your mix is drifting to existing customers. Tighten audiences/creative to reach net-new or reallocate toward prospecting-heavy channels that historically lift New Customer% (measure the lift, not the vibes).
Good clicks but low new customer conversion → fix ad-to-landing-page message match. The psychology of the click should meet the page; when it doesn’t, qualified traffic bounces and your nROAS suffers.
Strong new customer conversion but low RPV → improve the offer. Bundles, cross-sells, post-purchase offers, or price tests can lift order economics without sacrificing acquisition quality.
Build saved views for your roles with tiles for nROAS, nCAC, New Customer%, CTR, CPM, ECR, and RPV so everyone (buyers, leads, execs) sees the same signals and acts in the same window. Source of truth matters at scale; consistency compounds.
Conclusion
Growth is a new-customer game. If you optimize to nROAS and nCAC, standardize your decision window, and let Benchmarks & Stoplights guide your daily moves, you’ll scale the lines that actually expand your customer base — and you’ll stop confusing blended efficiency with durable growth.
To watch the full webinar, click here. Additionally, you can download our handy guide to keep at your side for when you're optimizing your campaigns, straight from the playbook our media strategists run. Download it for free here.
In an era where marketers juggle a dozen platforms before breakfast, choosing the right marketing channels has never mattered more.
The best marketing channel strategy isn’t about chasing trends, it’s about connecting with your audience efficiently, maximizing ROI, and aligning every campaign with a clear purpose.
Without a structured approach, channels overlap, results blur, and performance stalls. The key is to be selective, strategic, and data-driven, building a channel mix that supports growth.
This article walks you through how to choose the right marketing channels by defining your audience, evaluating each option by ROI and scalability, and using data to prioritize what works.
You’ll learn how to audit your current mix, test new channels effectively, and avoid common pitfalls like overlap, saturation, and attribution distortion so your marketing strategy becomes more focused, efficient, and impactful.
The Problem: Why Channel Strategy Often Fails
Many teams still treat marketing channels like a buffet: piling on whatever looks appealing without considering how it all fits together.
Budgets get scattered across platforms, results flatten, and leaders are left wondering which effort actually moved the needle.
When channels overlap, costs rise and attribution gets fuzzy. One campaign cannibalizes another, and performance metrics stop telling a coherent story. The result isn’t a growth engine; it’s noise.
A solid marketing channel strategy brings order to that chaos. It aligns every touchpoint with clear objectives (awareness, acquisition, retention) and ensures each dollar supports a measurable outcome rather than a hunch.
87% of marketing leaders report experiencing campaign performance issues, with poor channel fragmentation and other channel strategy issues being the primary driver.
In other words: it’s not about being everywhere. It’s about being strategic about where and why you show up.
Core Decision Criteria for Channel Selection
A disciplined channel strategy starts with clear evaluation criteria. Each potential channel should be measured against consistent axes, not just gut instinct or trends.
The goal is to understand where your audience actually engages, what outcomes the channel supports, and whether it’s sustainable to scale.
Audience Fit & Presence
The best-performing channels are the ones your audience already lives in.
Before investing, confirm where your target customers spend time and what content formats they respond to: short-form video, newsletters, long-form education, etc.
Funnel Stage & Channel Role
Every channel should serve a defined purpose: building awareness, driving acquisition, or nurturing retention.
Mapping this alignment ensures you’re not running top-of-funnel tactics where conversion is the goal, or vice versa.
Cost vs. Scalability
Evaluate each channel’s economics over time. Some channels deliver strong ROI early but taper off as competition rises or bids inflate.
Assess both upfront costs (setup, creative, tech) and marginal costs (CPC, CPM) to gauge long-term scalability without diminishing returns.
Resources & Capability
Even high-potential channels fail without proper execution. Do you have the in-house skill sets (copywriting, design, ad operations, data analysis) to manage the channel effectively?
If not, factor in outsourcing costs or training time before committing.
Measurement & Attribution Feasibility
If a channel can’t be reliably measured, it can’t be optimized.
Make sure you know how to choose marketing channels that integrate smoothly into your analytics and attribution models, so you can connect spend to results and continuously refine performance.
Differentiation & Saturation
Overcrowded platforms drive higher costs and lower impact. Look for channels where your competitors are not dominant; emerging or underutilized spaces often yield better ROI and creative opportunities.
Agility & Flexibility
Markets shift fast. Channels that allow you to pivot quickly (test creative variations, adjust bids, or redeploy spend) offer resilience in volatile conditions.
Agility often beats perfection in modern marketing.
Channel Types & Their Roles
Before mapping specific channels to your strategy, it helps to understand the broader framework they fall into.
Keen Decision Systems divides marketing channels into four major categories, each with different strengths and tradeoffs:
Paid channels offer speed and scalability but require constant optimization as costs rise.
Organic channels build equity over time but demand patience and consistent effort.
Digital channels provide agility, targeting, and real-time feedback.
Traditional channels like print, radio, and direct mail reach broad audiences but are harder to measure precisely.
These dimensions highlight key tradeoffs: speed versus sustainability, reach versus control, experimentation versus consistency.
The most effective strategies blend these forces rather than relying too heavily on one.
Once you’ve defined your evaluation criteria, the next step is mapping them to the major channel types within this framework.
Owned/Organic (Blog, Social Media, SEO)
Owned channels are the long game. They require time and consistency but build durable brand equity and compound over months or years.
Blogs, social media profiles, and SEO-optimized content give you full creative control and low incremental cost once established.
They’re ideal for awareness and education but demand patience before measurable ROI appears.
Paid/Performance (PPC, Ads, Sponsored Content)
Paid channels deliver speed and precision. They’re easy to test, quick to scale, and provide fast feedback loops, which is perfect for validating new offers or audiences.
The tradeoff: rising competition and cost volatility. As performance channels saturate, ROI can erode without constant optimization.
Earned exposure adds credibility and reach you can’t buy directly. Whether through PR mentions, influencer collaborations, or partner syndication, these channels extend your visibility to new audiences and reinforce trust.
However, they’re less predictable and harder to measure, which means they work best alongside more controllable tactics.
Direct channels nurture retention and loyalty. They allow you to communicate with customers without intermediaries or algorithms, giving full control over timing and message.
Direct channels also present high potential for personalization, especially when integrated with CRM data.
These channels blur the lines between organic and paid. They’re often niche, with lower competition and high creative potential, but require experimentation to find traction.
Podcasts, affiliate programs, and live events can build deep audience engagement if aligned with your brand voice and goals.
Keen’s framework divides channels into paid, organic, digital, and traditional categories to emphasize their different tradeoffs: speed versus sustainability, reach versus control, experimentation versus consistency.
The most effective strategies blend these dimensions rather than relying too heavily on one.
Framework for Building Your Channel Strategy
A channel strategy isn’t a one-time plan, it’s an iterative system for testing, learning, and scaling what works.
The process moves from exploration to execution through a disciplined set of steps that keep decisions data-driven and aligned with business goals.
Audit Existing Channel Performance
Start by taking inventory. Gather data on each active channel’s cost, reach, conversion rate, and incremental value, not just vanity metrics.
Look for overlap between channels (e.g., paid social clicks converting customers already reached via email) and identify attribution blind spots.
This snapshot becomes your baseline for smarter investment.
Hypothesis Scoring & Ranking
Treat each potential channel as an experiment. Score it against your decision criteria, including audience fit, ROI potential, risk level, and resource requirements.
The goal isn’t to predict the future perfectly but to rank channels by the strength of their hypotheses. This keeps you focused on the opportunities with the highest strategic upside.
Pilot & Test Approach
Before you commit to a given budget, validate your assumptions through small-scale pilots.
Keen’s research emphasizes testing early and often: running controlled experiments to see how each channel performs in practice.
Use clear KPIs like cost per lead, engagement rate, or conversion lift to decide what deserves more spend.
Role Definition & Funnel Mapping
Once you’ve identified the most promising channels, assign them clear roles in your funnel. Which ones drive awareness? Which nurture conversion or retention?
Mapping these interactions helps prevent duplication and ensures every channel feeds into the next stage of your customer journey.
Attribution & Feedback Loops
Decide how success will be measured. Whether you use last-click, multi-touch, or marketing mix modeling (MMM), attribution should provide actionable insight rather than inflated numbers.
More advanced teams are turning to Bayesian models for a nuanced view of multi-channel influence. Build dashboards that update automatically so performance data continuously informs allocation.
Scale, Reallocate, and Iterate
Once your tests identify clear winners, double down on what’s working and pause what’s not. Revisit your mix quarterly to adjust for seasonality, cost shifts, or audience fatigue.
💡 What Is the “Bang-Bang” Strategy?
Adapted for marketing by Eshghi et al. (2017), the bang-bang strategy argues that optimal advertising rarely comes from steady, continuous effort.
Instead, it’s more effective to focus your spend in powerful bursts or waves, pushing one channel hard while pulling back on others, then rotating effort as performance saturates.
This dynamic approach maximizes impact while minimizing waste, preventing you from spreading budget thinly across too many platforms at once.
A strong channel strategy is alive, not static. It grows with your business, your data, and your audience, compounding insight and ROI over time.
Tradeoffs, Risks, & Channel Pitfalls
Even a well-planned channel strategy can stumble if the moving parts aren’t managed carefully. Most failures don’t come from bad ideas, they come from invisible tradeoffs, misaligned timing, or operational friction.
Understanding these pitfalls upfront can help you prevent costly detours.
Overlap & Cannibalization
Not every channel expands your reach. Sometimes two or more hit the same audience, doubling your spend without adding new prospects.
Overlapping audiences between, say, paid social and remarketing can drive up costs and muddy attribution.
Use audience exclusions and cohort analysis to ensure your channels complement, not compete.
Attribution Distortion
When success metrics give too much credit to one channel (for instance, last-click conversions inflating search ROI) teams can make the wrong optimization calls.
Attribution distortion leads to overinvestment in easy-to-measure channels while undervaluing long-term drivers like content or PR. Cross-channel models and data triangulation help correct the imbalance.
Saturation & Diminishing Returns
Paid channels are efficient until they’re not. As spend increases, competition drives up bids and ROI erodes.
Recognizing this curve early lets you shift from high-cost acquisition to nurturing and retention before performance flattens.
Resource Dilution
More channels don’t always mean more results. Spreading teams thin leads to sloppy execution, slow response times, and half-baked creative.
A lean, well-managed mix consistently outperforms an overloaded one. Focus on mastery, not mere presence.
Dependence on External Platforms
Platforms change faster than you can plan. Algorithm updates, privacy policies, or ad pricing shifts can instantly rewrite your playbook.
Balance external reliance with owned channels like email or SEO that you fully control.
Ramp Time Mismatch
Every channel operates on its own timeline. SEO and content marketing may take six months to mature, while paid ads can drive traffic in days.
Mismanaging these timelines (for example, expecting organic to deliver at launch) can create gaps in visibility or pressure on paid spend.
Fragmented Messaging & Silos
When different teams own different channels without coordination, brand voice fractures. Messaging feels inconsistent and customer experiences become disjointed.
Align strategy, creative, and reporting across departments to maintain one cohesive narrative.
A strong channel mix is as much about orchestration as selecting marketing channels. Avoiding these pitfalls ensures your efforts add up to momentum, not noise.
3 Examples For How to Choose the Right Marketing Channels
The best way to understand channel strategy is to see it in action.
Below are three simplified examples that show how different types of businesses apply the same process: hypothesis, test, learn, and adjust.
Example #1: SaaS Startup
A B2B software startup wanted to attract qualified leads for its free trial program.
The team hypothesized that LinkedIn Ads + content download + email nurture would yield the best conversion flow given their professional audience.
After a two-month pilot, they tracked cost per lead (CPL), engagement with gated content, and downstream trial activation.
LinkedIn drove high-quality leads but at a steep cost. However, email nurtures converted 22% of those trial users: a strong retention signal.
The team reallocated part of their ad budget toward organic LinkedIn content and lifecycle emails, improving total ROI by 35%.
Key takeaway: Start broad with paid reach, then shift investment toward owned or direct channels that sustain engagement.
Example #2: DTC Brand
A direct-to-consumer beauty brand launched a new product line and wanted to find which mix of channels would generate both awareness and repeat purchases.
They ran an influencer + paid social + retargeting campaign for six weeks.
Initial metrics showed strong engagement but weak conversion from Instagram ads. Retargeting audiences were small and expensive. By surveying new customers, they learned that many discovered the brand through influencer posts but converted later via email or SMS.
The team reduced social ad spend, doubled down on SMS and push notifications, and built a referral program.
Within one quarter, repeat purchase rate rose by 28% and CAC dropped by 18%.
Key takeaway: Attribution often hides the “real” driver; follow the customer journey beyond the first click.
Example #3: Local Business
A neighborhood fitness studio wanted to rebuild attendance after a renovation, but they’re not sure about the best marketing channels for small businesses.
Rather than rely solely on digital, they paired offline outreach (local events, direct mail) with digital discovery (paid search, local social ads).
Their hypothesis: combining personal community touchpoints with online visibility would amplify trust and bookings. Test metrics included event attendance, coupon redemption, and new membership signups.
The hybrid approach worked: local events converted 1 in 4 attendees, while social ads boosted awareness at minimal cost.
They sustained the model by running one event per quarter, supported by targeted Google Ads and a referral discount.
Key takeaway: Offline and online channels can reinforce each other, especially when community and credibility matter.
Across all three cases, the lesson is the same: an effective channel strategy framework in 2025 is based on data-informed experimentation.
Test assumptions, measure real outcomes, and adjust the mix based on evidence.
Channel Selection Checklist
A great channel strategy doesn’t just live in a slide deck, it’s something you can operationalize and refine over time.
Use this checklist to turn your evaluation into an actionable plan that keeps your marketing mix sharp and accountable:
Conduct a Channel Audit: Start with what’s already running. Review each channel’s spend, reach, and performance metrics to identify overlap, underperformance, and opportunities to consolidate.
Interview or Survey Your Customers: Go straight to the source. Ask your audience where they actually spend time and what types of content influence them most. Even a small sample of customer feedback can reveal blind spots in your assumptions.
Pilot 2-3 New Channels (with Clear Metrics): Instead of spreading thin, choose a few new channels to test deliberately. Define success metrics upfront (cost per lead, engagement rate, retention lift) and evaluate after a fixed test window.
Define the Funnel Role for Each Channel: Every channel should have a specific job to do. Label it clearly (awareness, acquisition, retention) so your mix stays balanced and your performance data stays interpretable.
Build or Choose an Attribution Framework/Dashboard: Select a measurement model (last-click, multi-touch, or MMM) that fits your scale and tech stack. Visualize data in a dashboard that lets you see trends, compare channels, and reallocate fast.
Schedule Quarterly Reviews: Set a recurring cadence to revisit channel performance. Markets, audiences, and costs shift, and your mix should, too. Re-run prioritization each quarter to cut what’s stale and double down on what’s scaling.
A disciplined checklist like this keeps your marketing channels from running on autopilot. Strategy isn’t about doing more; it’s about doing the right things consistently.
Build Smarter, Not Broader
A strong channel strategy isn’t about being everywhere at once. It’s about showing up where it counts with intention, focus, and measurable impact.
Start by auditing what’s working. Then test a few new channels with purpose, and use data to guide every adjustment.
Over time, the result isn’t just better ROI, it’s a marketing engine that compounds insight, efficiency, and trust.
Don’t wait for perfect information. Pick two actions from the checklist above and start this week.
If your ads used to crush performance but now barely get clicks, it’s probably not the algorithm’s fault: it’s ad fatigue.
Even the best creative gets old fast when audiences see it over and over again. Engagement drops, costs rise, and before you know it, you’re spending more to achieve less.
Ad fatigue happens when your audience becomes so familiar with a campaign that they stop noticing it altogether.
The result? Declining CTR, rising CPC, and wasted media spend.
Here’s what you’ll learn in this guide:
How to recognize ad fatigue: the key signals and metrics that reveal when performance is slipping
Why it happens: the most common creative, targeting, and algorithmic culprits
How to fix it fast: proven remedies and prevention tactics to keep campaigns performing longer
In short, this article helps marketers recognize, diagnose, and fix ad fatigue before it drains ROI. You’ll walk away with a clear playbook to keep your creative fresh, your audience engaged, and your campaigns cost-efficient.
What Is Ad Fatigue?
Ad fatigue (sometimes called creative fatigue or ad wear-out) happens when audiences are exposed to the same creative too often.
Over time, what was once engaging starts to feel repetitive or irrelevant, and viewers begin to scroll past, ignore, or even react negatively to your ads.
It’s important to distinguish ad fatigue from general underperformance. A campaign might underperform for reasons like poor targeting, weak messaging, or seasonality, but ad fatigue specifically refers to performance decline over time as a result of overexposure.
In other words, it’s not that your ad never worked. It just stopped working once people had seen it too many times.
This phenomenon can occur across nearly every digital channel: social media feeds, display networks, video platforms, and even retargeting campaigns.
The more often a user sees the same creative in these spaces, the less likely they are to engage with it, and the faster your results begin to drop.
Why Ad Fatigue Matters
Ad fatigue isn’t just a creative nuisance. It has real business consequences: your engagement drops, costs climb, efficiencies erode, and brand perception can take a hit.
Key risks include:
Decline in CTR / Engagement
When an audience sees the same ad too often, they stop clicking, reacting, or paying attention.
For example, a study by Meta Platforms found that after just four repetitions of the exact same creative, the likelihood of a click dropped by about 45%.
Rising CPC / CPM
When engagement falls, the advertising platforms’ algorithms recognize your creative as less relevant and inefficient, so the cost to reach or get action from users goes up.
Falling ROAS / Conversion Efficiency
With fewer clicks and higher costs, the return on your ad spend drops. Essentially, you spend more to get less.
Negative Audience Feedback
Repetition can annoy or alienate users. Comments like “I’ve seen this already”, ad hides, or users choosing “not interested” all signal damage. That in turn can harm how users feel about your brand.
Algorithmic Suppression of Stale Creative
Digging deeper, when your creative becomes stale (low engagement, negative feedback, high frequency), ad platforms will serve it less or bid it up in cost, reducing reach and efficiency further.
It’s tempting to think “we’ll refresh eventually,” but the data shows that damage accumulates fast.
The higher you let costs rise and engagement fall, the harder (and more expensive) it’ll be to recover from the slide.
In short: ignoring creative fatigue puts your campaign health, budget efficiency and brand standing at risk.
Recognizing Ad Fatigue (Signals & Metrics)
Ad fatigue doesn’t always announce itself loudly. It can creep in gradually through subtle performance shifts.
To catch it early, marketers should monitor both quantitative and qualitative signals that reveal when an audience has seen too much of the same creative.
Quantitative Indicators
Consistent drop in CTR or engagement rate: A steady decline in click-throughs or interactions is one of the clearest signs your audience is tuning out.
Rising CPC or CPM with a stable budget: When your costs climb but impressions or clicks don’t, platforms may be penalizing your ad for poor relevance or repetitive delivery.
Falling conversion rates or ROAS despite stable traffic: If your traffic remains steady but sales, sign-ups, or return on ad spend drop, fatigue may be eroding effectiveness.
High ad frequency: Seeing the same ad too many times leads to boredom and frustration. As a general rule, frequency above 3-4 impressions per user often signals it’s time to refresh.
Plateauing reach: When you’re no longer expanding to new audiences, it can indicate saturation within your target segment.
Qualitative Indicators
Negative audience feedback: Comments like “I’ve seen this ad too much” or increased use of “Hide this ad” options suggest irritation and declining sentiment.
Internal performance reviews: Feedback from creative, media, or account teams noting repeated visuals or stale messaging often correlates with performance dips.
Audience sentiment monitoring: Social listening tools or manual reviews can reveal when tone shifts from neutral to negative, an early warning that perception is slipping.
Together, these metrics and insights create an early detection system.
By watching for even small shifts in engagement or sentiment, teams can refresh creatives before fatigue causes serious performance loss.
Root Causes & Risk Factors of Ad Fatigue
Understanding the root causes of ad and creative fatigue helps prevent the problem before it drains your budget.
Overuse Of The Same Creative
Running a single ad or a limited set of variations for too long is the most common driver of fatigue. Even high-performing creative will eventually lose impact once it’s seen too many times.
Narrow Or Saturated Targeting
When your audience pool is too small, the same users are repeatedly served your ads, especially if you’re also retargeting them across platforms.
Expanding or rotating segments can help distribute impressions more evenly.
Infrequent Creative Refresh Cycles
Without a consistent refresh schedule for visuals, copy, or calls-to-action, fatigue is inevitable.
Overly aggressive bidding or missing frequency limits can lead to excessive impressions per user.
Implementing caps ensures users see your message enough to remember it, but not enough to get tired of it.
Cross-Channel Overlap
Delivering identical or similar creatives across multiple channels (e.g., Meta, YouTube, Google Display) can overwhelm the same audience.
Differentiating creative formats or sequencing them across channels helps prevent oversaturation.
Algorithmic Downranking
Ad platforms deprioritize creatives that get low engagement or negative feedback. Once this happens, reach declines and costs rise, creating a cycle of underperformance that’s hard to recover from.
Weak Creative Strategy Or Lack Of Testing
Without structured experimentation (like A/B testing, message rotation, or creative benchmarking) teams miss the early warning signs that an ad is wearing out.
A strong creative testing framework ensures you catch fatigue before performance drops.
How to Overcome Ad Fatigue: Remedies & Prevention Strategies
Once you’ve identified signs of fatigue, the next step is to act fast.
The key to preventing ad performance decline is proactive management: regularly refreshing creatives, rotating audiences, and automating monitoring to catch early dips before they hurt ROI.
Here are seven steps to prevent ad campaign fatigue across Facebook (Meta), Google, and other channels.
Creative Rotation & Refresh
Maintain multiple creative variants and refresh visuals, headlines, and CTAs on a regular schedule (every few weeks for always-on campaigns).
Use seasonal hooks, trending topics, or new product angles to keep messaging timely and relevant.
Experiment with alternate formats, such as short-form video, carousel, or interactive ads, to re-engage users who have grown tired of static visuals.
Frequency Capping / Exposure Control
Set limits on how many times an individual user sees your ad within a set period (e.g., 3–5 impressions per week).
Use platform-level frequency caps or automated rules to manage exposure across campaigns
Pause or rotate creatives once thresholds are reached to prevent overexposure and preserve brand sentiment.
Audience Segmentation & Exclusion
Break audiences into smaller, more precise segments and tailor creatives to each group’s interests or behaviors.
Exclude users who have already converted or have seen your ad multiple times
Gradually expand reach with lookalike or similar audiences to discover new engagement pockets.
Dynamic Creative & Automation
Use Dynamic Creative Optimization (DCO) tools to automatically mix and match creative elements for optimal performance.
Apply automation rules to pause or refresh underperforming creatives based on metrics like CTR, frequency, or cost per conversion.
Let algorithms handle routine adjustments while your team focuses on strategy and creative innovation.
A/B & Multivariate Testing
Continuously test variations of visuals, headlines, copy, and formats to understand what resonates.
Retire versions as soon as engagement starts to drop, before fatigue fully sets in.
Maintain a structured testing cadence to ensure ongoing learning and optimization.
Channel & Format Diversification
Spread exposure across multiple platforms (social, display, video, search) to prevent oversaturation of any single audience.
Sequence creative delivery (e.g., teaser video -> carousel -> static reminder ad) to maintain interest and storytelling momentum.
Adjust creative formats by channel to make each placement feel fresh and contextual.
Monitoring & Alerting
Build dashboards that track CTR, CPC, frequency, conversions, and audience sentiment over time.
Set up automated alerts for sudden drops in engagement or unexpected cost spikes.
Consider advanced detection techniques (e.g., change-point or anomaly analysis) to catch early fatigue in large-scale campaigns.
Ad Fatigue Case Study
Even the best-performing campaigns eventually hit a wall.
The key difference between teams that sustain success and those that burn through budget is how quickly they recognize and respond to fatigue.
Problem
A direct-to-consumer skincare brand noticed engagement on its Meta ads drop by nearly 30% over two weeks.
Click-through rates were falling, CPMs were creeping up, and customer comments increasingly mentioned seeing “the same ad again.”
The team realized that while their creative had once been a top performer, it hadn’t been updated in nearly two months, and the same video was running across Meta, YouTube, and Google Display.
Solution
The marketing team conducted a quick diagnostic: they compared audience frequency data, analyzed feedback sentiment, and confirmed fatigue as the culprit.
Within a week, they rolled out refreshed creatives featuring new visuals, reworded CTAs, and UGC-style testimonials.
They also implemented frequency caps and expanded to a broader lookalike audience to dilute overexposure.
Impact
CTR rebounded by 22% within ten days, CPMs fell back to their pre-fatigue baseline, and positive engagement returned.
More importantly, the team built an automated refresh schedule and dashboard alerts to detect similar dips in the future, turning a reactive fix into an ongoing preventive system.
This simple cycle of detect, refresh, and automate demonstrates how even small adjustments can revive fatigued campaigns and protect long-term ad performance.
Ad Fatigue Best Practices Checklist
To keep creative fatigue from eroding performance, build these principles into your campaign workflow from day one.
Launch With Creative Diversity
Start every campaign with multiple visuals, headlines, and CTAs to reduce early repetition and provide a strong foundation for testing and optimization.
Cap Frequency Per User
Limit how often each user sees your ad to avoid overexposure and irritation. This helps maintain engagement and keeps brand sentiment positive.
Monitor Key Metrics Frequently
Track CTR, CPC, CPM, ROAS, and frequency together. Sudden shifts or cost spikes often signal the first signs of ad fatigue in campaigns.
Rotate Or Refresh Creative Proactively
Don’t wait for performance to collapse. Schedule creative updates based on engagement trends, campaign length, or key seasonality shifts.
Exclude Overexposed Users
Use audience exclusions to remove people who’ve already converted or seen your ad too many times. This helps reset your exposure balance and keeps targeting efficient.
Automate Rotation Rules
Apply automated triggers or platform rules to pause stale creatives and prioritize high-performing variants. Automation keeps campaigns agile without constant manual oversight.
Use Cross-Channel Sequencing
Guide users through a story across channels. For example, build awareness with video, drive engagement on socials, and convert through display retargeting.
Archive Top Creatives For Reuse (With Variation)
Keep a library of proven creatives to repurpose later with refreshed visuals or copy. This saves production time while maintaining the proven performance of earlier assets.
Keep Your Campaigns Fresh & Your ROI Strong
Ad campaign fatigue doesn’t always look dramatic. It creeps in slowly, quietly draining performance and budget.
But with early detection and consistent creative refreshes, you can turn it from a hidden liability into an opportunity for smarter, more adaptive marketing.
Start by auditing your current campaigns. Look for early warning signs like declining engagement, rising costs, or growing audience frequency.
Then, act fast: implement at least two remedial tactics this week, whether it’s refreshing your creative, capping frequency, or testing a new audience segment.
For long-term resilience, build a fatigue-monitoring system. Use dashboards and automated alerts to catch drops early, schedule regular creative rotations, and make ad reviews a standing part of your campaign process.
Your best campaigns don’t have to fade quietly. Intervene early, stay creative, and keep testing. Freshness isn’t just about new ideas, it’s about maintaining momentum.
Customer churn is one of the clearest signals that something in your customer experience, product, or pricing isn’t working as it should.
But it’s also one of the most valuable sources of insight if you know how to analyze it.
Churn analysis is the systematic process of studying why customers leave, when it happens, and what patterns predict it.
By combining quantitative data (like engagement metrics and revenue loss) with qualitative feedback (like exit surveys and interviews), teams can uncover the real reasons behind attrition and act before it compounds.
In this guide, we’ll break down how to:
Define and measure churn effectively
Recognize the leading signals that predict customer loss
Identify common root causes and risk factors
Apply practical recovery and win-back strategies
Use the right tools and models to make churn analysis scalable
The goal is to help product, growth, and marketing teams turn churn from a passive KPI into a powerful customer retention strategy that strengthens loyalty, boosts lifetime value, and drives sustainable growth.
What Is Churn Analysis?
Churn analysis is the process of using data to understand why customers stop using your product or service.
It goes beyond simply tracking who leaves. It's about uncovering the behavioral, experiential, and contextual factors that drive those decisions.
Churn analysis helps businesses identify churn patterns so they can act before it’s too late. By examining customer activity, engagement, sentiment, and lifecycle data, teams can detect early warning signs, diagnose the root causes, and implement interventions that reduce churn and increase retention.
Churn-Related Definitions
There are a few related concepts worth clarifying before we dive in:
Churn Rate vs. Retention Rate
Churn rate measures the percentage of customers who leave during a given period, while retention rate measures the opposite: those who stay.
Together, they give a full picture of customer loyalty and stability.
Voluntary vs. Involuntary Churn
Voluntary churn happens when customers intentionally cancel or stop using a product, often due to dissatisfaction or a better alternative.
Involuntary churn results from payment failures, expired cards, or account issues; situations where the customer didn’t necessarily choose to leave.
Customer Churn vs. Revenue Churn
Customer churn tracks the number of customers lost, whereas revenue churn measures the dollar value lost from those customers.
A business might lose fewer customers overall but still face high revenue churn if those who leave are high-value accounts.
In practice, effective churn analysis blends all these perspectives to reveal what’s really driving attrition and where the biggest opportunities for improvement lie.
Why Churn Analysis Matters
Customer churn is a direct hit to a company’s growth engine. Every customer who leaves takes their recurring revenue, advocacy, and future upsell potential with them.
Churn analysis is critical so you can:
Check Lost Revenue and Slower Growth
When churn goes unchecked, acquisition efforts simply fill a leaking bucket.
A high churn rate can offset even the strongest acquisition performance, forcing teams to spend more just to maintain the same topline.
Stop Erosion of Customer Lifetime Value (CLV)
CLV depends on how long customers stay and how much they spend over time.
Even modest improvements in customer retention can have an outsized effect; research by Bain & Company shows that even a 5% increase in retention can lift profits by 25–95%.
Glean Product and Service Insights
Churn data is a mirror reflecting where your product, experience, or messaging falls short.
By analyzing churn reasons and feedback trends, teams can uncover specific issues, from confusing onboarding flows to missing features or misaligned pricing.
Shift From Reactive Fixes to Proactive Retention
Instead of scrambling to win customers back after they leave, churn analysis enables early detection.
By tracking engagement signals and customer health scores, companies can intervene before users disengage, turning churn prevention into an ongoing growth strategy.
In short, churn analysis transforms attrition from a silent loss into an active source of insight; a continuous feedback loop that strengthens product-market fit, boosts retention, and compounds growth over time.
Recognizing Churn (Signals & Metrics)
Before you can reduce churn, you need to recognize what it looks like and where it’s happening.
If you’re wondering how to analyze churn, it starts with tracking when customers leave and how their behavior changes in the time leading up to it. The right metrics reveal both the scale of attrition and the early signals that predict it.
Here are the key signals and metrics to tune into:
Churn Rate and Revenue Churn
At the simplest level, your churn rate measures the percentage of customers who stop using your product during a specific time period.
Revenue churn goes deeper, showing the value of the revenue lost: a critical distinction for subscription or tiered-pricing models where losing one large customer can outweigh ten smaller ones.
Cohort Analysis
Looking at churn by cohort (grouping users by signup month, plan type, or campaign source) helps identify patterns that aggregate metrics might hide.
For example, customers acquired through one marketing channel may have a much higher drop-off rate after three months, signaling an onboarding or expectation gap.
A 10% churn rate could be hiding a 2% churn among enterprise clients and a 25% churn among new SMB signups; two very different challenges that require different strategies.
Leading Indicators of Churn
The most valuable churn signals appear before customers actually cancel.
For example, if you’re building churn prevention strategies for SaaS / subscription, watch for:
Declining product usage or engagement
Drop in feature adoption or login frequency
Rising support tickets or unresolved issues
Account downgrades, paused subscriptions, or missed payments
Tracking these trends helps you intervene while customers are still recoverable.
Customer Health Scores
A composite customer health score combines behavioral, transactional, and sentiment data, such as activity levels, feedback scores, and NPS ratings, to create an at-a-glance view of customer stability.
When a score dips below a certain threshold, it can trigger proactive outreach from your customer success or support teams.
Exit Feedback and Cancellation Data
When a customer leaves, their reason for doing so is invaluable. Use exit surveys or short cancellation forms to capture why they’re leaving, whether its price, missing features, product complexity, or something else.
Over time, this qualitative data helps validate what your metrics can only imply.
Together, these signals create a multidimensional picture of churn, including why customers churn and how to recover them.
Root Causes & Risk Factors of Churn
Churn is rarely random. Beneath every cancellation is a story about unmet expectations, friction, or shifting needs.
Identifying the underlying causes is the difference between reacting to churn and preventing it altogether.
Key causes of churn include:
Poor Product-Market Fit Or Mismatched Expectations
When customers realize the product doesn’t solve their specific problem, or that it promised more than it delivered, churn is inevitable. This often traces back to mismatched messaging during acquisition or an unclear value proposition.
UX Or Performance Issues
Slow load times, buggy releases, or confusing navigation create daily frustration that compounds over time. Even small usability issues can add up, especially when alternatives make switching easy.
Weak Onboarding Or Adoption Support
The first few days or weeks determine whether customers experience value early on. If onboarding is complex, impersonal, or unclear, users may never reach the “aha moment” that makes them stay.
Lack Of Ongoing Engagement Or Value Delivery
Even customers who start strong can drift away if they stop seeing consistent value. When communication drops off, features go unused, or updates fail to excite, engagement naturally declines.
Pricing Misalignment Or Perceived Value Gap
Customers compare what they’re paying to what they’re getting. If competitors offer a similar solution for less, or if the perceived ROI doesn’t justify the cost, price becomes a reason to leave, even if it’s not the real root cause.
Competitive Alternatives Or Product Comparison
A strong product today can still lose customers tomorrow if competitors innovate faster. Churn spikes often follow major market shifts, new features elsewhere, or changing user expectations.
Involuntary Churn
Not all churn is intentional. Payment failures, expired cards, and billing errors can quietly erode retention. These “silent” losses are often easy to fix with better dunning logic, reminder emails, or automated payment recovery.
Seasonal Or External Behavior
Some churn patterns are tied to seasonality or broader market conditions. Retail and travel businesses, for instance, often see predictable dips during off-seasons or economic downturns.
By isolating these drivers, teams can prioritize fixes based on what’s actionable, strengthening product-market fit, improving experiences, and reducing friction points before customers decide to leave.
Churn Recovery & Win-Back Strategies
Even with the best customer retention programs, some churn is inevitable. The key is to treat every lost customer as a potential return, and to design systems that make recovery both scalable and personal.
Effective win-back strategies not only re-engage past customers but also prevent the same issues from recurring.
A strong win-back program includes:
Exit Surveys And Feedback Loops
Every churn event is a chance to learn. Collect exit feedback through short surveys or cancellation flows that ask why customers are leaving.
Segment the responses by reason (pricing, product limitations, service quality, or other themes) to uncover patterns. Aggregating these insights provides a direct map of what to fix first.
Targeted Win-Back Campaigns
Not all customers respond to the same message. Build targeted campaigns that speak directly to the reason for churn: a feature release for users who cited missing functionality, a discount or extended trial for those who found it too expensive, or content highlighting recent improvements.
Personalized “come back” messages show customers that their feedback led to change.
Personalized Outreach And Reactivation Sequences
Beyond automated campaigns, direct contact can make a difference. Combine email, in-app prompts, support calls, and personalized incentives to remind customers of your product’s value.
The most effective reactivation sequences feel human and timely, not generic or pushy.
Flexible Re-Onboarding Paths
When customers return, they shouldn’t have to start from scratch. Offer a smooth, tailored re-onboarding process that acknowledges their prior experience and highlights what’s new or improved since they left.
This reduces friction and helps them quickly rediscover value.
Payment Recovery And Dunning Strategies
For involuntary churn, automation is your ally. Implement retry logic, card update reminders, and grace periods to recover failed payments before accounts lapse.
Well-designed dunning workflows can quietly save a surprising percentage of customers without requiring direct outreach.
Product Improvements And Feature Fixes
Feedback from churned customers is one of the most actionable sources of insight. Use it to prioritize product enhancements, close feature gaps, and eliminate known pain points.
When you resolve the issues that drove people away, win-back campaigns become far more effective.
Segmentation And Differentiated Retention Treatment
Not all customers are equally valuable or equally at risk. Segment your audience by lifetime value, engagement level, or tenure, and apply different levels of intervention.
High-value accounts may warrant one-to-one outreach, while lighter-touch email automations work for lower-value segments.
Predictive Churn Modeling And Early Interventions
The most powerful win-back strategy is prevention.
Use churn prediction models to forecast which customers are likely to leave based on behavioral and transactional data. Then trigger proactive retention campaigns (product nudges, support check-ins, or loyalty rewards) before churn happens.
Together, these tactics turn churn into a continuous learning loop: diagnose, improve, re-engage, and retain.
Tools & Methods for Churn Analysis
Successful churn analysis relies on the right combination of tools, data, and analytical approaches.
It’s not just about tracking who left, it’s about connecting signals across platforms to reveal why they left and what to do next.
Analytics Platforms
Tools like Amplitude, Pendo, and Tableau help teams visualize behavioral data, track engagement trends, and monitor how customer actions correlate with retention.
These platforms are essential for identifying the early warning signs of churn, such as reduced feature usage or declining session frequency.
Subscription Analytics Tools And Dashboards
For subscription-based or SaaS businesses, tools like Baremetrics and ChartMogul provide specialized dashboards for metrics like monthly recurring revenue (MRR), churn rate, and lifetime value (LTV).
These insights allow you to quantify churn’s financial impact and track improvements over time.
Machine Learning And Statistical Modeling
Predictive churn models use techniques such as logistic regression, random forest, and ensemble learning to forecast which customers are most likely to churn.
By training on historical data, these models identify patterns that human analysts might miss, enabling proactive interventions before cancellations occur.
Feature Engineering
Behind every predictive model lies smart feature engineering, deriving metrics like usage frequency, time since last activity, or support interaction counts.
These variables translate raw data into meaningful indicators of customer health and churn risk.
Cohort And Retention Analysis
Cohort tables and survival analysis reveal how long different customer segments remain active.
Tracking retention curves helps teams see where drop-offs occur and whether recent initiatives (like onboarding improvements or pricing updates) are having a measurable impact.
Cancellation Surveys And Qualitative Feedback
While quantitative data shows what is happening, qualitative insights explain why.
Exit surveys, user interviews, and customer feedback channels reveal the human reasons behind churn, turning raw metrics into actionable strategy.
A/B Testing Of Retention And Win-Back Techniques
Run controlled experiments to measure which retention tactics work best, whether it’s a new reactivation email, a feature release notification, or an incentive offer.
Continuous testing helps refine your churn prevention and win-back playbooks.
Regular Audits And Dashboards With Alerting
Establish ongoing churn monitoring with real-time dashboards and automated alerts.
By flagging sudden spikes or unusual churn patterns early, teams can investigate root causes and act before small issues scale into major losses.
Together, these tools and methods turn churn analysis into a living system that continuously learns, predicts, and optimizes retention outcomes over time.
Churn Analysis Example: eCommerce Case Study
To see churn analysis in practice, consider a hypothetical e-commerce brand called Glow that sells subscription-based beauty boxes.
Despite steady new signups, Glow noticed revenue plateauing and suspected a churn problem.
Using churn analysis, the team uncovered a clear pattern: while overall churn averaged 12% per month, a deeper segmentation analysis revealed one hidden, high-churn cohort: first-time subscribers acquired through social media ads.
This group was churning at nearly 30%, twice the company average.
When Glow dug deeper into behavioral data, several red flags emerged for this cohort:
Lower engagement with post-purchase emails and community content
High rates of unopened boxes and skipped shipments
Frequent “product didn’t match expectations” feedback in exit surveys
Armed with these insights, the team took a multi-pronged approach:
Targeted win-back campaigns: They launched personalized “We Miss You” emails featuring a sneak peek of next month’s box and a 15% discount for returning customers.
Flexible re-onboarding: Returning users were invited to retake the beauty quiz to refresh their preferences, ensuring the next box felt customized and new.
Product improvements: The company expanded its quiz logic and supplier pool to better match items with customer profiles.
Predictive modeling: Using usage and engagement data, Glow built a churn prediction model that flagged high-risk subscribers after their first shipment, triggering proactive check-ins.
Within three months, Glow saw measurable results:
Churn dropped by 29% in the target segment
Revenue per subscriber increased due to longer retention
Exit survey mentions of “poor fit” decreased by 40%
By pairing segmentation analysis with tailored recovery strategies, Glow transformed churn from a hidden leak into a source of actionable insight and built a system that keeps customers engaged long after their first purchase.
Churn Analysis Best Practices Checklist
These eight best practices ensure your churn analysis insights stay sharp and your retention strategies keep evolving.
1. Always Segment Before Analyzing Overall Churn
Aggregate churn rates can hide important differences between customer groups. Break data down by plan type, acquisition source, region, or tenure to reveal where attrition truly happens.
2. Track Both Customer And Revenue Churn
Losing one enterprise client can outweigh dozens of small accounts. Measuring both customer count and revenue impact helps you focus on the churn that matters most.
3. Use A Combination Of Quantitative And Qualitative Data
Metrics show what’s happening, but feedback explains why. Pair product analytics with surveys, interviews, and support transcripts to see the full picture.
4. Build Predictive Models To Act Early
Don’t wait for churn to happen. Use machine learning or behavioral scoring to flag customers showing early signs of disengagement and trigger targeted retention actions.
5. Test Win-Back Tactics Systematically
Treat reactivation like an experiment. A/B test subject lines, offers, and messaging to discover which tactics yield the highest recovery rates.
6. Prioritize Fixes Based On Impact And Volume
Not every churn driver deserves equal attention. Rank causes by how many customers they affect and how easily they can be resolved, focusing first on high-volume, high-impact issues.
7. Treat Involuntary Churn Separately
Payment failures and account issues are operational, not emotional. Use automated billing retries and clear communication to recover these customers efficiently.
8. Monitor Churn Over Time And Iterate
Retention strategies aren’t one-and-done. Track trends quarterly, measure progress against baselines, and refine your approach as products, markets, and customer expectations evolve.
When followed consistently, these principles turn churn analysis from a reactive report into a continuous engine for retention and growth.
Turn Churn Into Opportunity
Churn is an unavoidable part of doing business, but how you respond to it determines whether it stays a revenue leak or becomes a growth lever.
With the right data, tools, and processes, churn analysis transforms from a backward-looking metric into a forward-looking advantage.
Every lost customer offers insight into how to build a better product, experience, and relationship. By identifying early warning signals, understanding root causes, and applying targeted win-back strategies, teams can not only reduce churn but also strengthen customer loyalty and lifetime value.
Next steps:
Audit your churn metrics: Track both customer and revenue churn?
Build an exit feedback loop: Capture reasons for departure and aggregate them by cause.
Pilot one win-back campaign: Target a single churn segment and measure your recovery rate.
With thoughtful analysis and proactive retention, every departure becomes a chance to learn, adapt, and grow stronger.
Building a strong marketing funnel can sound like something reserved for data scientists or growth hackers, but it’s just a structured way to understand how people find, engage with, and buy from your brand.
This guide breaks the process of how to make a funnel down step by step, from strategy to structure, mapping to measurement.
You’ll learn how to define clear stages, assign content and offers to each one, track real performance data, and optimize over time.
Along the way, we’ll share common mistakes to avoid, tools to make the job easier, and an example of how a D2C brand turned funnel mapping into measurable growth.
The goal is to help you design a funnel that drives predictable, scalable conversions and gives you a clear, adaptable framework for future campaigns.
The Modern Marketing Funnel
The marketing funnel isn’t dead, it just evolved.
What used to be a neat, linear path from awareness to purchase now looks more like a web of overlapping touchpoints. Today’s buyers don’t move step-by-step; they bounce between channels, revisit old pages, and rely on peer recommendations as much as ads.
That makes understanding and mapping your funnel more important, not less.
The classic marketing funnel design is giving way to “influence maps.” These are frameworks that show how different moments of interaction shape a buyer’s decision over time.
Rather than treating every customer journey as a straight line, influence maps capture how awareness, consideration, and conversion loops feed into one another across social, search, email, and word-of-mouth.
At its core, though, the funnel still describes the same behavioral journey:
Top of funnel (TOFU): where you attract and educate new audiences.
Middle of funnel (MOFU): where you nurture interest and address objections.
Bottom of funnel (BOFU): where you convert, retain, and ideally re-engage.
Each stage has its own goal, content, key performance metrics, and, most importantly, its own leakage points where users drop off or disengage.
A well-mapped funnel gives you a shared language for your marketing strategy. It shows where people are falling off, which touchpoints actually drive conversions, and where to invest in testing and optimization.
When done right, funnel mapping transforms scattered marketing activities into a measurable, repeatable system for growth.
Foundational Setup: Before You Map
Before you start sketching boxes and arrows, it’s worth taking a step back. A great funnel isn’t built from templates, it’s built from clarity.
The strongest funnels start with a deep understanding of what success looks like, who you’re guiding, and where your current process falls short.
Here’s your funnel mapping guide for digital marketing:
1. Clarify Your Objectives and Conversion Goals
Decide what “winning” means for your business as you map out your funnel for lead generation and beyond. Is it a newsletter signup? A demo request? A completed purchase?
Defining this up front shapes everything else, from your messaging and targeting to how you measure success.
Without a clear conversion goal, even the best-designed funnel won’t tell you much.
2. Understand Your Audience and Journey
The funnel should reflect how your audience actually thinks and behaves.
Map out key personas, their motivations, information needs, and the objections that hold them back. What triggers a decision? What makes them hesitate?
The better you understand those nuances, the easier it is to design touchpoints that feel intuitive rather than forced.
3. Audit Existing Data and Performance
If you already have traffic or conversion data, treat it like a roadmap of what’s working and what’s not.
Look for drop-offs between ad clicks and signups, or between product pages and checkout. These “leaky” moments are where your funnel can make the biggest impact.
4. Decide Funnel Depth and Naming Conventions
Not every business needs a five-stage masterpiece. Some funnels work perfectly well with three simple stages, while others benefit from adding loyalty, referral, or re-engagement layers.
Choose a structure that fits your model and name each stage in plain language that your team can rally around.
Getting these foundations right ensures that when you finally start mapping, every stage of your funnel has purpose, data behind it, and a clear path to conversion.
How to Make a Marketing Funnel (Step-by-Step Mapping)
Now for the fun part: turning your funnel strategy into a productive workflow.
Whether you’re starting from scratch or refining an existing setup, the key is to treat this as a living system, not a one-and-done diagram.
Here’s a step-by-step guide to mapping your funnel:
1. Define Your Stages (and Naming)
Start by outlining the key phases of your funnel. The classic version looks like this:
Awareness
Interest
Consideration
Conversion
Retention
But don’t feel boxed in. The goal is to capture the mindset and behavior of your users at each step, not just replicate generic marketing terms.
For example:
Awareness might mean “Discover” for a lifestyle brand or “Evaluate” for a B2B product.
Retention could be reframed as “Delight” or “Advocacy” depending on your goals.
The important thing is that every stage has a clear handoff: a moment when a user takes an action that moves them forward.
2. Assign Content, Offers, and Touchpoints Per Stage
Each stage of your funnel should feel like a natural next step in the user’s journey.
To make that happen, plan content, offers, and channels that meet people where they are:
Top of funnel: educational blog posts, paid social ads, SEO content, and influencer collaborations.
Middle of funnel: comparison guides, webinars, demos, or email sequences that build trust.
Bottom of funnel: pricing pages, testimonials, case studies, and limited-time offers that help users commit.
In modern funnels, this often means building dynamic “feeds” rather than static paths so your content catalog, ad audiences, and product recommendations can all adapt automatically to a user’s behavior.
3. Map the Flow and Annotate Conversion Metrics
Once you’ve listed your stages and touchpoints, sketch out the flow.
Create a simple funnel diagram with arrows showing transitions between each stage, and annotate it with the metrics that matter:
Conversion rate per stage
Drop-off percentage
Traffic or volume at each step
This visual snapshot helps you quickly spot where your funnel is leaking and where optimization will have the biggest impact.
4. Instrument Tracking and Analytics Logic
Funnels are only as good as the data behind them. Set up the right tracking from day one:
Define key events: pageviews, clicks, form submissions, video completions, or email opens.
Use funnel or cohort analysis tools to see how users move between stages.
Build a dashboard that visualizes flow, bottlenecks, and conversion trends over time.
This instrumentation ensures your funnel isn’t just a diagram but a measurable engine you can actually optimize.
5. Run Tests and Iterate
Once your funnel is live, expect it to evolve. Launch a minimum viable funnel (MVF) first, even if it’s imperfect. Then use real data to improve it.
Try to:
Test one element at a time: the offer, CTA placement, or messaging tone.
Use A/B or split testing to see which variant moves users further down the funnel.
Revisit assumptions; the real journey often looks different from what you mapped.
Every iteration teaches you something new about how your audience behaves.
6. Extend and Refine (Beyond Conversion)
A strong funnel doesn’t stop at “thank you for your purchase.”
Post-conversion touchpoints can be just as valuable:
Retention: onboarding emails, loyalty programs, or user communities.
Expansion: cross-sell and upsell opportunities.
Re-engagement: win-back campaigns or referral programs.
Over time, your funnel becomes less of a straight line and more of a loop: a system that feeds continuous growth and customer loyalty.
Common Mistakes to Avoid
Even the best funnel strategy can go sideways if you overthink it, or worse, never evolve it.
Below are some of the most common traps teams fall into when establishing their marketing funnel design, and how to steer clear of them.
Overcomplicating the Funnel
More stages don’t automatically mean more insight.
It’s easy to get carried away mapping micro-steps like “clicked CTA” or “visited pricing page,” but too much granularity makes your funnel impossible to manage.
Start simple, validate what works, and add complexity only when the data demands it.
Treating the Journey as Strictly Linear
Real customer behavior rarely follows a straight path. People skip steps, return later, or convert from unexpected touchpoints.
If your funnel assumes a one-way progression, you’ll miss valuable signals from loops and lateral moves, like re-engagement or cross-channel conversions.
Misaligned Messaging
A common sign of a weak funnel is content that doesn’t match where users actually are in their decision process.
If you’re hitting cold audiences with hard-sell CTAs or serving introductory blog posts to ready-to-buy leads, your conversion rate will stall. Always align messaging to mindset.
Missing Instrumentation
You can’t optimize what you can’t measure.
When stage transitions aren’t tracked properly, like if email clicks or demo requests aren’t tied to a specific funnel stage, your data will have blind spots.
Fill those gaps early with clear event tracking and analytics logic.
Launching Without a Testing Plan
Your first funnel is just a hypothesis. Without A/B testing or regular reviews, you’re guessing at what’s working.
Build testing into your process from the start: small experiments beat sweeping changes every time.
Neglecting Post-Conversion Stages
Retention, referral, and upsell steps are where long-term value comes from. Many funnels stop at the sale, leaving repeat revenue on the table.
Keep the conversation going: it’s easier to re-engage an existing customer than win a new one.
Keeping a Static Funnel
Customer behavior, algorithms, and channels change constantly. Your funnel should evolve, too.
Revisit it quarterly to check for new drop-off points, emerging audience patterns, or outdated messaging. A living funnel is a profitable funnel.
Tools, Frameworks & Advanced Considerations
Once your funnel framework is in place, the right tools can turn it from a static diagram into a living, data-driven system.
From visualization software to analytics platforms, here’s how to build a modern funnel stack that actually works.
Tools for Visualization and Mapping
Start by getting your funnel out of your head and onto the screen. Visual tools like Miro, Lucidchart, or Funnelytics make it easy to map each stage, touchpoint, and handoff.
A visual layout helps teams spot gaps, align on definitions, and understand how campaigns fit into the bigger picture.
Pro tip: Keep your diagram simple enough for a newcomer to grasp in under a minute. If it looks like a subway map, it’s too complicated.
Analytics Platforms with Funnel Modeling
Once your funnel is mapped, you’ll need analytics tools to track how users actually move through it.
Platforms like Google Analytics 4, Mixpanel, and Amplitude let you:
Build custom funnel reports and measure conversion rates at each stage
Visualize drop-offs and time-to-convert
Segment users by source, campaign, or cohort
These tools make it easy to connect real user behavior to your funnel hypotheses.
Funnel Optimization and Experimentation
Optimization platforms allow you to test changes directly within your funnel.
You can:
A/B test messaging, CTAs, or page design
Identify high-impact improvements based on lift in conversion rates
Automate winning variants into your live campaigns
Even small tweaks in a key stage (like reducing form fields or simplifying copy) can have outsized effects on total conversion.
Cohort and Breakdown Analyses
Funnels don’t perform the same for everyone. Use cohort analysis to break down performance by segment. For example, new vs. returning users, or organic vs. paid traffic.
Cohort insights help you tailor content and identify underperforming audiences that need special attention.
Advanced Funnel Methods
As your data matures, you can move beyond traditional funnel modeling into adaptive, predictive systems:
Predictive funnels: use machine learning to forecast conversion probabilities and flag at-risk users.
Adaptive funnels: automatically adjust content and next steps based on real-time behavior.
Dynamic pathing: recognizes that not all users follow the same order and models non-linear journeys accordingly.
These approaches turn funnels into flexible systems that learn and improve continuously.
Cross-Channel Orchestration
Finally, ensure your funnel flows seamlessly across channels. A prospect who clicks a paid ad, reads a blog, and signs up for a webinar should feel like they’re on one cohesive journey, not three separate ones.
Centralize your data, align messaging, and use connected tools (like CRM integrations or marketing automation platforms) to bridge the gaps between touchpoints.
Example Funnel Walkthrough: D2C eCommerce
To see funnel mapping in action, let’s look at a hypothetical D2C skincare brand named LumaCare that sells cruelty-free moisturizers and serums online.
When the LumaCare team first started, they had strong traffic but inconsistent sales. They decided to map and optimize their funnel to understand where users were dropping off and how to fix it.
Here’s what they did:
1. Define Audience and Funnel Stages
LumaCare’s team defined their funnel around five simple stages:
Stage
Goal
Example User Action
Awareness
Reach new audiences
User sees an Instagram ad about “clean skincare that actually works”
Interest
Build curiosity and trust
User clicks through to a blog post on ingredient transparency
Consideration
Nurture engagement
User subscribes to the email list for a “skin quiz” and personalized recommendations
Conversion
Drive first purchase
User adds a moisturizer to cart and completes checkout
Retention
Encourage repeat purchases
User receives post-purchase emails, reviews products, and joins the loyalty program
2. Create Content, Offers & Touchpoints
For each stage, they listed targeted content and offers:
Awareness: Paid social ads, influencer videos, and UGC TikToks showcasing before/after results.
Interest: Blog posts, product comparison charts, and retargeting ads linking to customer stories.
Consideration: “Find Your Routine” quiz, 10% off first order email, and Instagram stories with skincare tips.
Conversion: Optimized product pages with social proof, free shipping thresholds, and a one-click checkout.
Retention: Automated reorder reminders, loyalty points, and referral discounts.
3. Map Flow & Identify Leaks
Once mapped visually, the data revealed a major drop-off between Consideration and Conversion: nearly 40% of email subscribers never returned to the site.
By adding retargeting ads that showcased each user’s quiz results (“Here’s your custom LumaCare routine”), conversion rates rose from 2.1% to 3.6%, and average order value increased by 15%.
4. Track & Instrument
The team used Google Analytics and other marketing intelligence tools to track:
Traffic by channel and UTM
Quiz completions and email signups
Add-to-cart vs. checkout completions
Repeat purchase rate
Custom dashboards displayed conversion rates by stage, making it easy to see how each channel performed and where to focus testing.
5. Test & Iterate
Every quarter, LumaCare ran one new test per funnel stage, like changing creative formats for Awareness or simplifying the quiz for Consideration.
Over six months, this consistent testing cycle helped them cut drop-off by 28% and raise customer lifetime value by 22%.
6. Extend the Funnel Beyond Conversion
The final optimization came after purchase. The brand created a “Glow Points” loyalty program and sent reorder prompts when customers’ average product usage time ran out.
This small retention loop turned one-time buyers into brand advocates, closing the loop and fueling the top of the funnel with referral traffic.
The takeaway: By treating their funnel as a living system, LumaCare built a predictable growth engine that turned awareness into advocacy.
Put Your Funnel Map Into Action
You’ve got the framework. Now it’s time to put it to work.
Funnel mapping isn’t just a planning exercise; it’s a habit that keeps your marketing grounded in data and aligned with real customer behavior.
Start simple:
Sketch your funnel. Even a rough version on paper helps you visualize how people move from discovery to conversion.
Spot one weak transition. Look for where engagement or conversion drops most sharply. That’s your first test point.
Design an experiment. Try a new offer, update messaging, or simplify a step in the journey. Measure what changes.
Create a funnel dashboard. Track key metrics (volume, conversion rate, and drop-off by stage) so you can see progress at a glance.
Once you’ve built your first version, keep iterating. The best funnels evolve alongside your audience, product, and market.
When you define clear stages, align content and data, and test continuously, you transform marketing from guesswork into a repeatable growth engine.
Black Friday–Cyber Monday compresses attention, inflates numbers, and tempts even disciplined teams to chase whatever’s working “right now.” That’s exactly when a pure one‑day read becomes your most persuasive liar. Paths to purchase stretch, earlier touchpoints mature, and last‑click pull rises under sale pressure—making capture look wildly efficient while prospecting gets under‑credited in the moment. The fix is a calm operating system: pre‑commit decision windows, then demand longer‑window corroboration before moving real budget.
This article serves as a part companion piece to a webinar I hosted about Black Friday. You can see the full recording of the webinar here:
Why Windows Matter More During BFCM
Cyber Five distorts time. People see your ads, browse, compare, subscribe, and come back, often days later. If you only look at “today,” you’ll miss what you seeded earlier and over‑credit what happened last.
What changes in the data (and why it matters):
Paths to purchase lengthen, so one‑day under‑credits earlier work that pays back over 7/14/30 days.
Last‑click pull increases, making capture look even better than the true incremental picture.
Segment dynamics shift: first‑time customers skew ~73% “on‑day,” while blended sits just under 60% because returning revenue lags—so your windows should vary by segment, not just by channel.
The Rule of Thumb that Survives the Weekend
Short windows for capture. Longer windows for creation. That’s it.
Capture (Brand, Shopping, retargeting): optimize primarily on short windows (1–3 day) because these tactics convert quickly under sale pressure. Kill Last‑Click Bias: a simple playbook for 1‑day vs 7/14/30‑day reads during BFCM
Black Friday–Cyber Monday compresses attention, inflates numbers, and tempts even disciplined teams to chase whatever’s working “right now.”
That’s exactly when a pure one‑day read becomes your most persuasive liar.
Paths to purchase stretch, earlier touchpoints mature, and last‑click pull rises under sale pressure, making capture look wildly efficient while prospecting gets under‑credited in the moment.
The fix is a calm operating system: pre‑commit decision windows, then demand longer‑window corroboration before moving real budget.
Use C+DV to Keep the Upper‑Funnel Funded
Clicks alone miss where view‑driven discovery is doing real work. C+DV (Clicks + Deterministic Views) adds verified views on top of clicks, making upper‑funnel influence visible precisely when last‑click capture is shouting.
Keep C+DV on, and use Model Comparison to see how the same prospecting set performs at 1‑day vs 7/14/30‑day before you move money.
If clicks‑only looks soft on 1‑day but C+DV at 14/30‑day shows lift, hold (or scale within your ladder) and let the window do its job. Save short‑window enforcement for capture.
Guardrails You Can Publish
To keep weekend decisions fast and consistent, publish simple, shared guardrails before the sale—and stick to them through Cyber Five.
Pre‑commit decision windows by channel and set a refresh cadence: one review pre‑Thanksgiving and one post‑Black Friday. This keeps targets current without moving the goalposts mid‑stream.
Document on‑day vs lag contribution for each channel after the event (e.g., blended ~59% same‑day; first‑time ~73% same‑day), then carry those profiles into next year’s windows and pacing.
Practice in‑flight hygiene: in your overview, compare total vs one‑day revenue to judge how much of “today” is truly coming from today’s spend; check data‑freshness indicators before changing budgets.
Make intraday reads proportional to volume: use hourly only for high‑throughput channels (Meta/Google). For smaller channels, rely on 1/3/7‑day trends—there isn’t enough hourly signal for surgical changes.
Pitfalls to Dodge (and the Easy Fix)
Weekend jitters are real: when the one‑day view softens on Saturday or Sunday, cutting prospecting feels safe—but it starves Monday of demand.
Hold floors and require 7/14/30‑day corroboration before touching those budgets.
The other trap is chasing last‑click heroes off a one‑day read; keep capture funded on short windows, but validate prospecting with longer windows and C+DV so you scale what’s truly working; not just what’s loud.
Don’t cut prospecting on the weekend; enforce 7/14/30‑day checks.
Don’t chase one‑day last‑click spikes; keep capture short‑windowed, use C+DV for prospecting.
Bottom Line
BFCM rewards teams that read like operators, not spectators. Lock your decision windows, enforce “short for capture, longer for creation,” and layer C+DV so view‑heavy prospecting gets deterministic credit. That’s how you prevent last‑click bias from draining the very budgets that make the weekend big.
To watch the full webinar, click here. Additionally, you can download our guide that explains all you need to know about Clicks + Deterministic Views, written by my colleague, CJ Hunter, and me. Download it for free here.
The five days from Thanksgiving through Cyber Monday have a shape you can count on: a decisive lift into Black Friday, a natural cool‑off on Saturday and Sunday, and a measured rebound on Cyber Monday.
In Northbeam’s aggregate read from 2024, median brands increased daily spend roughly one‑third from Thanksgiving to Black Friday, pulled back about a fifth across the weekend, and added back a modest ~5% on Monday.
On efficiency, median MER jumped into Black Friday, dipped over the weekend, then recovered by ~19% on Monday versus the weekend. These are medians across the portfolio, not single‑brand anecdotes—which is why they’re useful as pacing guardrails for 2025.
Black Friday’s spike is the harvest of intent seeded earlier; the weekend lull is normal, not an indictment of your offer; and Monday’s rebound is real, just typically smaller than Friday’s peak.
If you build your spend ladders and decision windows around that arc, you avoid the budget whiplash that comes from reacting to every wobbly 1‑day metric in the moment.
This article serves as a part companion piece to a webinar I hosted about Black Friday. You can see the full recording of the webinar here:
What the Data Actually Says
Let’s be explicit about the 2024 baselines:
Median daily spend moved ~+34.9% from Thanksgiving to Black Friday, fell ~−17.7% across the weekend, then rose ~+5.0% on Cyber Monday.
Median MER climbed ~+38.1% from Thanksgiving to Black Friday, dipped over Saturday/Sunday, and rebounded ~+19.5% on Monday versus the weekend.
Those moves are durable enough to pre‑commit your day‑by‑day budgets without waiting for in‑window drama to dictate your plan.
Why the Week Takes this Shape
Paths to purchase lengthen around BFCM. A pure 1‑day read under‑credits the prospecting and mid‑funnel work you did in the lead‑up, while last‑click pull gets louder under sale pressure.
On Friday, capture surges at the same time earlier touchpoints mature; by Saturday/Sunday, the initial wave cools; on Monday, lagged impact and lifecycle channels (Email/SMS) push efficiency back up.
Set your windows in advance and you’ll see the shape clearly instead of second‑guessing it.
A Pacing Plan Built on the Pattern
Start by treating Thanksgiving as your runway and Friday as your step‑change.
Pre‑approve about a one‑third lift into Black Friday—aligned to the median MER jump—and use MMM (if you have it) to set channel‑level caps so you don’t run past diminishing returns in the heat of the moment. This removes debate from the biggest move of the weekend.
On Saturday and Sunday, plan a controlled pullback of roughly 17–20% versus Friday while you protect demand creation.
Keep capture lanes (Brand/Shopping/retargeting) fully covered, but preserve prospecting budgets that pay back on multi‑day windows; cutting too deep on the weekend is how teams accidentally starve Monday’s rebound.
For Cyber Monday, lean in, don’t lunge.
Median brands added back only a small slice of weekend spend (~5%), while many top performers simply held near weekend levels and let lagged impact do more of the work. Think control, not heroics.
Operationally, publish profit/MER targets and channel stoplights for Fri–Mon, then schedule two refreshes (pre‑Thanksgiving and post‑Black Friday) so “what good looks like” stays current without moving the goalposts mid‑stream.
How to Read Performance In Real Time
A strict 1‑day view is misleading during BFCM. Make your decision windows the guardrail: optimize capture on short windows; require 7/14/30‑day corroboration for prospecting before you move meaningful budget. Expect more last‑click pull and resist reflexive reallocations that rob demand creation to feed capture.
Use hourly views only where you have real volume (Meta, Google). For smaller channels, rely on 1/3/7‑day trends—there isn’t enough hourly signal to justify surgical changes—and always check data‑freshness indicators before shifting budgets.
If you’ve enabled Clicks + Deterministic Views (C+DV), keep it in your model comparison throughout the window. View‑heavy upper‑funnel often looks soft on 1‑day clicks while C+DV over multi‑day windows shows real contribution; that’s your permission to hold or scale without starving the top of the funnel.
What “Good” Looked Like Under the Hood Last Year
Two calibrators from 2024 can keep your expectations grounded in 2025:
Segment timing: During BFCM, first‑time customer revenue is heavily same‑day (median ~73%), while blended on‑day impact sits just under 60% given the returning base’s lag. That split argues for segment‑specific targets and windows—don’t force a single blended rule on both audiences.
Creative and site response: Median CTR and conversion rate improved materially over the Cyber Five window when ad creative and promo‑specific landing pages were tightly aligned. Ship those assets early—they’re part of why Friday spikes and Monday rebounds.
The Bottom Line
Plan to the shape the market keeps giving you: a firm step into Friday, a disciplined weekend pullback that preserves demand creation, and a measured Monday re‑lean.
Align spend to the median MER moves we saw in 2024, publish stoplights, and read performance through windows that reflect how Cyber Five actually converts.
That’s how you move fast and stay profit‑first through the most volatile week of the year.
When people talk about product growth, Monthly Active Users (MAU) is one of the first metrics that comes to mind.
It tells you how many unique users actively engage with your product in a given month, offering a snapshot of your audience’s true engagement and loyalty.
MAU reflects sustained, recurring use that signals product-market fit and long-term health. Tracking it consistently helps you understand not just how many people are discovering your product, but how many are coming back for more.
In this article, we’ll break down exactly what MAU means, how to define and measure it accurately, and how to interpret changes in your monthly active user trends.
You’ll learn how MAU compares to other engagement metrics like DAU and WAU, what to watch for in your data, and how to use these insights to guide product, marketing, and growth decisions.
Defining MAU: What It Means & Why It Matters
Monthly Active Users (MAU) is one of the most common metrics used to understand how many people actually use your product within a given 30-day period.
In simple terms, MAU counts the number of unique users who engage with your product at least once in a rolling month.
This engagement could mean anything from logging in, completing a key action, or using a specific feature depending on how your business defines “active.”
That last point is crucial: what qualifies as “active” isn’t universal.
For a social media platform, it might be posting, liking, or messaging. For a SaaS product, it could be running a report or completing a workflow.
Defining activity too broadly (like any login) can inflate your numbers, while defining it too narrowly can hide genuine engagement.
The key is to choose a definition that reflects meaningful interaction, the kind of behavior that signals users are actually getting value.
Investors and product teams alike use MAU as a shorthand for overall product health: rising MAU suggests growing adoption and retention, while declines can signal churn, friction, or competition pulling users away.
In practice, MAU serves as a core growth signal across teams:
Product uses it to measure adoption and validate feature success.
Marketing tracks it as a response to campaigns or seasonality.
Investors see it as a proxy for momentum and market traction.
In short, MAU gives you a pulse on whether your product is not only attracting users, but keeping them.
MAU vs DAU vs WAU: Other Activity Metrics
While MAU captures how many people engage with your product in a month, it’s just one piece of the activity puzzle.
Most companies also look at DAU (Daily Active Users) and WAU (Weekly Active Users) to understand how frequently people return.
DAU measures the number of unique users active on a given day; a great pulse check for products meant to be used daily, like messaging apps or social platforms.
WAU looks at weekly engagement; often the sweet spot for tools that support recurring but not constant use, such as project management or eCommerce dashboards.
MAU, meanwhile, shines when your product fits into users’ broader monthly cycles; for example, subscription-based services, billing tools, or long-term planning apps.
Together, these metrics help paint a fuller picture of user behavior.
DAU vs. MAU Importance
One of the most useful ways to connect them is the DAU/MAU ratio, sometimes called a stickiness metric. It shows what percentage of your monthly users return on a typical day.
A higher ratio means users find ongoing value and are integrating your product into their regular routines. For example, a ratio above 20% often indicates strong engagement for a consumer product, while enterprise tools may trend lower due to less frequent usage patterns.
Knowing when to focus on DAU vs. MAU depends on the nature of your product:
Daily tools (e.g., chat apps, task trackers) should prioritize DAU: consistency and habit formation matter most.
Periodic tools (e.g., accounting software, marketplaces) should emphasize MAU: breadth of retention over time is the key metric.
MAU Limitations
Of course, MAU alone can never tell the whole story. It can’t show depth of engagement (how long or meaningfully users interact) or the quality of those sessions.
Different teams might also define “active” differently, leading to data inconsistencies.
That’s why MAU should always be paired with complementary indicators, such as session duration, churn rate, or feature-specific usage, to create a more accurate view of true user engagement metrics.
How to Measure & Define MAU Accurately
Tracking MAU for growth seems simple on the surface, but accuracy depends on how you define and measure it. Subtle differences in setup can lead to big variations in your reported numbers, so a consistent, transparent methodology is key.
1. Choose Your “Active” MAU Definition
Start by identifying the action that represents meaningful engagement.
For some products, that might be any session or login; for others, it’s a specific key behavior like sending a message, creating a project, or making a purchase.
The goal is to capture real value moments, not vanity metrics.
2. Ensure User Identification and Deduplication
To avoid inflated numbers, you need a reliable way to identify unique users. Most teams do this through a user ID system, device ID, or cookies that tie activity back to one person or account.
Deduplication is critical: one user logging in on multiple devices shouldn’t count multiple times.
3. Use a Rolling 30-Day Window (Not Just Calendar Months)
Many analytics tools default to monthly reporting, but a rolling 30-day window provides a truer picture of ongoing activity.
Instead of resetting at the start of each month, it continuously measures the last 30 days, giving you smoother, more actionable trend lines and avoiding arbitrary cutoffs.
4. Handle Edge Cases
Inactive accounts, bots, and duplicates can all skew your MAU data.
Exclude accounts that haven’t verified or completed onboarding.
Filter out automated or spam activity.
Set a threshold for reactivation (e.g., users inactive for 90+ days are removed until they return).
These small steps make a big difference in maintaining data integrity.
5. Pick the Right Tools for MAU Tracking
You can measure MAU through a range of analytics platforms, depending on your setup:
Google Analytics 4 (GA4) or Mixpanel for web and app analytics.
Product analytics suites for deeper event-based tracking.
Custom data systems if you need more control or privacy compliance.
6. Maintain Data Quality and Consistency
Your MAU metric is only as reliable as the data you feed it.
Make sure your event logging, attribution, and cleaning practices are standardized across teams. Document how “active” is defined, when data is refreshed, and what’s excluded so everyone interprets the number the same way.
When measured accurately, MAU becomes a powerful, trustworthy signal that you can safely use to track growth, retention, and engagement trends without worrying about false positives or noise.
Interpreting MAU Trends & Signals
Once you’ve defined and tracked MAU consistently, the next question is: what are the numbers actually telling you?
MAU trends offer valuable clues about product health, user behavior, and the effectiveness of your growth strategies, but only if you know how to read them.
Rising MAU: Growth, Adoption, and Retention
A steady or sharp increase in MAU usually signals healthy user acquisition and engagement. It can mean that new users are finding value quickly, existing users are sticking around, or both.
You’ll often see MAU spikes after successful product launches, marketing campaigns, or PR moments, followed by a stabilization period. If those new users keep showing up in the following months, it’s a sign your retention loop is working.
Plateauing or Declining MAU: A Warning Sign
When MAU growth slows or reverses, it can indicate churn, disengagement, or friction in the user experience.
Common causes include:
A confusing onboarding flow or recent UI change
Technical issues or poor performance
Competitors capturing attention
Lack of fresh content or features
A short-term dip isn’t always cause for alarm, it could reflect seasonality or campaign cycles, but persistent decline deserves investigation. Pair MAU with churn, NPS, or usage depth metrics to pinpoint the issue.
Understanding Seasonal or Cyclical Effects
Certain products have natural rhythms. For instance, budgeting apps spike in January, travel platforms surge in summer, and education tools peak during the school year.
Recognizing these patterns helps you avoid misreading normal fluctuations as problems. Track year-over-year MAU to distinguish true shifts in engagement from seasonal cycles.
Using Cohort Analysis for Deeper Insight
MAU alone gives a top-line view, but breaking it down through cohort analysis can help reveal why it’s changing.
Segment your users by acquisition channel, geography, signup month, or product version to see which groups are growing, retaining, or dropping off.
For example, you might find that users acquired through organic search retain better than those from paid ads, or that engagement is strong in Europe but lagging in North America.
Correlating MAU with Product and Marketing Actions
Finally, connect the dots between MAU shifts and your initiatives.
Overlay your MAU timeline with key events like campaign launches, feature releases, and pricing changes to identify what’s moving the needle.
This helps you tie outcomes back to actions, making MAU not just a metric, but a diagnostic tool for smarter growth decisions.
Applying MAU Insights Strategically
Tracking MAU for growth is one thing. Using it to drive smarter decisions is another.
Once you understand what your MAU trends reveal, the next step is to turn those insights into action across product, marketing, and strategy.
Inform Feature Prioritization and Product Roadmaps
MAU can be a powerful signal for where your product is (and isn’t) delivering value.
If MAU spikes after a new feature launch, that’s validation. If it drops when a core workflow changes, that’s feedback.
By pairing MAU with engagement data at the feature level, product teams can prioritize improvements that actually move the needle, and confidently de-prioritize those that don’t.
Tie Growth Levers to MAU
MAU often reflects how well your onboarding, reengagement, and retention levers are performing.
If new users aren’t converting into monthly active users, revisit onboarding flows or early activation moments.
If returning users are slipping, test reengagement tactics like reminders, new content drops, or loyalty rewards.
If churn is high, explore lifecycle campaigns that deliver value just before users typically drop off.
Run Incremental Experiments
Think of MAU as a live feedback loop. You can experiment with interventions, like a product notification, a refreshed home screen, or new content, and watch how monthly activity responds.
Because MAU reflects user engagement metrics over a 30-day span, it’s ideal for measuring medium-term impact: not just whether users click once, but whether they return and stick around.
Build Forecasts and Growth Models
As MAU stabilizes, you can use it to model future growth scenarios.
Tracking month-over-month changes and retention cohorts helps you estimate how many active users you’ll have in 3, 6, or 12 months.
This becomes a foundation for revenue projections, staffing plans, and investor discussions.
Report MAU with Context and Caveats
Finally, when reporting MAU to stakeholders, especially investors or leadership, always pair the metric with context.
Share supporting indicators (like DAU/MAU ratio or retention rate) and note any changes in definitions or data collection methods.
MAU is valuable, but without transparency, it can be misinterpreted as raw “growth” when it might reflect seasonality or a data update.
Used thoughtfully, MAU isn’t just a vanity metric: it’s a strategic compass that connects user behavior with business growth, helping teams make decisions grounded in reality rather than instinct.
Example: MAU in Action
Let’s say you’re part of a mid-sized SaaS company that provides an automation tool for marketing teams.
For months, your MAU hovered around 18,000, but in Q2, you noticed a gradual 10% decline even though website traffic and sign-ups remained steady.
At first glance, it was unclear what was driving the drop. So your team dug deeper. You split MAU by cohort; specifically, acquisition channel and signup month.
The data revealed that while organic and referral users stayed consistent, users who came through paid ads in March were disengaging faster than expected. Their activation rate was half that of other cohorts.
That insight pointed to a problem not with your product, but with onboarding alignment.
Paid users were landing on a generic “get started” flow that didn’t match the use cases highlighted in the ads.
Your product and marketing teams collaborated to fix this disconnect. They:
Created targeted onboarding checklists tailored to ad messaging.
Added in-app tooltips and prompts guiding new users through key actions.
Launched a reengagement email series aimed at dormant new signups from that cohort.
Within two months, MAU rebounded past its previous high; not only recovering lost users, but adding 15% growth.
The DAU/MAU ratio also improved, showing that new users weren’t just logging in once, they were returning regularly.
This kind of analysis demonstrates why MAU should never exist in isolation.
When you combine it with cohort data and behavioral metrics, it becomes a diagnostic tool that highlights where engagement is breaking down and where targeted fixes can unlock meaningful growth.
MAU Next Steps & Implementation Prompts
Whether you’re just starting to measure engagement or refining mature analytics, here are a few ways to put MAU into practice right away:
Audit Your Current MAU Definition and Logic
Revisit what “active” means in your context. Are you measuring logins, sessions, or meaningful actions?
Check that your definition reflects genuine engagement, not just surface-level activity, and that it’s applied consistently across tools and teams.
Review MAU Trends Over the Past 6-12 Months
Plot your MAU history alongside major product or marketing milestones.
Look for patterns: Do dips align with feature rollouts? Do spikes follow campaigns?
This exercise helps you separate signal from noise and identify long-term opportunities for growth.
Build or Update Your Engagement Dashboard
Create a centralized dashboard that tracks MAU, DAU/MAU ratio, and key cohort splits (like acquisition channel, region, or tenure).
Seeing these metrics side by side will make it easier to spot retention issues early and respond proactively.
Run One Small Experiment Aimed at Boosting MAU
Try a reengagement campaign, an onboarding tweak, or a feature announcement, and measure its impact on your MAU over a rolling 30-day window.
Small, controlled interventions build your team’s intuition about what actually drives sustained engagement.
MAU works best when it’s part of a continuous feedback loop: measure, test, learn, and repeat.
With the right data hygiene, consistent definitions, and habit of analysis, you’ll turn MAU into a true engine for growth.
Turn MAU Into Meaningful Growth
MAU is more than a number, it’s a narrative.
It tells you who’s showing up, how often, and whether your product is still delivering value where it counts. But like any metric, its power depends on how you use it.
Defining “active” users thoughtfully, tracking MAU alongside complementary metrics like DAU and retention, and digging into cohort behavior all turn surface-level data into actionable insight.
When MAU dips, it’s not just a red flag but a roadmap to where engagement is breaking down.
When MAU climbs, it’s proof that your product is building real, recurring habits.
With consistent measurement, contextual analysis, and a willingness to experiment, MAU can become a compass pointing you toward sustainable, user-driven growth.
Personalization has become the gold standard in modern marketing, and true personalization starts with accurate and effective user segmentation.
User segmentation is the process of dividing your audience into groups based on shared traits, behaviors, or needs.
By understanding what makes each group unique, marketers can move beyond generic messaging and deliver content, offers, and experiences that actually resonate.
So how do you personalize marketing for every audience?
You start by segmenting smartly. When you know who your users are and how they interact with your brand, you can design journeys that feel relevant at every touchpoint, from onboarding flows to loyalty campaigns.
In this article, we’ll explore why marketing segmentation is essential to personalization, the main types of segmentation, and how to build, validate, and activate your segments in practice.
We’ll also look at how to measure performance, learn from results, and keep your segments evolving as your users do.
Soon you’ll have a practical framework for turning segmentation into one of your strongest growth levers, helping you engage, convert, and retain users with greater precision and impact.
Why Segmentation Matters in Personalized Marketing
Most marketing approaches still make the same mistake: treating every user the same way. But “one size fits all” messaging rarely fits anyone.
Generic campaigns overlook the very differences that make users tick (their goals, preferences, and behaviors), leading to low engagement and wasted spend.
Marketing segmentation solves that problem by helping you understand who your users are and what drives them.
By grouping users with shared traits or behaviors, you can tailor your offers, messages, and experiences to meet each group’s needs more precisely.
Instead of sending one broad campaign, you can guide every audience down a path that feels relevant and personal, whether that means re-engaging inactive users, rewarding loyal customers, or nudging high-intent prospects to convert.
The business impact is measurable. Effective audience segmentation leads to:
Higher engagement: Users are 3x more likely to interact when content resonates.
Better conversion rates: Targeted offers outperform generic promotions over 7x.
Smarter resource allocation: Marketing spend goes toward audiences most likely to act.
Lower churn: Users who feel understood are at least 20% more likely to stay.
When done well, audience segmentation transforms marketing from mass communication into meaningful conversation, making personalization not just possible, but scalable.
Types of Segmentations & Use Cases
Not all segmentation strategies serve the same purpose. The right approach depends on your goals, data maturity, and the kind of personalization you want to achieve.
Below are the main segmentation types and when to use each one:
Segmentation Type
What it is
Best for
Demographic Segmentation
Groups users by static traits like age, gender, location, education, or income.
The simplest form of segmentation groups users by static traits like age, gender, location, education, or income. It’s often a starting point when data is limited, helping you establish broad audience filters.
Best for: Awareness campaigns, regional offers, or top-of-funnel messaging where personalization doesn’t rely on behavioral data.
Behavioral Segmentation
Behavioral segmentation looks at what people do, not just who they are, including usage patterns, purchase frequency, feature adoption, and engagement levels. It’s one of the strongest predictors of intent.
Best for: Lifecycle marketing, upsell campaigns, and re-engagement flows based on recent actions.
Psychographic or Attitudinal Segmentation
This approach digs deeper into values, interests, lifestyles, and motivations; the “why” behind behavior. It allows marketers to connect on a more emotional or aspirational level.
Best for: Brand storytelling, content marketing, and campaigns that aim to build affinity or align with user identity.
Technographic or Device Segmentation
In SaaS and digital products, knowing your users’ devices, browsers, and tech sophistication can dramatically improve their experience.
Best for: Tailoring UX, onboarding, or in-app prompts (e.g., showing different tutorials to mobile vs desktop users).
Customer Journey or Stage-Based Segmentation
Users move through distinct lifecycle stages: from new to active, dormant, or loyal. Segmenting by stage helps you meet them where they are.
Best for: Onboarding sequences, win-back campaigns, or loyalty programs designed around lifecycle transitions.
RFM or Value-Based Segmentation
RFM stands for Recency, Frequency, and Monetary value, and it's a classic e-commerce framework that separates high-value customers from occasional or inactive ones.
Best for: Prioritizing retention efforts, VIP rewards, and targeted promotions for your top spenders.
Advanced, Data-Driven, or Hybrid Segmentation
As data sophistication grows, companies can use machine learning, clustering, or lookalike modeling to uncover patterns traditional methods miss. These hybrid approaches often blend behavioral, demographic, and psychographic data for richer insight.
Best for: Large user bases, predictive personalization, or uncovering hidden “power segments” with strong conversion potential.
Microsegments
At the most granular level, microsegmentation targets ultra-specific user groups, sometimes down to the individual. While resource-intensive, it enables near one-to-one personalization.
Best for: High-value customers, enterprise accounts, or personalized product recommendations in advanced marketing setups.
Each method has its place. Demographics are useful for broad targeting; behavioral segments capture intent; psychographics build emotional connection.
The best strategies layer multiple segmentation types to create a unified view of the customer that drives meaningful personalization at scale.
Building & Validating Segments
Once you’ve decided how to segment users for personalization, the next step is making sure those segments actually work.
Good audience segmentation for personalized marketing campaigns is part art, part science. It requires clean data, logical definitions, and continuous validation.
1. Data Inventory & Selection
Start by auditing the data sources you already have: CRM systems, product analytics, web and app events, surveys, and marketing platforms.
Identify which data points are both reliable and meaningful for understanding user behavior.
More data isn’t always better. A few clean, consistent variables (like “last login” or “purchase frequency”) often outperform sprawling datasets with inconsistencies.
2. Segmentation Logic & Rules
Next, define your inclusion and exclusion criteria. For example: “Users with ≥ 3 logins in the past week” or “Accounts that adopted Feature X but haven’t upgraded.”
Clear logic prevents ambiguity later on and ensures that each segment reflects a distinct behavior or trait.
3. Cohort & Overlap Checks
Once segments are built, test them for cohort overlap or collision. You don’t want the same user showing up in multiple, conflicting segments; it muddles insights and campaign delivery.
Use visualization tools or SQL checks to confirm mutual exclusivity where it matters (or document overlaps intentionally if they’re strategic).
4. Size & Statistical Power
A segment must be large enough to analyze and act on.
Tiny segments often lack statistical power, meaning you can’t confidently measure differences or outcomes. If a segment is too small, merge it with others or reframe it around a broader shared behavior.
5. Iteration & Validation
Before rolling out major campaigns, test whether segments actually behave differently.
Run A/B or holdout experiments to see if your “active,” “churn-risk,” or “power user” groups respond distinctly to messaging or offers. If not, refine your definitions.
6. Segment Evolution
User behavior changes, and so should your segments.
Set a refresh cadence (e.g., monthly or quarterly) to reprocess data, retire outdated groups, and add new ones as needed. Dynamic updates ensure your personalization stays accurate and relevant over time.
When done right, segmentation becomes a living framework rather than a static spreadsheet. It evolves as your users do, sharpening the precision of every campaign.
Activating Segments: Personalization in Practice
Once you’ve defined your user groups, the next step is putting those insights into action, tailoring experiences, messaging, and offers in ways that make each interaction feel relevant and timely.
1. Message & Offer Tailoring
Different segments respond to different motivations. Use your segmentation data to craft unique messages, incentives, and CTAs for each audience.
The key is to go beyond light personalization (like “Hi, [Name]”) and align your value proposition to each group’s needs.
2. Journey & Funnel Specialization
Map distinct user journeys for each segment. New users might get a guided onboarding flow, while power users receive product tips or loyalty rewards.
Tailored nurture paths keep users moving forward without overwhelming them with irrelevant content.
3. Channel-Specific Personalization
Different channels lend themselves to different types of segmentation.
Email: Ideal for lifecycle campaigns and personalized offers.
In-app or web: Adjust content or interface in real time based on behavior.
Push or SMS: Best for timely, location- or action-triggered updates.
Align your message format and frequency to where (and how) each segment prefers to engage.
4. Dynamic Content & Smart Rules
Leverage dynamic content blocks and automation rules to serve relevant experiences automatically. For example, show different homepage banners or dashboard recommendations depending on segment membership.
5. Trigger-Based Campaigns
Set up automated triggers that fire when users move between segments. For example, if a user slips from “active” to “inactive,” launch a win-back email or reactivation ad within 24 hours. These behavioral triggers make personalization feel immediate and human.
6. Cross-Channel Consistency
Ensure your segments are mapped consistently across platforms, including your CRM, ad tools, email systems, and analytics dashboards. A user marked “loyal” in one channel shouldn’t feel like a stranger in another.
Consistency builds trust and reinforces brand recognition.
7. Predictive & Real-Time Updates
As your data capabilities evolve, move toward real-time segmentation. Update user status dynamically, from “likely to churn” to “ready to upsell,” so campaigns respond to behavior instantly.
Predictive models can surface these signals early, giving teams time to act before opportunity or engagement is lost.
Measuring & Learning from Segments
Measuring performance helps you understand which segments drive value, which overlap too much, and which should be retired or redefined.
Here’s how to make sure you’re measuring and learning from your user segments:
1. Define Clear KPIs
Start by setting measurable goals for your segmentation efforts. Common metrics include:
Engagement lift: Do segmented campaigns earn higher open or click-through rates?
Conversion lift: Are segmented users converting at higher rates than non-segmented groups?
Retention: Does tailored messaging reduce churn or increase repeat usage?
A/B lift by segment: Are certain groups responding better to specific creative or offers?
Your KPIs should connect directly to business outcomes, not just vanity metrics like impressions or reach.
2. Test with Control Groups and Uplift Modeling
To confirm that segmentation is driving results (and not coincidence), use holdout control groups or uplift modeling.
Compare performance between segmented users and a random unsegmented sample. If the segmented group consistently outperforms the control, you know your targeting logic is adding real value.
3. Monitor Segment Drift
Segments aren’t static. Over time, users’ behavior or attributes change. What’s true of a “power user” today might not be true next quarter.
Track segment drift, or how membership composition shifts over time. If a segment starts to balloon or shrink dramatically, revisit your criteria or data quality.
4. Track Overlap and Cannibalization
Watch for segment overlap, especially when multiple campaigns run simultaneously.
Competing offers to the same users can cause message fatigue or undercut results. Use analytics dashboards or Venn-style overlap reports to spot and reduce collisions between segments.
5. Feed Results Back into the System
Finally, treat segmentation as an iterative process. Feed your findings back into your segmentation logic:
Retire or merge segments that underperform or converge with others.
Refine rules or thresholds based on what predicts engagement or conversion best.
Add new dimensions (behavioral, psychographic, value-based) as your data evolves.
When you measure and iterate, segmentation becomes a self-optimizing loop where every campaign teaches you more about who your users are and what makes them act.
Example: User Segmentation Strategy in Action
Imagine an e-commerce brand specializing in sustainable home goods. The marketing team noticed that overall repeat purchase rates had plateaued, despite steady traffic. They decided to use user segmentation to better understand their customer base and re-energize loyalty.
Step 1: Defining Segments
Using transaction and engagement data, the team segmented users by recency (how recently they purchased) and frequency (how often they buy).
They created four primary groups:
Loyal Shoppers: Purchased multiple times in the last 60 days
At-Risk Customers: Purchased once, but no repeat activity in the past 90 days
Dormant Users: No purchases in over 6 months
Bargain Hunters: Smaller, frequent purchases during sales events
Step 2: Campaign Activation
Each segment received tailored campaigns:
Loyal Shoppers: Early access to new product drops and a “thank you” discount for referrals.
At-Risk Customers: A reactivation email offering free shipping if they returned within 7 days.
Dormant Users: A “we miss you” win-back campaign highlighting bestsellers and social proof.
Bargain Hunters: Personalized bundle offers during upcoming promotions, paired with loyalty reward reminders.
Step 3: Measuring Results
Over a 6-week test:
Loyal Shoppers’ repeat purchase rate rose by 22%.
At-Risk Customers showed a 15% higher reactivation rate versus the control group.
Dormant Users underperformed, but provided insight into message fatigue and timing.
Bargain Hunters generated 30% higher average order value (AOV) when shown bundle offers, a discovery that reshaped the brand’s promotional calendar.
Step 4: Iteration
The team refined their definitions and made the “bargain hunter” segment a more prominent category in their marketing and sales strategy.
They also automated lifecycle triggers so that users moved seamlessly between “at-risk” and “loyal” based on real-time purchase behavior.
This example shows how segmentation isn’t just a way to categorize users, it’s a feedback engine for learning what motivates each audience.
By testing, measuring, and refining, even simple segments can evolve into powerful levers for retention and growth.
Next Steps & Implementation Prompts
Segmentation doesn’t need to be an all-or-nothing overhaul. You can start small, prove value, and build sophistication over time.
Here’s how to turn the concepts from this article into action:
Audit Your Current Segmentation: Take inventory of the segments you’re already using in your CRM, analytics tools, or ad platforms. How many do you have? What rules define them? Mapping your existing logic often reveals redundancies or outdated assumptions.
Re-run a High-Impact Campaign: Pick one campaign that drives real results, like onboarding, reactivation, or upsell, and re-run it using a sharper segmentation approach. Test whether more precise targeting lifts engagement or conversion rates.
Validate with Test vs Control: Always include a control group of unsegmented users. Compare performance to confirm whether segmentation genuinely improves outcomes, not just creates extra complexity.
Build a Segment Performance Dashboard: Use your analytics stack (e.g., Looker, Tableau, HubSpot, or GA4) to visualize metrics by segment. Track key KPIs like open rate, conversion rate, or retention lift to identify your most valuable audiences.
Plan for Continuous Updates: Segments evolve as user behavior changes. Set a quarterly or biannual cadence to revisit definitions, refresh data, and merge or retire segments that no longer perform.
Even simple refinements like a cleaner data model, a few stronger definitions, or a smarter test can make your segmentation strategy far more powerful.
The goal isn’t to create hundreds of microsegments overnight, but to ensure every message feels a little more personal and every campaign performs a little better.
Turn User Segmentation into a Growth Engine
At its core, user segmentation is about understanding people.
When done thoughtfully, it transforms marketing from broad communication into something genuinely personal and adaptive.
The best strategies start simple: define clear segments, test your logic, and let data guide refinement over time.
As your sophistication grows, so does your ability to deliver the right message, to the right person, at the right moment, consistently across every channel.
Segmentation isn’t a one-time project but an ongoing cycle of insight and improvement. Get it right, and it becomes one of the most reliable levers for engagement, conversion, and long-term customer loyalty.
Which campaigns benefit most from C+DV? Prospecting, reach/traffic, and retention by the numbers
On view‑heavy platforms, people often discover you in feed or video and return later via search or direct to purchase.
Clicks-Only records a multi-touch lens, but doesn't account for the entire attribution process.
C+DV (Clicks + Deterministic Views) closes that gap by ingesting verified impression events from participating platforms and matching them one‑to‑one with orders alongside your first‑party click data.
That’s deterministic (factual), not probabilistic modeling, which allows views to share credit with clicks without guesswork.
This article serves as a part companion piece to a webinar I hosted about C+DV. You can see the full recording of the webinar here:
Three Objectives, Three patterns: Who Benefits Most
When you compare Clicks‑Only vs C+DV and cut results by objective, three consistent patterns emerge.
1) Prospecting (top‑of‑funnel) Prospecting campaigns usually gain meaningfully under C+DV. They’re the programs designed to generate discovery, so their impact shows up once verified views can carry their share of credit. Expect many “borderline” prospecting lines (under clicks‑only) to cross into positive territory when you widen the lens to include deterministic views and longer windows.
What to look for:
Campaigns that miss goal on Clicks‑Only, but meet or exceed goal under C+DV—especially when their LTV/longer‑window ROAS strengthens.
First‑time customer contribution: it’s a better read on incrementality for view‑attributable impact.
2) Reach/Traffic “Reach” and “traffic” objectives are the classic under‑credited lines in a click‑centric workflow. Under C+DV, they frequently move from ambiguous to plainly accretive because the model now attributes part of the outcome to verified exposures that didn’t earn an immediate click.
What to look for:
A sharp contrast between Clicks‑Only and C+DV performance for reach/traffic campaigns, with bigger gains as you extend the attribution window (7/14/30‑day).
Stronger lifts on video‑heavy placements versus static‑heavy ones, consistent with how views influence later conversion behavior.
3) Retention can benefit too, but more modestly: it’s typically a mix of genuine influence and exposures to already‑engaged customers. Treat it as a balancing lever, not your primary growth engine. When evaluating retention lift, weight first‑time vs returning customer performance differently to separate incrementality from “already going to buy.”
Why These Gains are Real (and Not “View Inflation”)
C+DV’s credit rules are deliberately conservative: rapid view‑plus‑click pairs within 30 seconds are de‑duplicated (the click wins), clicks retain even weighting, and views are pooled by platform and down‑weighted as the number of views from the same platform rises—preventing “view flooding” from stealing outsized credit. Position in the funnel doesn’t bias the view; it’s the platform‑specific pool size that matters.
What Gives Up Credit, and How to Plan Around It
As top‑of‑funnel gains credit, something else gives. You’ll often see “unattributed” shrink as view‑driven touchpoints fill clickless gaps. You’ll also see last‑click “credit catchers” (most commonly branded search and shopping) cede some conversions they previously absorbed in a Clicks‑Only world. That’s expected and healthy: the model is reallocating to the touchpoints that actually created demand across publishers.
Budget implication: protect and fund prospecting and reach/traffic programs that strengthen under C+DV; trim the bottom‑heavy skew that depended on last‑click over‑attribution.
How to Find Your Objective‑level Winners In Minutes
Here’s a quick workflow you can run every week to identify which campaigns to protect or scale.
Start broad, then narrow
Open the Model Comparison view and flip from Clicks‑Only to C+DV to see directional changes by source. Then, pivot by objective to pinpoint where lift concentrates (prospecting, reach/traffic, retention). This tells you what deserves protection before you dive into line items.
See this clip from the webinar, where I examine the model comparison:
Compare intra‑channel at the campaign level
Within each platform, compare each campaign under Clicks‑Only vs C+DV. Highlight any lines that are weak on clicks but meet/exceed goal under C+DV—especially where LTV/longer‑window performance climbs too. Those are “don’t cut—fund” candidates.
Examining how you can compare different models across channels:
Respect conversion lag on view‑heavy channels
On TikTok in particular, one‑day performance rarely tells the story; it’s common to see large jumps from 1‑day to 14/30‑day under C+DV as view influence materializes. Make 7/14/30‑day comparisons before deciding to scale, hold, or pause.
Triage for Incrementality
When judging prospecting/reach lift, emphasize first‑time customer results; you’ll get a truer read on which upper‑funnel efforts are adding net‑new value.
Concrete Examples (to Anchor the Objective Patterns)
Reach/traffic and prospecting on Pinterest: Omnilux saw a 659% ROAS lift, 728% more transactions, and 671% more revenue on Pinterest when deterministic views were credited versus Clicks‑Only—exactly what you’d expect when awareness objectives get their due. (read the Omnilux case study here)
Prospecting at scale on Meta: Across TubeScience brands, enabling C+DV surfaced view‑driven contribution that clicks‑only missed, with ROAS and revenue rising—e.g., Kitsch +18–25% ROAS lift and +21% revenue lift; Blissy’s blended Meta ROAS improved from 0.82 to 0.92, adding roughly $80K in attributable revenue over the analysis window. These gains align with high‑impression prospecting mixes.
Understanding how C+DV influences credit in across platforms:
Guardrails (So Teams Don’t Misread Objective‑level Lift)
Don’t compare to Clicks + Modeled Views (CMV); it’s probabilistic and includes different credit logic. Use Clicks‑Only as your baseline when assessing objective‑level lift under C+DV.
Expect a “bake‑in” period as C+DV accrues. For objective‑level decisions (especially on Meta/TikTok), rely more on 7/14/30‑day windows and, for early weeks, assess intra‑channel decisions rather than cross‑channel budget shifts.
When evaluating retention lift, segment by first‑time vs returning to avoid over‑reading exposures to existing customers.
Protect Discovery, Scale What Compounds
Prospecting and reach/traffic campaigns are the primary beneficiaries of C+DV because the model finally recognizes verified, cross‑publisher view influence alongside clicks.
Treat the objective‑level lift you see as a map for budget: protect the discovery lines that get stronger under C+DV (and over longer windows), rebalance away from last‑click catchers, and use first‑time customer performance as your north star for incrementality.
That’s how you fund demand creation—not just demand capture—and build a healthier, compounding mix.
To watch the full webinar, click here. Additionally, you can download our guide that explains all you need to know about Clicks + Deterministic Views, written by my colleague, CJ Hunter, and me. Download it for free here.
The Fogg Behavior Model (FBM) is a simple yet powerful framework for understanding why people take action, or don’t.
The model breaks behavior down into three elements: Motivation, Ability, and Prompt. For a behavior to occur, all three must align at the same moment.
By mapping where customers fall on the motivation-ability spectrum, and pairing that insight with the right prompt, teams can design experiences that feel intuitive, engaging, and effective, without relying on guesswork or manipulation.
In this guide, we’ll unpack how the Fogg Behavior Model works, explore the three core elements that drive behavior, and show how to apply the framework across real-world marketing and product scenarios.
You’ll learn how to identify behavioral bottlenecks, optimize prompts, and ethically design for action, complete with a step-by-step guide and practical examples.
Fogg Behavior Model (FBM): Origins & Core Concept
The Fogg Behavior Model (B = MAP) was developed by Dr. BJ Fogg, a researcher and founder of Stanford University’s Behavior Design Lab.
His work explores why people do or don’t take action, and how small shifts in design can lead to big changes in behavior.
At its core, the model states that Behavior happens when three elements converge at the same moment: Motivation, Ability, and a Prompt.
If any one of these is missing or too weak, the behavior won’t occur.
Fogg summarizes this as: B = MAP (Behavior = Motivation × Ability × Prompt)
In other words, even a highly motivated person won’t act if the task feels too difficult, and even the simplest task won’t happen if there’s no motivation or cue to trigger it.
To visualize this relationship, Fogg uses an “action line” plotted on a two-axis chart: Motivation on the vertical axis, Ability on the horizontal.
When a Prompt appears, behaviors that fall above the line are likely to happen; those below the line generally won’t.
This curve shows the dynamic trade-off between motivation and ability: the easier a behavior is, the less motivation it requires, and vice versa.
In marketing, this framework provides a powerful lens for understanding why users click, convert, or abandon a process.
Marketers can use it to craft more effective calls to action (the prompt), reduce friction in the customer journey (increasing ability), or boost emotional resonance and urgency (increasing motivation).
Whether it’s optimizing a signup flow, designing re-engagement emails, or nudging a free user toward upgrade, the Fogg Behavior Model (FBM) helps teams identify which lever to pull, and when, to drive meaningful action.
Deep Dive: Motivation, Ability & Prompts
Understanding how motivation, ability, and prompts influence behavior helps explain why some marketing campaigns convert instantly while others struggle to move the needle.
Each element can be adjusted, like turning a dial, to make the desired behavior more likely.
Motivation
Motivation is the “why” behind behavior; the emotional drive that moves people to act.
According to BJ Fogg, motivation is powered by three core forces:
Sensation (pleasure or pain)
Anticipation (hope or fear)
Belonging (social acceptance or rejection)
These motivators operate on a spectrum from low to high. When motivation is strong, people are more willing to overcome friction; when it’s weak, even the simplest task can feel like too much effort.
Crucially, motivation and ability can trade off: if one is low, the other must rise for the behavior to occur.
In practice:
Sensation might mean creating an experience that feels rewarding, like a dopamine hit from completing a challenge or receiving instant feedback.
Anticipation plays on future benefit or loss: “Sign up today before your trial ends.”
Belonging taps into social proof: “Join 50,000 people already using our product.”
Effective behavior design recognizes which motivator is most relevant for the user and activates it at the right moment.
Ability
If motivation is about wanting to act, ability is about being able to.
Fogg defines ability as simplicity: how easy or frictionless it is to perform the desired action.
He outlines six factors that determine simplicity:
Time: How long the action takes.
Money: What it costs to complete.
Physical effort: The level of exertion required.
Mental effort: The cognitive load or complexity involved.
Social deviance: How much the action conflicts with social norms.
Non-routine: Whether the behavior is unfamiliar or disruptive.
Increasing ability doesn’t necessarily mean teaching users more. It often means removing friction.
That could mean shortening a form, reducing choices, offering autofill options, or breaking a complex process into smaller steps.
In marketing, simplification is one of the fastest levers to pull: make the path to conversion, signup, or engagement effortless, and users need far less motivation to follow through.
Prompts
A Prompt (previously called a trigger) is the cue that tells someone to take action. It’s the “do this now” moment.
Even if motivation and ability are both high, nothing happens without a prompt.
Fogg identifies three main types of prompts:
Facilitator: Used when motivation is high but ability is low. The goal is to make it easier (e.g., “Start with one click” or “We’ll walk you through it”).
Spark: Used when ability is high but motivation is low. The goal is to inspire action (e.g., a message emphasizing urgency or benefit).
Signal: Used when both motivation and ability are high. It’s a simple reminder (“Time to check in” or “Your order is ready”).
Timing and context are everything. A poorly timed prompt, like a push notification during a meeting or an irrelevant ad, won’t work, even if the user is otherwise ready to act.
In digital experiences, prompts often work best in chains: a small prompt leads to a small action, which unlocks another prompt, and so on.
Think of how a “You’ve got new likes” email brings a user back to an app, where new features or notifications encourage deeper engagement.
Over time, these micro-prompts build sustained habits and long-term retention.
Motivation, ability, and prompts rarely operate in isolation.
The real power of the FBMl comes from understanding their interplay: knowing when to boost desire, when to lower friction, and when to deliver the right cue.
Once you can map where your users fall on that spectrum, you can design experiences that feel intuitive rather than forced.
Applying the Fogg Model: Strategy & Use Cases
The Fogg Behavior Model isn’t just a way to understand human behavior, it’s a tool for improving it.
Once you know how motivation, ability, and prompts interact, you can start using that insight to design smarter marketing campaigns, product experiences, and user journeys.
Strategy
A practical way to use the model is to map your users on the motivation-ability grid, with plot motivation on the vertical axis and ability on the horizontal.
Your goal is to identify which behaviors sit above the action line (likely to occur when prompted) and which fall below it (unlikely to happen without intervention).
Each quadrant suggests a different optimization strategy:
High Motivation / Low Ability: Users want to act but find it too hard. Focus on reducing friction: simplify steps, cut form fields, or provide clear instructions.
Low Motivation / High Ability: Users could act but don’t feel compelled. Add emotional or social incentives; urgency, exclusivity, or social proof can provide the needed spark.
Low Motivation / Low Ability: A difficult group to reach. Start with tiny, achievable actions that build momentum and confidence over time.
High Motivation / High Ability: These users are ready and able. The key is to deliver the right prompt at the right time. Think: timely reminders, nudges, or push notifications that reinforce existing intent.
Marketers can use this grid as a diagnostic tool.
Whether you’re refining an onboarding flow, crafting a call-to-action, or designing retention triggers, the model helps pinpoint whether the real issue is motivation, ability, or timing.
Use Cases
Consider these three use cases for applying the Fogg Model in marketing and UX:
Mobile App Conversion: Turning Free Users into Paid Subscribers
A user who loves your app (high motivation) but finds the upgrade process confusing (low ability) may stall before converting.
Here, a facilitator prompt works best. Simplify the steps (“Try premium free for 7 days, cancel anytime”) and highlight how easy the process is.
Email Marketing: Re-Engagement Campaigns
When users have the ability to return but lack motivation, a spark prompt can reignite interest.
Examples include limited-time offers, new feature announcements, or personalized “We miss you” messages that play on curiosity and belonging.
Habit Formation & Retention
To turn one-time users into repeat users, chain smaller prompts that build consistency, such as daily streaks, achievement badges, or contextual reminders (“You’re halfway to your goal!”).
Over time, these micro-prompts move users above the action line by steadily increasing both motivation and ability.
Limitations, Critiques, & Considerations
Like any behavioral framework, the Fogg Behavior Model simplifies reality, and that simplicity is both its strength and its weakness.
While it’s incredibly useful for understanding immediate, observable actions, it doesn’t capture every nuance of human decision-making.
Here are a few known limitations of the Fogg Model:
Exclusion of Outside Factors
One common critique is that the FBM underplays unconscious and environmental factors. Real-world behavior is shaped not only by motivation, ability, and prompts, but also by habits, mood, social context, and external constraints.
A user might ignore a perfectly timed notification simply because they’re distracted, tired, or overwhelmed; variables that the model can’t fully account for.
Single-Behavior Focus
Another limitation is that the model assumes a single moment of behavior: the instant when action happens.
This makes it less suited to long, complex, or multi-step behaviors, such as adopting a new lifestyle habit or changing an organization’s culture.
In those cases, the journey involves multiple prompts, varying levels of motivation, and feedback loops that evolve over time.
Action-Orientation
Similarly, the model struggles to capture deeply reflective or identity-based change, the kind that requires introspection or belief shifts rather than quick behavioral nudges.
Getting someone to click a button is one thing; inspiring them to see themselves differently is another.
Ethical Considerations
Finally, there are important ethical considerations. The same behavioral design principles that make experiences more seamless can also be used to manipulate.
Poorly applied, prompts can exploit users’ attention or emotions, leading to compulsive engagement rather than meaningful interaction.
Marketers using the FBM should therefore aim for transparency, user benefit, and consent, ensuring that their behavior change framework serves the person as much as the product.
Fogg Behavior Model Case Study
Let’s put the model in motion with a simple example: introducing a new feature in a mobile app, like a personal analytics dashboard for tracking user activity.
When the feature launches, the product team notices that adoption is low. Despite strong engagement elsewhere in the app, few users are exploring the new dashboard.
Using the Fogg Behavior Model, the team breaks down the problem into its three components:
Motivation: Users might not understand why the feature matters. It’s new, buried in the menu, and doesn’t yet have an emotional hook.
Ability: The feature requires three steps to access and a short setup process. Manageable, but still enough friction to cause drop-off.
Prompt: The only cue is a small “New Feature” badge in the navigation bar, which is easy to miss and poorly timed.
In the first iteration of the new feature, users rely on curiosity alone. The small badge appears without explanation, and no context is given for the value of the dashboard. Users with low motivation or limited patience never click through.
Applying the Fogg Behavior Model in product design, the team makes three key adjustments:
Increase Motivation: They add a short message: “See your weekly progress in one glance. Track how far you’ve come!” This appeals to both anticipation (future benefit) and sensation (personal satisfaction).
Improve Ability: They simplify setup to one click. The dashboard now loads instantly with pre-filled sample data, reducing both time and mental effort.
Optimize the Prompt: They introduce a spark prompt: a personalized push notification triggered when a user hits a milestone (“You’ve completed 10 sessions. See your progress!”).
Within a week of rollout, feature engagement doubles.
By aligning their motivation, ability, and prompt model, the team moved the desired behavior, new feature exploration, above the action line.
This hypothetical case study highlights how even subtle design changes, when guided by the Fogg Behavior Model, can turn user inertia into meaningful action.
Whether launching a new feature, running a re-engagement campaign, or improving checkout flow, the same principles apply: remove friction, activate emotion, and time the prompt just right.
Actionable Steps For Applying the Fogg Model in Marketing and UX
The beauty of the Fogg Behavior Model is that it’s easy to translate into practice.
Whether you’re optimizing onboarding, running a campaign, or building new features, the framework gives you a systematic way to uncover why users act, or don’t.
Here are five straightforward steps to put it in motion:
1. Pick a Key Behavior to Drive
Start with one specific action you want users to take, like upgrading, sharing, completing a setup, or making a repeat purchase.
Narrowing the focus makes it easier to diagnose what’s blocking progress.
2. Map Motivation and Ability
Assess where your users fall on the motivation-ability grid.
Use data from surveys, interviews, or analytics to understand both their desire (why they would act) and their ease (how simple it feels to act).
3. Identify the Right Lever
Once you know where users sit, decide what needs to shift.
If motivation is low, strengthen emotional appeal or relevance.
If ability is low, simplify the task or reduce effort.
If both are high, focus on timing and delivery of the prompt.
4. Prototype and Test Prompts
Experiment with the three types of prompts (facilitator, spark, and signal) to see which drives the strongest response.
Small changes in wording, timing, or placement can dramatically alter outcomes.
5. Monitor and Iterate
Measure engagement and conversion rates after each change. Track where users drop off and refine accordingly. The FBM works best as a continuous feedback loop, not a one-time fix.
By cycling through these steps, teams can transform abstract behavioral insights into concrete improvements that boost adoption, retention, and satisfaction, without relying on guesswork.
Bringing Behavior Design to Life
The Fogg Behavior Model (FBM) offers more than just a way to explain user actions. It gives marketers, designers, and product teams a framework to shape them responsibly.
By balancing motivation, ability, and prompts, you can move users from hesitation to action with clarity and empathy.
At its best, behavior design is about meeting people where they are, understanding what drives them, and making it easier for them to succeed.
Whether you’re crafting a campaign or optimizing a product flow, Fogg’s simple equation reminds us that meaningful change happens when the right spark meets the right moment.
Winning brands don’t just acquire customers, they guide them.
From the very first touchpoint to long-term loyalty, the most effective marketers understand that growth isn’t a moment, it’s a journey.
Every message, channel, and experience plays a role in shaping that relationship, and how well those moments connect determines whether customers stay engaged or drift away.
This is the essence of lifecycle marketing: a data-informed, stage-based approach to engaging customers from initial awareness all the way to advocacy.
Rather than treating marketing as a funnel that ends with conversion, lifecycle marketing views the customer journey as an ongoing cycle that deepens trust, reinforces value, and turns buyers into champions.
Throughout this guide, we’ll explore how to design strategies that meet customers where they are, with the right mix of insight and intent.
Two measurement frameworks will serve as our foundation: Multi-Touch Attribution (MTA), which offers granular, real-time clarity into digital performance, and Media Mix Modeling (MMM), which provides the high-level, strategic view needed to plan and optimize for durable growth.
What Is Lifecycle Marketing & Why It Matters
Lifecycle marketing is a holistic, ongoing approach to customer engagement that adapts messaging, offers, and experiences to where each person is in their journey.
Rather than treating marketing as a one-time effort to drive acquisition, lifecycle programs nurture relationships over time, from the first touchpoint to the moment a customer becomes a loyal advocate.
The goal is simple: increase activation, retention, and advocacy by meeting people with the right message at the right moment.
This approach matters because acquisition alone is rarely sustainable. Studies consistently show that acquiring a new customer is 5-25x more expensive than retaining an existing one, and that even modest improvements in retention can yield exponential gains in profitability.
Loyal customers also convert at much higher rates (60-70%) than new prospects (5-20%), spend more over time, and are more likely to recommend your brand to others.
In other words, lifecycle marketing doesn’t just keep customers engaged; it compounds value across every interaction.
Measurement Frameworks for Lifecycle Marketing
Measurement frameworks bring this process into focus:
Multi-Touch Attribution (MTA)reveals which specific touchpoints (ads, emails, content, or campaigns) contribute most along the path to conversion, providing near-real-time clarity at the tactical level.
Media Mix Modeling (MMM), on the other hand, looks at the bigger picture: it quantifies how channels work together across both online and offline environments and shows how changes in budget allocation influence long-term results.
Together, MTA and MMM form the analytical backbone of a modern lifecycle strategy, helping teams understand not only what’s working now, but where the next dollar will perform best.
Customer Lifecycle Stages
Every customer journey follows a familiar rhythm: discovery, evaluation, decision, and deepening loyalty.
Lifecycle marketing breaks this journey into five key stages, each requiring its own strategies, messages, and success metrics.
Awareness
Awareness is where it all begins: a potential customer realizes a problem or need and starts exploring possible solutions.
The goal here is visibility: to appear where prospects are looking and establish credibility before competitors do.
Consideration
Consideration follows once customers begin actively comparing options. They’re weighing features, prices, and proof points.
Effective brands provide educational content, case studies, and reassurance that their product is both relevant and reliable.
Conversion (Purchase)
Conversion marks the decision point.
This is where trust and friction collide; where a seamless checkout flow, strong social proof, and clear calls to action make the difference between intent and action.
Retention
Retention starts immediately after the first purchase. It’s about value realization and habit formation: ensuring customers see ongoing benefits, stay engaged, and return repeatedly.
Thoughtful onboarding, personalized communications, and proactive support are essential at this stage.
Advocacy (Loyalty)
Advocacy represents the highest form of customer engagement, when people not only stay but share.
Referrals, reviews, and community participation amplify the impact of every satisfied customer, turning individual experiences into network effects.
Of course, the customer journey isn’t linear. People loop back to earlier customer lifecycle stages, skip steps entirely, or pause midstream.
Multi-Touch Attribution (MTA) reveals which early interactions drive awareness and consideration, and which combinations of touchpoints tend to precede conversion.
Media Mix Modeling (MMM) complements that view by showing how long-term investments in retention and advocacy influence overall revenue performance.
Together, they ensure every stage contributes to sustainable, compounding growth.
Strategies to Engage at Each Stage
Each phase of the customer lifecycle requires a distinct approach. The tactics below outline how to engage customers effectively at every stage and how to measure success using Multi-Touch Attribution (MTA) and Media Mix Modeling (MMM).
Awareness
Goal: Build visibility and credibility as prospects identify their problem or need.
Tactics:
Publish thought-leadership content and SEO-optimized articles.
Leverage social media and PR for category awareness.
Offer light-touch lead magnets such as quizzes, resource guides, or newsletters.
Measurement:
Optimize the customer journey with multi-touch attribution to compare which channels and creatives generate the highest-quality first touches.
Feed performance insights back into content and media planning cycles.
Consideration
Goal: Provide proof, reassurance, and clarity as prospects compare solutions.
Tactics:
Share case studies, testimonials, and comparison guides.
Host webinars or interactive demos.
Use retargeting ads or nurture emails to stay top of mind.
Measurement:
Apply MTA to understand how different combinations of touchpoints (e.g., content -> email -> retargeting) move prospects closer to conversion.
Conversion (Purchase)
Goal: Remove friction and reinforce confidence at the moment of decision.
Deliver timely nudges like cart reminders or limited-time offers.
Measurement:
Use MTA to pinpoint where users drop off and which final interactions drive conversions.
Track assisted conversions, not just last-click results, to capture hidden influence.
Retention
Goal: Strengthen loyalty through ongoing value and engagement.
Tactics:
Build onboarding sequences that accelerate time-to-value.
Trigger lifecycle emails around product usage or milestone events.
Offer loyalty programs, personalized perks, and proactive support.
Measurement:
Use MMM to estimate the incremental lift of retention programs.
Calibrate spend by cohort or segment to optimize ROI.
Advocacy (Loyalty)
Goal: Turn satisfied customers into brand champions.
Tactics:
Launch referral programs and incentives.
Spotlight user-generated content (UGC) or customer success stories.
Encourage reviews and community participation.
Measurement:
Apply MMM to model long-term revenue effects of advocacy.
Compare marginal loyalty investment vs. incremental acquisition spend to find balance.
Tools & Channels for Lifecycle Marketing
Behind every effective lifecycle marketing strategy is a strong operational backbone: the systems, data, and tools that make personalized engagement possible.
These components work together to deliver the right message to the right customer, at the right time, across every stage of the journey.
Data Foundation
Purpose: Build a single, accurate view of the customer to enable precise targeting and measurement.
Key Elements:
Event tracking: Capture key behaviors such as sign-ups, purchases, and feature usage.
Identity resolution: Connect anonymous and known user data across devices and sessions.
Unified profiles: Merge behavioral, transactional, and demographic data to identify lifecycle stage and engagement patterns.
Automation
Purpose: Deliver timely, personalized experiences at scale.
Key Capabilities:
Journey builders: Create dynamic workflows that adapt to user behavior or stage transitions.
Behavioral triggers: Automate responses based on recency, frequency, or inaction.
Segmentation rules: Update audiences automatically as customers progress through the lifecycle.
Channels
Purpose: Communicate consistently across touchpoints while honoring user preferences.
Primary Channels:
Email: Still the most reliable owned channel for retention and loyalty.
In-app & push/SMS: Ideal for real-time engagement and usage reminders.
Paid & retargeting: Re-engage or re-activate customers across platforms.
Website personalization: Adapt content dynamically to match user behavior or intent.
Community & social spaces: Encourage advocacy and deepen connection beyond transactions.
Measurement Frameworks
Purpose: Evaluate performance across both granular and strategic horizons.
Multi-Touch Attribution (MTA):
Tracks user-level interactions across digital touchpoints.
Enables rapid optimization, creative testing, and budget rebalancing within channels.
Media Mix Modeling (MMM):
Analyzes aggregate performance across online and offline media.
Accounts for seasonality, saturation, and external factors.
Guides medium- and long-term budget planning and scenario testing.
Together, these tools and frameworks create a connected ecosystem that captures, automates, and measures every customer interaction throughout the lifecycle.
Implementation Considerations
Even the best lifecycle strategy can fall apart without strong operational discipline.
Successful programs depend on shared definitions, consistent rhythms, and a clear feedback loop between marketing, product, and customer success teams.
Shared Definitions
Clarity starts with language. Every team should agree on what qualifies as a new user, an active user, or a churned user, as well as when customers enter and exit each lifecycle stage.
Standardizing attribution windows and engagement thresholds ensures that everyone is evaluating performance from the same baseline. Without this alignment, teams risk drawing conflicting conclusions from the same data.
Cadence
Set a sustainable rhythm for measurement and decision-making.
Multi-Touch Attribution (MTA) insights work best on a weekly cycle, supporting fast iteration on creative, targeting, and friction fixes.
Media Mix Modeling (MMM) operates at a monthly or quarterly cadence, guiding high-level decisions on budget distribution, channel mix, and seasonal optimization.
This dual timing keeps teams responsive in the short term while steering long-term efficiency.
Most marketers spend hours debating dashboards. What if your data told you in seconds what to scale and what to cut?
That’s the promise of Northbeam’s benchmarks & stoplights. You define what “great” looks like by distilling your most profitable days.
Then your dashboard lights up green, yellow, or red against those standards—so you can scale winners, trim waste, and move on with your day.
No more debating which metric to trust. Just clear signals, grounded in your own best days, that turn data into decisions.
To see the full webinar I did on optimizing your ads, click the video below:
What Are Benchmarks & Stoplights?
Let me keep this simple.
Benchmarks are performance profiles pulled from your most profitable days, defined by you. They’re not averages from the internet—they’re your actual patterns when things go right.
Stoplights are the green/yellow/red signals that compare today’s numbers to those profitable-day profiles, across platform, campaign, ad set, and ad levels. Green says “press the gas.” Yellow says “watch closely.” Red says “ease off.” This cuts through analysis paralysis and gets you acting fast, with confidence.
Watch the following clip for a quick explainer of Benchmarks and Stoplights:
Here’s why this matters: in a multi‑touch world, in‑platform metrics are biased toward their own channels.
Benchmarks and stoplights sit on top of a shared, multi‑touch source of truth, so the signal you get reflects how growth actually happens across channels—not just what one platform claims.
Setting Up Benchmarks
Here’s how I set this up so the signal is clean and actionable.
Step 1: Identify your most profitable days over a meaningful window (I often see 6–12 months). Exclude promo and coupon spikes—you don’t want to benchmark to discount‑driven anomalies you don’t intend to repeat.
Step 2: Segment by new vs. returning customers, by product categories/SKUs, and blended targets. That way, when the stoplight fires, you know if it’s aligned with your objective—acquisition, retention, or blended efficiency.
Step 3: Store those performance profiles as reusable benchmarks. From there, stoplights render against them automatically wherever you work—platforms, campaigns, ad sets, and ads—so the scaling opportunities don’t hide inside a single top‑line number.
💡Pro tip: create saved views tailored to each role. Leadership keeps a high‑level blended view; media buyers get stoplights closer to the money (campaign/ad set/ad) for acquisition vs. retention; creative gets views that help validate concepts and hooks. Shared source of truth, role‑specific clarity.
Reading Stoplights In Action
Here’s the rulebook I run:
Green = Scaling opportunity. If it’s green against the right benchmark (e.g., first‑time ROAS/CAC for acquisition), you can scale with confidence.
Yellow = Caution/monitor. Hold, gather corroborating metrics, and confirm direction before you move budget.
Red = Inefficiency → trim. Reduce spend, re‑allocate, or fix what’s broken (creative, targeting, or landing experience).
Use the Benchmark function to see performance at a glance.
Now, let me address the moment that spooks a lot of teams. In my demo, (which you can see here) you’ll see Meta showing a 0.36 ROAS in Northbeam, while in‑platform showed ~2.7.
With stoplights aligned to the brand’s benchmarks, we could see Meta was actually on track. Not because we’re sugarcoating—because we’re measuring against a multi‑touch, profitable‑day baseline that reflects how the business really makes money.
The quick takeaway: don’t let a scary number force the wrong move. Read it through your benchmarks and stoplights before you decide.
Daily Workflow With Stoplights
I want your daily pass to feel like a repeatable loop you can run forever. Here’s the flow I coach teams to use:
1. Gut Check Your Goal
Are you optimizing for new customer acquisition, reviewing a promo, checking a new category, or validating creative? When you know your goal, you know which stoplights matter most today (first‑time vs. blended vs. returning).
2. Open Your Saved View and Scan Top‑Down
Start at platform/channel stoplights, then drill to campaigns, ad sets, and ads. You’ll spot scale pockets inside an overall yellow, and see waste that’s hiding under a blended green.
In this clip from the webinar, you can see how we quickly scan the performance of ads and adsets as part of our optimization framework.
3. Apply the Scaling Rules
If it’s green against the right benchmark, scale confidently. If it’s red, trim. If it’s yellow, hold and confirm with supporting metrics (CTR, CPM, CPC, conversion rate, new customer percent, revenue per visit). Keep it simple, that’s how you move fast without being reckless.
4. Diagnose with Supporting Metrics
Low CTR, high CPM? Creative/targeting mismatch; fix the angle, hook, or audience.
Strong clicks, weak first‑time conversion? Ad‑to‑landing mismatch—align the promise that earned the click with the page that receives it.
Good conversion, low revenue per visit? Improve the offer—bundles, cross‑sells, post‑purchase upsells, or price testing.
Use red to find friction; use green to fund iteration.
In the webinar, we discussed the kinds of metrics you can review as part of your strategy to optimize your campaigns more effectively, which you can see in the clip here:
5. Re‑Check and Validate
Come back tomorrow or next week, look at the stoplights again, confirm your moves, and repeat. This is how you escape one‑off heroics and build durable performance habits.
Attribution Posture
A quick note on why this works so well with Northbeam’s attribution settings.
I recommend a conservative posture; clicks‑only model on a one‑day window, and accrual reporting (tie revenue to the timestamp of the touchpoint).
This strips inevitable lower‑funnel catchers (brand search, email/SMS, direct) from over‑claiming and pushes credit back to the actual demand generators. It also prevents “false flags” from cash‑mode spikes (like an email send) that weren’t the real driver. Your benchmarks depend on this integrity; your stoplights are only as trustworthy as the accounting underneath.
Conclusion
Benchmarks and stoplights compress decision time and turn your best days into a daily guidance system. You define winning based on reality.
The dashboard lights up with simple, trustworthy signals. And your team moves budget with speed and conviction—scaling what’s working and cutting what isn’t, without the endless debate.
Try Northbeam for yourself. Build the daily loop once, then run it every morning for faster, smarter optimization.
To watch the full webinar, click here. Additionally, you can download our handy guide to keep at your side for when you're optimizing your campaigns, straight from the playbook our media strategists run. Download it for free here.
In today’s competitive landscape, marketing success depends on understanding not just who your customers are but how they behave.
Behavioral data captures the actions and interactions users take across your digital ecosystem, from clicks and scrolls to purchases and feature usage.
Unlike other forms of user data, behavioral data offers a living picture of user intent, engagement, and friction in real time.
This article explores what behavioral data is, where it comes from, and how to analyze it for actionable behavioral marketing insights.
You’ll learn how leading marketing teams use behavioral tracking to personalize experiences, prevent churn, and allocate resources more effectively, all while maintaining ethical, transparent data practices.
Behavioral Data & Its Role in Marketing
Behavioral data captures what users actually do: the clicks, scrolls, page views, sessions, and conversions that map every interaction across your digital ecosystem.
Unlike demographic or firmographic data, which describe who your users are, or attitudinal data, which suggest what they believe, behavioral data shows the truth of how they behave.
This distinction makes behavioral data the most dynamic and revealing signal in modern marketing.
It surfaces user intent, identifies friction points, and uncovers the paths people take on their journey from first touch to loyal customer.
By tracking and interpreting these interactions in real time, teams gain a living, breathing view of their audience that evolves as quickly as user behavior itself.
Types & Sources of Behavioral Data
Behavioral data comes from nearly every point of contact between a user and your brand. Each click, view, or interaction adds another clue about intent, engagement, and value. The strongest insights emerge when you connect these signals across channels.
On-Site and In-App Behavior
On-site and in-app behavioral data include the most immediate actions: page views, clicks, scroll depth, navigation paths, session length, and drop-off points.
These reveal how users explore your site or product and where they hesitate or lose interest.
Engagement with Content and Campaigns
Email opens, ad clicks, video views, and social interactions all reflect how users respond to your messaging, highlighting what resonates and drives follow-up actions across channels.
Transaction and Commerce Behavior
Product views, add-to-cart events, purchases, and abandonments capture commercial intent. These are some of the clearest signals of conversion likelihood and revenue potential.
Feature and Product Usage
Post-purchase behavior offers rich insight into customer value: which features users adopt, how frequently they engage, and which actions correlate with long-term retention or churn risk.
Offline and Cross-Channel Triggers
When available, in-store visits, event check-ins, or location data can connect digital and physical touchpoints, helping marketers form a continuous picture of the customer journey.
Enriched Behavioral Data
Combining multiple signals, like clicks, dwell time, and scroll behavior, reveals engagement intensity and intent.
Adding contextual layers such as time of day, device type, or location helps predict when and how users are most likely to act.
Challenges in Behavioral Data Collection
The more data you collect, the harder it becomes to keep it clean and consistent.
Duplicate identities, bot traffic, missing data, and cross-device fragmentation can distort insights and weaken decision-making. Investing in strong data governance and identity resolution is essential for accuracy and trust.
How to Analyze Behavioral Marketing Insights
Collecting behavioral data is only the first step. The real value comes from transforming raw signals into patterns, predictions, and action.
By analyzing how users move through your product or campaigns, marketers can uncover what drives growth, what slows it down, and where to focus next.
Here’s how to use behavioral data in marketing:
Behavioral Segmentation
Not all users engage in the same way.
Grouping users by behavior patterns, such as frequent users, dormant users, or high-intent browsers, helps tailor experiences to each group’s needs.
According to Growth-Onomics, campaigns built on behavioral segmentation can achieve 10–30% higher conversions compared to one-size-fits-all messaging.
Funnel and Path Analysis
Mapping how users progress through your funnel reveals where they succeed and where they drop off.
By examining navigation paths, session sequences, and exit points, teams can identify friction zones and optimize the steps that lead to higher completion or purchase rates.
Cohort and Retention Analysis
Cohort analysis compares groups of users who share a common trait, such as acquisition date or campaign source, to see how engagement and retention evolve over time.
It’s a powerful way to measure the long-term impact of marketing decisions and uncover which channels or features sustain loyalty.
Predictive Modeling and Propensity Scoring
By applying machine learning or statistical models, marketers can use behavioral signals to predict what users are likely to do next: whether they’ll churn, re-engage, or upgrade.
These predictive insights allow teams to prioritize outreach and personalize timing with precision.
Anomaly Detection and Trend Shifts
Sudden changes in user behavior often serve as early warnings.
A spike in drop-offs or a dip in feature usage can signal a broken experience, performance issue, or shift in audience interest.
Automated monitoring helps teams respond before small issues become widespread problems.
Behavioral Triggers and Automation
Once you understand behavior patterns, you can act on them in real time.
Set up triggers, like cart abandonment emails, onboarding nudges, or personalized homepage experiences, that respond automatically to user behavior.
This turns insights into impact, driving engagement while reducing manual effort.
Applications: Driving Growth with Behavioral Data
Once behavioral insights are in place, marketers can turn them into concrete strategies that drive growth, retention, and efficiency.
By understanding how users act, not just who they are, teams can personalize experiences, refine journeys, and optimize spend based on what actually moves the needle.
Personalization and Content Targeting
Behavioral data powers personalization that feels natural, not forced.
By analyzing browsing history, click behavior, and content preferences, marketers can tailor offers, recommendations, or creative assets to each user’s demonstrated interests.
When personalization reflects real actions rather than assumptions, it builds trust and boosts engagement.
Retargeting and Remarketing
Not every interaction ends in conversion, but behavioral retargeting helps close the loop.
Dynamic ad creative can automatically show users the products or services they viewed but didn’t purchase, keeping intent alive across channels.
When tied to behavioral thresholds (like time since last visit or cart activity), these campaigns turn lost opportunities into recoverable value.
Onboarding and Activation Flows
Early behavior is one of the strongest predictors of long-term success.
Monitoring how new users engage (which features they try, where they get stuck, or what they skip) allows teams to guide them toward key “aha” moments faster.
Adaptive onboarding sequences, in-app tips, and behavior-based nudges all help accelerate activation.
Retention and Churn Prevention
Declining activity patterns can reveal churn risk before it happens.
By tracking metrics like session frequency, feature use, or content engagement, marketers can flag at-risk users and proactively re-engage them with reminders, loyalty offers, or personalized outreach.
A small, timely intervention often saves a customer relationship.
Optimizing User Journeys and Removing Friction
When users repeatedly drop off or hesitate in specific areas, it’s often a sign of friction, whether that’s due to unclear messaging, broken UX, or irrelevant steps.
Funnel and path data highlight these pain points so teams can refine copy, simplify navigation, and reduce cognitive load, improving both satisfaction and conversion rates.
Resource Allocation and Campaign Optimization
Not all engagement is created equal. User behavior analytics show which segments or campaigns deliver the strongest downstream impact, including higher conversions, longer retention, or greater lifetime value.
With these insights, marketing teams can shift budget and focus toward the behaviors and audiences that drive the most growth.
In short, behavioral data doesn’t just describe the customer journey, it actively guides how brands shape it in real time.
Ethical Considerations & Data Governance
As behavioral data becomes more sophisticated, so does the responsibility to handle it ethically. Users expect transparency, control, and respect, and brands that deliver on those expectations build lasting trust and loyalty.
Privacy, Consent, and Transparency
Every interaction tracked should come with clear communication about how data is collected and used.
Transparent consent mechanisms, from cookie banners to preference centers, empower users to make informed choices and strengthen brand credibility in the process.
Anonymization and Data Protection
Even when consent is granted, identifiable data should be handled carefully.
Techniques like anonymization, de-identification, and aggregation help preserve analytical value while protecting user privacy.
The goal is to understand patterns, not individuals.
Regulatory Compliance
Compliance with privacy laws such as the GDPR, CCPA, and emerging regional regulations isn’t just a legal box to check, it’s a marketing advantage.
Companies that embed ethical data handling into their operations earn consumer trust and reduce long-term risk.
Transparency and accountability are core to sustaining user relationships.
Balancing Utility and Intrusion
Personalization should enhance the customer experience, not overstep it. Behavioral insights can quickly become invasive if they feel manipulative or overly precise.
Striking the right balance means using data to serve user needs, not just brand goals.
Governance and Data Quality
Good data governance ensures that behavioral insights remain accurate, representative, and fair. That means auditing data sources, correcting bias or sample skew, and validating inferences before acting on them.
Behavioral data is only as reliable as the systems and standards that manage it.
When used responsibly, behavioral data deepens relationships rather than exploiting them, allowing marketers to deliver relevance with integrity, and innovation with accountability.
[Example] Behavioral Data in Marketing
To see data in action, let’s look at how a SaaS company might use it to boost user retention and reverse churn through behavior-based interventions.
1. Detecting the Signal
User behavior analytics reveal that users who stop using Feature X within their first two weeks are 60% more likely to churn by Month 2.
The company sets up a dashboard to monitor feature usage daily and flags users whose engagement drops below a set threshold.
2. Segmenting the Audience
Two segments are defined:
Healthy users: actively using Feature X multiple times per week.
At-risk users: engaged early but showing declining or zero activity after Week 1.
3. Designing the Intervention
The marketing and product teams collaborate to create targeted nudges for the at-risk group:
An automated email highlighting how top users benefit from Feature X.
A short in-app tutorial that reintroduces the feature’s value and provides an incentive to try it again.
4. Acting on the Behavior
These messages are triggered dynamically: as soon as a user meets the “at-risk” criteria, the system deploys the re-engagement sequence automatically.
5. Measuring the Impact
After 30 days, the team compares cohorts: users who received the nudge show a 22% higher retention rate and a 35% higher likelihood of upgrading to paid plans compared to the control group.
Why It Works
By connecting usage data with automated triggers, the company transformed passive observation into proactive engagement.
Instead of reacting to churn after it happens, they used behavioral insights to predict and prevent it, turning early warning signs into growth opportunities.
Behavioral data isn’t just diagnostic; it’s prescriptive. When used thoughtfully, it closes the gap between insight and action, allowing marketers to intervene at precisely the right moment to change the outcome.
Implementation Prompts & Next Steps
Using behavioral analytics in your marketing strategy doesn’t require an overhaul, just a few focused steps to align teams, tools, and tactics. The goal is to start small, prove value, and scale as insights compound.
1. Audit Your Data Collection
Start by mapping out where and how behavioral data currently flows through your systems.
List key touch points (website interactions, app usage, campaign engagement, purchase events) and identify what’s missing.
Clean, comprehensive tracking is the foundation for every insight that follows.
2. Define High-Value Behavioral Signals
Choose two or three metrics that directly tie to growth goals, such as feature engagement, repeat logins, or cart abandonment rate.
Instrument these carefully so you can measure changes in real time and tie them to outcomes like retention or conversion.
3. Build Behavior-Based Segments
Group users by observable actions rather than static traits.
For example:
Group A: frequent users or buyers
Group B: new but inactive users
Group C: churn-risk users showing declining engagement
Launch one tailored campaign for each to validate whether different behaviors truly require different approaches.
4. Set Up Dashboards and Alerts
Use user behavior analytics tools to visualize and monitor trends across cohorts, funnels, and events.
Configure alerts to flag anomalies like sudden drops in activity, usage spikes, or changes in conversion behavior so you can investigate and act quickly.
5. Run Iterative Experiments
Pick one high-impact behavior, such as a checkout drop-off point or low engagement feature, and test a single change like new messaging, a simplified UI, or a retargeting email.
Measure, learn, and repeat. Behavioral insights improve exponentially when paired with experimentation.
Small, systematic improvements like these create a feedback loop between marketing and user behavior, helping teams stay agile, data-informed, and one step ahead of customer needs.
From Insights to Action: The Power of Behavioral Data
Behavioral data turns guesswork into guidance. By observing what users actually do, not just what they say or who they are, marketers gain a real-time window into intent, friction, and opportunity.
From segmentation and personalization to churn prevention and journey optimization, these insights transform every touchpoint into a learning loop.
When paired with ethical data practices and smart experimentation, tracking user behavior for growth can also improve the customer experience.
The takeaway is simple: behavioral data is most valuable when it drives action. Start small, stay curious, and let your users’ behavior lead the way toward sustainable growth.
The best growth teams don’t just look at averages, they study how groups of customers behave over time.
Averages might tell you that retention is holding steady, but they won’t reveal that your most recent customers are churning faster, or that last quarter’s cohort is spending more slowly than the one before.
Cohort analysis groups customers by a shared characteristic, and then tracks how those groups evolve across consistent time intervals.
By comparing outcomes like retention, engagement, or revenue period over period, you uncover patterns that aggregate metrics often obscure.
In this guide, you’ll learn:
When to use acquisition vs. behavioral cohorts
How to interpret cohort tables and retention analysis curves
Which questions cohorts answer better than traditional segmentation
How to apply cohort insights to messaging, lifecycle programs, product nudges, and budget allocation
What Is Cohort Analysis & Why It Matters
Cohort analysis is the practice of grouping customers who share a meaningful trait at a specific point in time.
For example, you may group together everyone who made their first purchase in April, and then track how their behavior unfolds in consistent intervals (days, weeks, or months since that first purchase).
Basically, instead of looking at broad averages or segments, you’re watching how groups move over time.
Cohort analysis vs segmentation:
A segment is a snapshot: “high-value shoppers” or “subscribers who opened last week’s email.”
A cohort is (usually) a time series: “customers acquired in Q2” or “users who completed onboarding within 7 days.”
With cohorts, you’re not just asking who they are but how their behavior evolves; whether they ramp up quickly, form lasting habits, or drop off early.
From a business perspective, this shift in lens unlocks several advantages:
Distinguish acquisition from retention. Cohorts clarify whether growth is driven by bringing in more customers or by getting existing ones to stick around longer.
Spot product-market fit signals. If newer cohorts retain better than older ones, it suggests your product or marketing changes are hitting the mark.
Guide lifecycle investments. Cohorts reveal exactly where drop-offs occur, whether that’s during onboarding, in months 2-3, or after a promotion ends.
Enable realistic lifetime value cohort forecasting. Instead of relying on blended averages, you can model spend and churn analysis by cohort to get more accurate projections.
Finally, cohorts make experimentation cleaner.
When you launch a change (say, a new onboarding flow or pricing strategy) you can measure its impact directly by comparing post-change cohorts against earlier baselines, while controlling for time-based effects like seasonality.
The Main Cohort Types
Not all cohorts are created equal. Depending on your goals, you might group customers by when they first arrived, what they did inside your product, or even their predicted likelihood to churn.
The three most common (and useful) types are:
1. Acquisition Cohorts (Time-Based)
These cohorts group customers by when they first interacted with your brand, whether that’s initial signup, first purchase, or first touchpoint.
They help answer questions like:
Are our newest customers retaining better than earlier ones?
Do promotion-driven cohorts lapse faster?
Which acquisition months generated the strongest downstream lifetime value cohorts?
2. Behavioral Cohorts (Event-Based)
Here, customers are grouped by milestones or actions, such as:
Completing onboarding within seven days
Using a feature three times in their first month
Making two or more purchases within 30 days
Opting into SMS updates
These cohorts reveal which behaviors correlate with long-term value and which nudges can accelerate customers toward their “aha” moment.
3. Predictive Cohorts (Model-Based)
Predictive cohorts use machine learning or rules-based models to group customers by their likelihood of future outcomes.
For example, using cohorts to forecast LTV, churn risk, or upsell readiness.
They’re particularly powerful for targeting high-impact interventions like retention offers, VIP perks, or win-back campaigns.
Where Cohorts Shine
Across all three types, cohorts provide sharper insights than averages:
Retention: Visualize survival curves to spot “cliff” periods where customers fall away.
Monetization: Track cumulative revenue to see payback periods and the lasting impact (or decay) of promotions.
Channel quality: Compare downstream metrics for customers acquired from different campaigns or offers, without relying on blunt last-click logic.
Non-Linearity and Overlap
But keep in mind that customers aren’t static: they can move between behaviors and risk levels.
That’s why cohort definitions should be consistent but flexible enough to reflect product changes. For instance, an onboarding redesign might shift which behaviors best predict retention.
How to Read a Cohort Table
Cohort tables are the bread and butter of cohort analysis, but they can feel intimidating at first glance.
Once you understand the structure, though, they become one of the most powerful ways to visualize customer behavior.
Month
Month 1
Month 2
Month 3
Month 4
Month 5
Month 6
January Cohort
100%
50%
40%
35%
30%
25%
February Cohort
100%
65%
50%
40%
35%
30%
March Cohort
100%
70%
65%
50%
40%
35%
April Cohort
100%
80%
70%
65%
50%
40%
Structure:
Rows represent cohorts; for example, everyone who made their first purchase in January, February, March, and so on.
Columns represent intervals since the anchor event, such as Week 0, Week 1, Week 2 or Month 1, Month 2, Month 3.
Cells contain the metric you’re tracking; most often retention (active rate), repeat purchase rate, or revenue per user.
This layout allows you to compare how different cohorts behave over time, and spot patterns that would be invisible in an average.
Patterns to look for:
Diagonal trends: If newer cohorts consistently perform better (or worse) than older ones, you may be seeing the impact of product improvements or acquisition channel shifts.
Early-life retention: The first 1-2 intervals are often make-or-break. A steep fade here usually points to onboarding friction.
Promotion fingerprints: Sharp spikes followed by rapid drop-off suggest discount-driven behavior rather than habit formation.
Plateaus vs. compounding: Flat cumulative revenue per cohort means limited expansion, while stair-stepped growth indicates that cross-sell or upsell strategies are working.
Normalization & denominators:
Consistency is critical. Always keep denominators the same (e.g., percent of the original cohort).
When analyzing revenue, use per-user or cumulative metrics to control for cohort size differences.
This ensures that small and large cohorts can be compared on equal footing.
Strategies & Plays Driven by Cohorts
Cohort analysis isn’t just about spotting patterns. It’s about using those patterns to shape smarter acquisition, activation, and retention strategies.
Here are some of the most effective plays for cohort analysis for ecommerce marketers.
Acquisition Quality Plays
Not all channels or campaigns produce customers of equal value. Cohorts let you compare retention, spend, and payback by acquisition source.
Identify which campaigns generate customers who stick around for 90+ days and reduce spend on those that churn early, even if the upfront CPA looks efficient.
Spot seasonal dynamics, such as holiday buyers who purchase once and vanish, and design post-purchase sequences tailored to those groups.
Onboarding Acceleration
The first days or weeks often determine whether a customer becomes long-term. Behavioral cohorts highlight who reaches critical milestones and who doesn’t.
Define cohorts around “aha” actions (e.g., creating a project, adding a payment method, completing a profile).
Use guided tours, checklists, or nudges to help lagging customers reach those milestones.
Track retention analysis curves to see how accelerating onboarding changes long-run value.
Monetization and Expansion
Cohorts also reveal where growth opportunities lie beyond the first transaction.
Create cohorts for customers who try a new product line, hit an AOV threshold, or upgrade to a subscription.
Test time-sensitive offers (like encouraging a second purchase within 21–30 days) and compare repeat rates against prior cohorts.
Monitor cumulative revenue per cohort to measure lift from cross-sell, bundling, or loyalty programs.
Churn Risk and Win-Back
Cohorts segmented by inactivity or churn risk help you prioritize interventions.
Define risk cohorts such as “no sessions in 14 days” or “no purchases in 60 days.”
Start with light-touch re-engagement (reminders, testimonials, social proof) before resorting to discounts.
Measure success by the reactivation rate and the retention curve of reactivated cohorts.
Pricing & Offer Tests
Because cohorts are anchored in time, they’re perfect for evaluating pricing and packaging changes.
Compare cohorts exposed to different price points or trial lengths.
Look beyond aggregate revenue to see whether short-term boosts come at the expense of long-term retention or payback.
B2C vs. B2B Nuances
B2C cohorts often emphasize purchase frequency, promotional impact, and early repeat behavior.
B2B cohorts focus more on behavior milestones, like inviting teammates or installing integrations, that predict renewal likelihood.
Tools, Data, and Setup
Cohort analysis only works if the data foundation is solid. Before you can run sophisticated plays, you need consistent tracking, clear definitions, and the right workflow in place.
Data Foundation
Event tracking: Use clean, consistent naming for your anchor event (e.g., first purchase, signup) and any key milestones. Ambiguity here leads to messy cohorts.
Identity resolution: Stitch sessions, devices, and channels into unified profiles so you can follow a customer’s journey across touchpoints.
Time buckets: Choose clear intervals (weekly or monthly) that align with your buying cycle. For fast-moving eCommerce, weekly may be best; for subscription businesses, monthly is often more telling.
Definitions to Standardize
Without standardized definitions, cohort metrics quickly become unreliable. Make sure your team aligns on:
Cohort anchors: What starts the clock: first purchase, signup, or another trigger?
Inclusion rules: Who counts as “new” versus “returning”?
Inactivity windows & reactivation criteria: At what point does a customer count as churned, and when do they re-enter?
Metric definitions: What exactly is an “active” or “retained” user? Are values measured per user or per account?
Workflow & Governance
A repeatable workflow keeps cohorts actionable rather than academic.
Instrument events
Validate data quality
Establish baseline cohorts
Launch experiments
Read results on new cohorts
Iterate
Add guardrails to avoid common pitfalls:
Use sampling checks so small cohorts don’t produce noisy conclusions.
Respect customer experience with frequency caps and preference centers when triggering cohort-based messaging.
Tooling Landscape (Vendor-Neutral)
You don’t need a specific platform to get started, but you do need the right categories of tools:
Web/app analytics: For basic cohort tables and explorations. For example, consider a GA4 cohort exploration for retention.
Product analytics: For behavioral and predictive cohorts, consider product analytics cohort playbooks with the ability to overlay charts by segment.
Marketing automation/journey builders: To act on cohorts in real time. For example, nudging “users who didn’t reach a given milestone by Day 7.”
Cohorts in Measurement & Forecasting
Cohort analysis isn’t just for diagnosing problems. It’s a powerful tool for measurement, forecasting, and communicating results at the executive level.
By anchoring customer behavior over time, cohorts give you cleaner comparisons and more reliable projections than averages ever could.
Experimentation
When you launch a new initiative, whether it’s a pricing test, onboarding flow, or marketing offer, cohorts create a natural before-and-after view.
By anchoring on the launch date, you can compare users acquired after the change to earlier cohorts, isolating its impact while controlling for seasonality and other time-based effects.
Attribution Complement
Attribution models help answer how customers converted by mapping the path of touches along the journey.
Cohorts answer what happens after: do those customers stick, grow, and pay back?
Together, attribution and cohorts provide a full picture: one approach for acquisition efficiency, the other for long-term value.
Forecasting and Budgeting
Cohort-level LTV curves and payback charts let you project revenue more realistically.
Instead of relying on blended averages, you can model how specific cohorts spend and churn over time.
This prevents a common pitfall: over-investing in channels that look cheap upfront but produce weak, short-lived customers.
Board-Level Storytelling
Cohort visuals also resonate at the executive and board level.
A single chart showing cumulative revenue by cohort versus CAC can illustrate improving unit economics, or highlight when acquisition quality has slipped.
It’s a clear way to demonstrate progress, justify budget allocation, and keep stakeholders aligned.
Implementation Considerations
Even the best cohort strategy can fall apart without the right cadence, metrics, and governance.
To keep insights consistent and actionable, teams should align on how often to review cohorts, which KPIs to track, and how to avoid common pitfalls.
Cadence
Weekly: Focus on early-life retention and activation milestones; the critical leading indicators of long-term value.
Monthly: Review cumulative revenue, payback trends, and emerging differences across acquisition sources.
Quarterly: Reassess cohort anchors, behavior definitions, and predictive features to ensure they still reflect how customers interact with your product.
KPIs by Objective
Acquire: 30/60/90-day active rate, first-to-second purchase conversion, time to second purchase.
Activate: “Aha” milestone completion rate by Day X, median time to value.
Monetize: Cumulative revenue per user, attach/expansion rate, average orders per user by interval.
Retain: Survival or retention rates at key intervals (e.g., Month 3, Month 6), churn analysis by risk cohort.
Win-back: Reactivation rate and post-reactivation retention curves.
Common Pitfalls
Overfitting: Small cohorts can produce noisy data; smooth trends before making decisions.
Shifting definitions: Changing what counts as “active” or “retained” mid-stream without backfilling history breaks comparability.
Promo distortion: Don’t mistake short-lived spikes from discounts or promos as durable gains.
Denominator drift: Watch for exclusions (like refunds or cancellations) that alter the base and skew results.
Change Management
Keep everyone on the same page with clear documentation and governance.
A simple one-pager listing definitions, SQL/event names, and links to dashboards helps teams avoid confusion and ensures that cohort metrics remain consistent across the organization.
The Power of Cohorts
Cohort analysis shifts marketing from static averages to dynamic trajectories.
By anchoring on meaningful events and tracking how outcomes unfold over time, you can see exactly where value is created (or lost) and tie your interventions to measurable improvements in retention, monetization, and payback.
The playbook is simple but powerful: start with crisp definitions, set consistent intervals, and build a baseline dashboard. From there, run targeted experiments, measure how new cohorts respond compared to prior ones, and keep iterating.
The reward is more than just sharper insights: it’s a durable growth system.
Cohorts give you the confidence to double down on channels and behaviors that create lasting value, while avoiding the trap of chasing short-term spikes that fade away just as quickly.
The products that thrive aren’t just the ones people try once, they’re the ones people return to again and again.
Habitual usage is the strongest signal of loyalty, product market fit, and long-term growth.
If your app, platform, or tool becomes part of a user’s daily rhythm, you’ve built something far more powerful than short-term popularity.
User stickiness, defined simply as the ratio of Daily Active Users (DAU) to Monthly Active Users (MAU), measures how often your customers engage with your product on a regular basis.
A higher percentage signals stronger habitual engagement and a greater likelihood that your product will retain users, monetize effectively, and scale sustainably.
In this article, we’ll break down exactly what user stickiness is, why it matters, how to measure user stickiness, and proven strategies to improve it. We’ll also cover implementation tips to ensure your data is accurate and actionable.
By the end, you’ll have a clear playbook for making your product not just popular, but sticky.
What Is User Stickiness
At its simplest, user stickiness is a way of measuring how often your customers keep coming back.
Stickiness vs. Retention
As we define this key metric, it’s important to distinguish stickiness from retention.
Retention shows whether a user keeps coming back over time; think of it as continuity. Stickiness, on the other hand, captures frequency: how regularly users engage once they’ve signed up.
A product can have strong retention but low stickiness if people only log in occasionally, or high stickiness but poor retention if usage is intense but short-lived.
Together, these two metrics tell a more complete story of engagement.
Why User Stickiness Matters
From a business perspective, stickiness is powerful because frequent engagement compounds value.
Users who return daily or weekly are more likely to:
Stay loyal and less likely to churn.
Discover and use more features, which increases monetization opportunities.
Deliver better ROI on acquisition spend, since converting them yields longer-term payoff.
In other words, stickiness doesn’t just show how popular your product is, it shows whether it’s embedded in your users’ routines, which is the real driver of sustainable growth.
How to Measure User Stickiness
The most widely used way to measure user stickiness is through the Daily Average Users (DAU) / Monthly Average Users (MAU) ratio, also known as the DAU/MAU stickiness ratio.
Stickiness ratio = DAU ÷ MAU
For example, if you have 5,000 DAU and 20,000 MAU, your stickiness is 25%. That means one in four of your monthly users engage with your product on a daily basis, a strong sign of habitual usage.
While DAU/MAU is the standard, it’s not the only option. Depending on your product and usage cycle, you may want to track:
WAU/MAU (Weekly Active Users ÷ Monthly Active Users) to capture weekly engagement patterns.
Cohort analysis, which groups users by signup date or acquisition channel and tracks how their stickiness changes over time. This can help you see whether new features or campaigns improve frequency of use.
To track these ratios, most teams rely on a mix of analytics and business intelligence (BI) tools.
Popular options include:
Google Analytics 4 (GA4) for top-level reporting.
Custom dashboards that integrate DAU and MAU data from multiple sources.
BI platforms like Looker, Tableau, or Mode for deeper analysis and visualization.
The key is to choose a measurement approach that reflects your product’s natural usage cadence and provides consistent, comparable data over time.
Stickiness Benchmarks By App Type
So what counts as “good” stickiness? The answer depends heavily on your industry.
Different types of apps naturally invite different usage cadences:
Social and messaging apps: 50-80%. Users often check in multiple times per day, so these products typically see the highest stickiness ratios.
Productivity tools:40–60%. Daily work habits drive strong engagement, but usage may taper on weekends or holidays.
Fintech and e-commerce apps: 15–30%. People don’t manage finances or shop every day, so stickiness is lower, but still meaningful when compared to peers.
These ranges offer helpful context, but it’s important not to treat them as hard rules. Upward movement in your own numbers is usually more valuable than hitting an external target.
For example, a fintech app with a stickiness rate growing from 12% to 20% is often in a stronger position than one stuck at 25%.
To make benchmarks actionable, start by establishing your internal baseline (what’s typical for your product today) and then measure progress against that.
External comparisons should serve as guardrails, not goals, to help you calibrate expectations and spot opportunities for improvement.
Key Drivers of User Stickiness
If stickiness shows how deeply your product is woven into users’ routines, the natural question is: what makes people come back again and again?
While every product is different, a few common drivers tend to make the biggest impact:
Onboarding Clarity and Speed
First impressions matter.
A confusing or drawn-out onboarding flow can derail new users before they ever form a habit. Quick wins and early exposure to value are critical.
Performance and UX Reliability
People won’t return if your product is slow, buggy, or unintuitive. Smooth, reliable performance is table stakes for building trust and repeat usage.
Relevant Personalization
Customized recommendations, tailored dashboards, or context-aware reminders help your product feel indispensable rather than generic.
Habit-Forming Features
Streaks, reminders, daily dashboards, or other lightweight nudges create rhythms that encourage users to return at predictable intervals.
Value-Rich Content or Functionality
Ultimately, people stick around for utility. Whether it’s fresh content, powerful features, or unique insights, the core value needs to keep evolving.
Feedback Loops and Iteration
Products that improve based on user feedback reinforce the sense that engagement leads to a better experience, which in turn drives more engagement.
Together, these drivers create a reinforcing cycle: onboarding unlocks early value, performance and personalization sustain it, and habit-forming features plus ongoing improvements deepen loyalty over time.
Strategies to Boost Stickiness
Once you know where your product stands, the next step is figuring out how to move the needle.
Improving user stickiness isn’t about one big initiative. It’s about layering small, targeted improvements that make your product more engaging over time.
Here are some of the most effective tactics:
Segment Your Users
Not all customers behave the same way. Segmenting by behavior, persona, or lifecycle stage allows you to tailor messaging, features, and nudges for maximum relevance.
Streamline Onboarding
Help new users find value fast. Highlight “aha” moments early so they form a habit before friction or confusion sets in.
Personalize Content and Reminders
Personalize content and reminders – Use behavioral data to deliver timely notifications, recommendations, or updates that feel helpful rather than intrusive.
Nudge Feature Usage
Tooltips, badges, and progressive disclosure can gently introduce users to underutilized features that deepen engagement.
Act On User Feedback
Monitor drop-off points, support tickets, and surveys to identify what frustrates users most and fix it quickly.
Run Experiments
Test variations of messaging, workflows, or features to see what improves frequency of use. Even small tweaks can make a measurable difference.
Track and Iterate
Stickiness gains often come from compounding improvements. Keep measuring, learning, and refining to sustain progress.
Think of these as building blocks: segmentation helps you understand who you’re serving, onboarding gets them to value, personalization and nudges keep them coming back, and feedback plus experimentation ensure your product keeps evolving in step with user needs.
Implementation Considerations
Measuring stickiness may seem straightforward, but the way you implement it can make the difference between meaningful insight and misleading numbers.
A few key considerations help ensure accuracy and actionability:
Consistent Definitions
Align on what counts as a Daily Active User (DAU) and Monthly Active User (MAU) across all analytics tools.
If one platform defines “active” as any login while another requires a completed action, your ratios will be impossible to compare.
Seasonality and Campaign Effects
Expect spikes around holidays, launches, or major campaigns. Build in context when interpreting changes so you don’t mistake a temporary surge for a lasting trend.
Segment-Level Analysis
Stickiness isn’t one-size-fits-all. Break it down by cohort, persona, or acquisition channel to see which groups are forming habits, and which ones aren’t.
This can reveal whether your product resonates more strongly with certain audiences.
Complementary Metrics
Stickiness is powerful on its own, but it’s most useful when paired with retention, activation, and lifetime value (LTV).
Together, these metrics show not only how often users engage, but also whether they stick around, find value, and ultimately contribute to growth.
Handled carefully, stickiness becomes more than a single ratio. It becomes a practical lens for understanding user behavior and driving smarter decisions.
Bringing It All Together
User stickiness is more than just a ratio. It’s one of the clearest signals of engagement, product-market fit, and long-term growth potential.
By measuring how often people return, you gain insight into whether your product is truly becoming part of their daily or weekly routines.
The good news: stickiness isn’t fixed.
With consistent measurement, thoughtful experimentation, and a commitment to iteration, you can steadily strengthen the habits that keep users coming back.
At the end of the day, popularity fades, but habits endure.
The most resilient products aren’t the most popular ones. They’re the stickiest ones.
Audiences no longer meet brands in a single place.
One moment, it’s a quick scroll past a sponsored Instagram post. The next, it’s reading a media mention in an industry outlet, or opening a company’s newsletter.
These interactions are fragmented, fleeting, and scattered across countless channels.
The PESO model unites Paid, Earned, Shared, and Owned media to help marketers turn disconnected touchpoints into cohesive, strategic campaigns.
Instead of treating each channel as a silo, PESO shows how they can reinforce and amplify one another for greater impact.
In this article, we’ll break down what the PESO model is, explore each of its four components, and show why integration matters. We’ll also outline a practical implementation framework, map key KPIs, highlight common pitfalls, and offer best practices to ensure success.
Whether you’re just starting with PESO or looking to refine your approach, you’ll leave with a clear roadmap for putting the model into action.
What Is the PESO Model & Its Origin
The PESO model is a communications framework that brings together four types of media: Paid, Earned, Shared, and Owned.
Instead of treating each channel as an isolated tactic, PESO helps marketers design campaigns where these elements reinforce one another.
The concept was popularized in 2014 by Gini Dietrich, founder of the communications blog Spin Sucks.
Dietrich introduced PESO as a way to move beyond the old silos of “advertising versus PR” and reflect the reality of the modern digital landscape, where lines between channels blur.
For example, a blog post (Owned) might be promoted with ads (Paid), spark conversation on social media (Shared), and eventually be cited in a news story (Earned).
In today’s fragmented environment, where audiences discover brands through a mix of feeds, mentions, and direct brand touchpoints, the PESO model provides a structured approach.
It gives organizations a common language for planning integrated strategies, while also offering a practical map for how to amplify reach, credibility, and impact across platforms.
The Four Components Explained
The PESO model is built on four distinct but complementary types of media. Each plays a unique role in shaping how audiences discover, engage with, and trust your brand.
Understanding the strengths and limitations of each component is the first step toward designing an integrated campaign.
Paid Media
Paid media includes advertising and sponsored placements; anything your brand pays to distribute. Think search ads, social ads, display banners, or sponsored posts.
The main advantage is precision: you can target audiences by demographics, interests, or behaviors and scale visibility quickly.
Pros: Highly targeted, scalable, delivers fast results.
Cons: Costly over time, subject to ad fatigue, and easily tuned out if the creative doesn’t resonate.
Earned Media
Earned media is coverage you don’t pay for, such as press mentions, influencer shout-outs, or third-party endorsements.
It builds credibility because the message comes from someone other than the brand.
Pros: Carries strong trust and authority, often more persuasive than advertising.
Cons: Hard to control, unpredictable in timing, and inconsistent in volume.
Shared Media
Shared media refers to the engagement that happens on social platforms: likes, shares, comments, and user-generated content.
It thrives on authenticity and the ability to reach new audiences through organic amplification.
Pros: Authentic, community-driven, often viral in nature.
Cons: Algorithm dependence, reputational risk if negative sentiment spreads, and limited control over reach.
Owned Media
Owned media covers the channels your brand controls, like your website, blog, email newsletter, or resource library.
It’s the foundation of your narrative and provides long-term value.
Pros: Full control over messaging and format, evergreen potential, valuable for SEO.
Cons: Requires consistent resourcing and effort, slower to scale without amplification.
Together, these four components form the building blocks of an integrated media framework to help you reap PESO model benefits. When orchestrated intentionally, they create a cycle where each type amplifies the others.
PESO Model Integration Strategy
Each component of PESO has value on its own, but the real power comes from weaving them together.
When Paid, Earned, Shared, and Owned media operate in sync, they generate a compounding effect that no single channel can achieve.
For example:
You publish a white paper on your website (Owned)
It is then shared on social channels (Shared)
It sparks discussion online and catches the attention of journalists or bloggers (Earned)
That coverage can then be promoted with paid campaigns to reach an even wider audience (Paid)
Each step reinforces the others, creating a loop of visibility, credibility, and engagement.
This integrated approach is more than just “nice to have.” In a landscape where audiences encounter brands in fragmented, unpredictable ways, orchestration ensures consistent messaging and maximizes ROI and PESO model benefits.
Instead of chasing results from isolated tactics, you build a connected system where every piece of content contributes to a larger media strategy.
Ultimately, PESO integration turns scattered touchpoints into a unified experience, amplifying reach, deepening trust, and delivering results greater than the sum of their parts.
How to Implement the PESO Model
Understanding the PESO model is one thing; putting it into practice is another.
The most effective way to execute an integrated campaign is to approach it as a phased strategy, starting with what you already have and layering on support from each media type.
1. Audit Your Assets
Begin by reviewing what you already own across PESO categories.
Do you have a strong blog library? A social presence with active engagement? Recent press coverage or ad campaigns?
This inventory highlights strengths to build on and gaps to address.
2. Select a Campaign Hub
Choose a high-impact owned asset to serve as the centerpiece of your campaign.
This could be a research report, a webinar, or a long-form blog post; something substantial enough to anchor the rest of your efforts.
3. Build the PESO Cascade
Once you have your hub, plan how to extend it across channels:
Shared: Post highlights, snippets, or graphics on social media to drive organic discussion.
Earned: Pitch the asset to media outlets, bloggers, or industry partners for coverage and third-party validation.
Paid: Amplify the best-performing content through targeted ads to reach new audiences and reinforce awareness.
4. Coordinate Across Teams
Messaging, timing, and creative elements should be consistent across all channels.
Align content calendars, synchronize agency or team contributions, and make sure every touchpoint reinforces the same narrative.
5. Launch, Monitor, and Iterate
Roll out the campaign and track performance in real time. Use early results to optimize distribution, adjust creative, or rebalance your paid budget.
Treat PESO in modern marketing as an ongoing cycle: each campaign should inform and improve the next.
When executed with intention, this framework transforms a single asset into a multi-channel engine, maximizing reach and ROI through integrated orchestration.
Measurement & KPI Mapping
An integrated PESO media strategy is only as strong as its ability to prove results.
Each media type comes with its own performance indicators, and tracking them consistently is key to understanding impact.
Paid Media Metrics
Impressions and reach
Click-through rate (CTR)
Cost per acquisition (CPA) or return on investment (ROI)
While these metrics provide useful channel-specific insights, the real value of PESO lies in integrated measurement.
For example:
Traffic from earned or shared placements boosting conversions on owned assets.
Cross-channel conversions where a customer first sees an ad, then engages with social, before converting on the website.
Aggregated PESO ROI that reflects the interplay of channels rather than isolated performance.
A unified dashboard that pulls in metrics from advertising platforms, web analytics, PR coverage, and social monitoring makes these connections visible.
It shifts measurement from siloed reporting to holistic insights, helping teams optimize campaigns and demonstrate how PESO integration drives business outcomes.
Common Pitfalls & Best Practices
Even with a solid framework, many organizations stumble when rolling out PESO campaigns.
Here are four common challenges, and the best practices that prevent them.
Teams often manage Paid, Earned, Shared, and Owned channels independently, which leads to missed opportunities for amplification.
Instead, use a synchronized planning calendar that includes all channels. This ensures campaigns launch in concert and every channel supports the same initiative.
Inconsistent Messaging -> Consistent Brand
Without alignment, the same campaign may be communicated differently across platforms, weakening impact and confusing audiences.
Instead, establish a consistent brand voice and messaging framework. Provide guidelines and shared content assets so every channel reflects the same narrative.
Narrow Metrics -> Cross-Channel Metrics
Measuring each channel in isolation (clicks here, impressions there) ignores the compounded impact of integration.
Instead, track cross-channel metrics in a unified dashboard. Focus on outcomes like conversions, brand lift, or customer lifetime value, not just single-channel KPIs.
Poor Coordination -> Cross-Functional Processes
When agencies, internal teams, and stakeholders work separately, campaigns can stall or fragment.
Instead, build cross-functional processes that foster collaboration, including regular check-ins, shared dashboards, and clear ownership of tasks.
By anticipating these pitfalls and adopting best practices, organizations can avoid wasted effort and unlock the full potential of the PESO model.
Turning Strategy Into Action
The PESO model isn’t just a theory: it’s a practical roadmap for creating campaigns that resonate across today’s fragmented media landscape.
It works best when Paid, Earned, Shared, and Owned channels are intentionally orchestrated, not run in isolation.
A simple way to get started is with a pilot campaign. Choose one strong owned asset (a blog, a report, or a webinar) and map its progression through the PESO cycle: share it socially, pitch it for coverage, and amplify it with targeted ads. Monitor the results, then refine and repeat.
By taking this structured approach, brands can transform scattered efforts into a cohesive system where every channel amplifies the others.
Seven Reasons Your Awareness Campaigns Aren’t Driving Revenue
As marketers, our number one priority is new customer acquisition. This only happens if you plant the seed for acquisition via awareness — that top-of-funnel that marketers love and hate.
If you’re reading this, there’s a strong chance you’re undervaluing your awareness campaigns right now.
But what does “undervalued” mean? I’m saying that you aren’t attributing full revenue credit to those campaigns.
Here’s some common reasons that might be happening.
Don’t blame me if this hurts.
1. Lower-Funnel Bias
Let’s face it — we all have KPIs we're supposed to hit, and whether I'm answering to myself, the CEO, or clients, I need to prove ROI from my marketing spend.
Marketing is always the first department that gets cut. That's never the right choice — we all know that — but we’re under pressure to show provable revenue growth, at risk of being defunded or even fired. This creates bias in our reporting.
That pressure leads us to highlight the things we know are driving revenue growth. And that's always bottom-of-funnel campaigns that we have clear last-click attribution on.
These ads are the closest, most-provable, most-easily-understood answer for the eternal question: “Which ads are driving revenue?”
To the untrained eye using a clicks-only model, it looks like my lower-funnel campaigns are the only ones that are working. But I know that's not true; these campaigns only work because I spent 60% of my budget on awareness campaigns actually filling the funnel.
But until Clicks + Deterministic Views, I never had a clear picture of how well my upper-funnel campaigns are driving revenue.
Your stakeholders, however, look at that 60% of your budget that has no attribution and want explanations as to why it “isn’t driving revenue.”
Here’s what happens:
You start cutting budget from top-of-funnel because you don't have data on how well it's working.
You start putting that budget towards bottom-funnel because on paper, bottom-funnel looks like the only thing that's working.
But this is a self-destructive prophecy. The only reason bottom-of-funnel is working is because you were putting money into top-of-funnel — building that awareness and interest that actually resulted in capturing conversions later.
Put simply: your awareness campaigns aren't driving revenue because you’re not measuring them on revenue.
2. You Can’t Out-Tactic Human Psychology
Very few of us sell things that can be purchased instantaneously from the moment someone sees a picture of it.
So why do we keep pretending like we can?
I see this all the time. Driven by hubris, operators assume their products can be sold via Meta ads on a one-day, clicks-only basis. That privilege is limited basically to cheap, easy-to-understand, or delicious things.
If you’re measuring all your ads on a one-day clicks-only basis, you’re essentially saying your product is an impulse decision only. You’re saying the only ads you want to run are the digital equivalent of candy in the checkout line at the supermarket.
I’m sure you value your product higher than candy in the checkout line; very few of us sell things that someone sees and immediately says, "I want that right now."
So why is one-day-clicks-only the most common attribution model we use to measure our marketing performance?
In reality, sometimes you have to talk to people a lot about things before they understand, some of us are selling products that are complicated, expensive, or genuinely disruptive. We have to tell people many times and explain over a long period why they should buy it.
So why am I measuring my awareness campaigns, consideration campaigns, and conversion campaigns on a model that's only useful for measuring candy in the checkout line? It doesn't make sense.
If I sell a product that people have to think about before they buy, I have to build a campaign and marketing structure that does the work of building education, trust, interest, and consideration.
You can't out-tactic basic human psychology unless you sell candy. It's time we stop pretending otherwise and start measuring our marketing campaigns like real marketers.
3. Engagement Does Not Equal Sales
We all know this one: likes don't necessarily translate into sales.
For lack of a better way of tracking upper-funnel or organic impact, I often find myself relying on vanity metrics like engagement, view-through rates, or sometimes even likes to understand if a campaign is driving those top-of-funnel awareness measurements I'm looking for.
The problem? People who engage are not necessarily the same as people who buy.
Actually, I'd go further and say most people who engage are never going to buy. And a lot of people who do buy don't even remember what they actually saw that inspired them to buy.
That's kind of the dirty secret of post-purchase surveys, right? They remember the loudest thing or the most recent thing that brought them to purchase instead of the full actual journey that got them there.
So using engagement metrics isn't the best way for tracking that silent majority of viewers who end up going on to buy.
It's important that you have a clear model for attaching a view to the later revenue it generated. Because if you’re building an entire creative experimentation structure off of vanity metrics like engagements or views, you’re going to build creative for vanity metrics and engagements instead of building creative that drives revenue later.
It's really important that you’re following the right signal when building awareness creative.
4. Relying on Platform Data
If you're relying exclusively on platform data — Meta, TikTok, Snap, looking only at their ads managers interfaces — you're only getting half the story on the performance of your ad campaigns.
Unless your entire digital funnel, including your awareness, mid-funnel, conversion, and actual conversion events, happens inside that one platform, you're missing critical context.
Think about it this way: let's say you're running a bunch of Pinterest campaigns. They're getting a lot of engagement, a lot of interest, even driving click-throughs, but they're not driving a lot of attributable sales. Those might look like campaigns that aren't doing well if you're only looking at platform data in Pinterest.
However, if you run a quick correlation analysis, you might see that actually, your Google and Facebook performance improved in the periods after you ran those Pinterest campaigns.
Now you know those two things are happening separately, in a vacuum, but you can't actually connect them together without some sort of first-party data, like what you'd get from Northbeam.
Without that kind of data, you might undervalue those Pinterest campaigns and how well they're driving awareness. You'd cut budget from them and then watch your Meta campaigns suffer later down the road; that's exactly the kind of thing we’re trying to avoid.
You need a scientific, full-strategy view of how all your channels interact with each other. Because especially for awareness campaigns, oftentimes awareness happens on one channel like YouTube, but purchase happens somewhere else like Google or Meta.
5. You Need More Than Just MMM and Incrementality
Some people only measure the success of their awareness campaigns based on media mix modeling results or incrementality testing.
But that's only two-thirds of the story.
You need those models to help you understand what channels are actually driving revenue, what spend is important, which campaigns or channels you're testing are actually driving incremental new customer awareness and generation.
But you really need to have MMM, incrementality, and MTA for every stage of your funnel if you want maximum visibility into how well your awareness is working.
Specifically for awareness campaigns, without a model like C+DV for measuring the performance of actual creatives, you don't have that insight. You need creative-level insight into which awareness campaigns are actually driving revenue for your business.
Otherwise, you'll spend forever doing campaign testing, running incrementality tests, and rerunning your MMM models when really what you need is to optimize your awareness campaigns as effectively as your mid-funnel or conversion campaigns.
That's why we built C_D+V: to help you understand which specific assets are responsible for generating your revenue at the top of funnel.
6. Creative Is Targeting, Now
Most of us still misunderstand the massive shift that happened since the launch of the algorithmic-first approach to social media — something that really started in earnest right after COVID.
TikTok pioneered this, but what they did was fundamentally change how social media works: from a friends-first feed into an algorithmic-first feed.
That means the way you get in front of people is not via targeting selections within the platforms themselves. The way you get in front of people — and the way you psychologically teach them about your product — is by making creative that gets their attention.
Instead of thinking of the internet, especially an algorithmic feed like TikTok, as a collection of demographics (young people, old people, Gen Z, Millennials, people who like cats, people who like dogs), you should think of it as communities of people who consume similar content.
Content that looks similar to each other. Content that follows a specific visual energy, style, or pacing.
That's the new way you communicate with people.
The only way you can really value creative as a discovery, top-of-funnel tool is by understanding which ads are driving the sales.
This sort of strategy requires you to think: How do I make content that travels through the algorithm and gets in front of the people I know will like my product and my message?
7. Brand Versus Strategy
Awareness isn’t about beating people over the head with your brand identity anymore. It's about speaking to them where they're at; the algorithm changed this.
Every subset of your audience is going to think differently about your product. You need to meet them where they are, and if you have no real data outside of engagement metrics, you don't know how these ads are actually being received.
So often we prioritize brand identity in our marketing when we should be prioritizing our strategic vision. I talked earlier about how you really get in front of people through creative testing and different types of creative; that means you need to change the way you're approaching your brand standards and brand identity in your ads.
Think about it: if you sell protein powder, there are going to be bodybuilders.
There are going to be ballerinas.
People who just need a little bit more protein in their life.
People trying to recover from eating disorders.
Athletes.
Grandmas and grandpas.
Dads.
All sorts of people who need protein powder.
Why do you think one message would work for all of these people when we live in such a fragmented, decentralized, algorithm-driven internet?
You need to say, "Okay, I'm going to adjust my brand to fit the styles of communication that all these different demographics actually use and respond to."
Instead of saying, "No, we only use this color," or "We only use this type of model," or "Our visuals have to be up to this certain standard," ask yourself:
For each subset of people on the internet that I want to communicate with, what sort of content works best for getting that person's attention?
It does not cheapen your brand to change the way your brand appears on the internet.
The most important thing is that the people online who are watching your ads feel like the product is for them.
How do you achieve that? By meeting them where they're at.
If you've ever rejected an ad because you felt it wasn't up to your brand standards, you're already damaging your awareness performance.
Instead of thinking about your ads from a brand perspective — "Does this creative fit my brand guidelines? Does it fit my style?" — ask yourself: Does this ad creative reach out to and have an impact on the people I want it to reach?
And the only way you can measure that is by using models that attach that performance back to revenue generated.
Try out Clicks + Deterministic Views now and start getting that awareness revenue you deserve.
Omnilux, the world leader in medical-grade LED light therapy, partnered with Northbeam and Pinterest to transform its digital marketing performance. By leveraging Northbeam’s Clicks + Deterministic Views (C+DV) model, Omnilux was able to better understand the impact of its Pinterest ad views on real business results: seeing a 659% lift in ROAS, a 728% increase in transactions, and a 671% lift in revenue compared to a traditional clicks-only model.
The challenge
Founded in 2003 and based in Napa, CA, Omnilux is a trusted name in skincare technology, known for its flagship product, the Omnilux Contour Face. While the company had built a strong reputation among dermatologists and skincare professionals worldwide, it faced a pressing challenge: identifying cost-effective channels that could reliably generate incremental demand and revenue.
As Omnilux prepared for the competitive 2025 holiday season, the team needed confidence in which channels were most efficient at driving purchases. Their goal was ambitious yet clear: achieve 3x ROAS on a 1-day attribution window—a benchmark critical for scaling growth profitably.
The solution
Wanting to better understand Pinterest’s role in the customer journey, Omnilux partnered with Northbeam to test Clicks + Deterministic Views, an industry-leading attribution model that goes beyond traditional clicks-only models by layering in verified impression & view data directly from participating ad platforms.
These platform events are combined with Northbeam’s industry-leading, first-party pixel and order data to provide a complete and accurate view of how marketing actually drives conversions. Unlike traditional “view-through” attribution, Northbeam links views deterministically, meaning every impression tied to a conversion is verified, not inferred.
Working closely with both Northbeam and Pinterest, Omnilux was able to quickly implement C+DV and begin measuring campaigns. This collaborative approach allowed the team to directly attribute both clicks and views to transaction data, providing unprecedented visibility into how Pinterest drives revenue.
The actual ad creative used on Pinterest and optimized with Clicks + Deterministic Views.
The results
The results were game-changing:
659% lift in ROAS
728% increase in transactions
671% increase in revenue
Significant lift in conversions among new-to-file customers
Beyond the numbers, Omnilux gained a qualitative advantage: a clear, data-backed understanding of Pinterest’s role in driving net-new demand. The marketing team was able to shift spend more efficiently, validate Pinterest as a growth channel, and streamline internal decision-making with trusted attribution.
“Northbeam’s partnership with Pinterest gave us a new level of clarity into how view-based engagements translate into real revenue. It’s been a game-changer for our strategy heading into the holiday season.” — Caleb Orion, Director of Acquisition & Ecommerce, Omnilux
Why Northbeam?
Unlike other attribution tools that rely solely on last-click models, Northbeam’s privacy-preserving device graph and deterministic data integrations deliver a truer picture of channel impact. For Omnilux, this meant cutting through the noise, eliminating wasted spend, and doubling down on what works.
Northbeam is pleased to announce Clicks + Deterministic Views (C+DV), a new attribution model now available in Northbeam.
C+DV takes verified impressions and view data directly from participating ad platforms and combines them with Northbeam’s first-party performance data.
Advertisers can use C+DV to measure how ad impressions and views influence conversions. This is “viewthrough” conversion measurement done deterministically, which means every impression tied to a conversion is verified, not inferred.
Put simply: you can now measure the impact of upper-funnel campaigns more accurately, whereas before advertisers struggled to measure those campaigns with clicks-focused models.
Clicks + Deterministic View solves a huge blind spot that plagues modern advertising, paving the way for a new era of growth marketing.
The problem: clicks-only has a blind spot
Clicks-only measurement is biased towards campaigns at the bottom of the funnel. This obscures the impact of your vital awareness campaigns.
If you’ve ever managed a digital marketing budget, you’ve probably fallen for this destructive bias.
It goes like this: you run amazing awareness campaigns on TikTok, Snap, and CTV. On these platforms people don't always click through immediately. The content performs great, engagement is solid, brand mentions are up, and revenue begins to climb across all your other channels.
But according to your attribution dashboard? Those awareness campaigns are basically worthless, as they aren’t connected to any measurable revenue via click behaviors.
The longer you ignore or undervalue your awareness campaigns, the sooner the performance of your bottom-of-funnel conversion campaigns dry up. This leads to further consolidation of budget to lower funnel campaigns, which creates a self-destructive cycle that eventually kills the ad account – and maybe even your business.
Before C+DV, marketers focused on clicks and not views because clicks used to be the most reliable measure for proving revenue.
It's like being a basketball coach and only focusing on stats about who scored the final basket, while completely ignoring all the assists, steals, and defensive plays that made it possible.
The marketing environment: attention is shifting
Media consumption is constantly changing, and marketers seeking competitive CPMs are looking to these new channels for efficiency. Yet many of the rising new channels are difficult to measure exclusively with click-based attribution.
As marketers, our goal is to get our message in front of the right people at the right time (in a measurable way, I’d argue.) As attention shifts to new channels, it’s our job to follow. So where are people going?
The short answer: video. Video-first, algorithmic-led social platforms lead in time spent on-app by users, with YouTube, TikTok, and Instagram dominating the field.
TikTok continues to lead the field in users’ time spent on app. The algorithm-driven discovery model of TikTok’s feed has sweeping influence on other social media, all of whom are launching similar feeds. Users love TikTok, and they love video – as marketers, we must react.
But how do you reliably measure the performance impact of these emerging video channels?
The solution: a different type of viewthrough model
As any marketer knows, videos drive considerably less clicks than static ads, but are massive for driving discovery and awareness.
Growth marketers needed a reliable model for measuring how these emerging placements drive revenue without clicks: C+DV is perfectly positioned to help you take advantage of this new media environment.
So what makes Clicks + Deterministic Views different? Instead of making educated guesses about what happened (like most view-through attribution models do), C+DV actually knows what happened.
We built direct integrations with the major platforms (Meta, TikTok, Snap, Pinterest, Axon, Vibe, and MNTN) to get verified impression data. Not modeled data. Not estimated data. The actual "yes, this specific person saw this specific ad" data.
Then we match that against our first-party pixel and order data using exact identifiers: order IDs, hashed emails, login data. When we say an impression led to a conversion, we can prove it.
This is called “deterministic” attribution, where other viewthrough modeling is “probabilistic.”
The difference between deterministic and probabilistic attribution:
Deterministic (what we do): Uses verified identifiers to directly connect an ad impression to a conversion. It's like having security camera footage.
Probabilistic (what everyone else does): Uses statistical models to guess which ad drove a conversion based on signals like IP address and device type. It's like relying on eyewitness accounts.
Early results from beta testers:
We've been testing this with select customers, and the numbers are shocking:
A luggage brand saw a 283% increase in attributed transactions once they could properly track their TikTok impact - allowing them to more accurately value TikTok in their paid digital funnel
A CPG company discovered 175% more attributed revenue than they thought they had - giving massive value back to the campaigns they thought were underperforming
A cosmetics brand recorded a 550% increase in attributed transactions - adding massive vision where they previously thought their paid digital wasn’t affecting revenue
These aren't small optimizations – these are massive revelations about how their paid media is affecting the bottom line.
“Northbeam’s partnership with Pinterest gave us a new level of clarity into how view-based engagements translate into real revenue. It’s been a game-changer for our strategy heading into the holiday season.” — Caleb Orion, Director of Acquisition & Ecommerce, Omnilux
What you can do with C+DV:
See the full customer journey: Finally understand how both clicks and views work together to drive conversions, instead of just seeing the last touchpoint. Witness the funnels that drive your conversions from the moment of discovery to the point of sale, and optimize accordingly.
Upper-funnel campaigns get their due: Quantify the actual revenue contribution of your awareness and prospecting campaigns that have been flying under the radar. Better connect revenue to top of funnel campaigns. Justify to clients, bosses, and stakeholders that your awareness campaigns are worth the spend.
Make decisions with confidence: Every impression tied to a conversion is proven, not modeled, so you can trust your data. Optimize, reallocate, and experiment in your top of funnel campaigns while knowing you have a way to accurately measure their viewthrough impact.
Smarter budget allocation: Know exactly which campaigns are driving downstream revenue and scale what actually works. Stop wasting spend on awareness campaigns that boost vanity metrics but don’t drive measurable revenue.
Cross-platform clarity: Get all your verified impression data combined with our order-level tracking in one unified view. See all your channels in one single dashboard.
Privacy-first approach: All data processing happens in our secure clean room, ensuring compliance and protecting user privacy.
Stop wasting money: Eliminate the over-crediting of last-click channels and avoid underspending on the campaigns that create real demand.
Watch our full webinar for a full breakdown in how to use C+DV.
How it works
Thanks to Northbeam's direct integration with each ad platform included in this model, Northbeam can do true in-app "impression matching".
This means that conversions can be matched with impressions that helped generate those conversions at the user level.
The logic is straightforward:
In C+DV, clicks still come first. If someone clicked an ad in their journey, that click still gets the credit (just like always).
Views fill the gaps. Views only get credit when there are no clicks in the journey, so we're not inflating your numbers or double-counting.
It's additive, not replacement. C+DV never overrides click credit—it only assigns value to orders that were previously categorized as "free" or "direct."
How does C+DV attribute credit?
When looking at ads under the C+DV model, paid clicks and paid views take full precedence over touchpoints with the following Category (Northbeam) labels:
- Direct
- Organic
- Organic Search
- Paid - Branded Search
- Email
- SMS
Credit between paid clicks and paid views is split as follows:
Each first-party (i.e. Northbeam observed) paid click touchpoint receives a share of credit.
For each participating C+DV partner platform, the remaining share of credit is distributed equally across all view touchpoints from that platform using the following methodology:
Views are grouped by platform and credit is divided within each platform “pool.” For example, if there is 1 view on Platform A and 10 views on Platform B, Platform B will receive more total credit, but each individual view will have less weight than the 1 view on Platform A.
We do not weight views from different platforms differently; the amount of credit assigned to each platform is dependent on how many views are in that platform’s “pool.”
Credit is not assigned based on where each touchpoint falls in the funnel; for example, if a customer views an ad on Snap, and later views an ad on Pinterest, the Pinterest ad does not earn less credit.
Top-of-funnel channels will see the biggest lift. If you have channels doing lots of demand generation or with low click-through rates, you'll see the highest impact.
Performance marketing stays accurate. For bottom-funnel campaigns with high click-through rates, you'll see more modest changes since clicks were already being tracked properly.
Upper-funnel campaigns will fill out a lot of previously-unattributed revenue. Those awareness campaigns that seemed like money pits? You're about to discover their real value.
The Bottom Line
We've been dealing with incomplete attribution data for so long that we'd almost gotten used to making important budget decisions with half the information. This feels like one of those moments where the industry actually takes a meaningful step forward.
As our VP of Product Stas Goldobin puts it:
"We built Clicks + Deterministic Views to set a new standard for attribution accuracy and transparency. There's no guesswork: every conversion is matched with certainty."
How to turn on C+DV
If you're already a Northbeam customer, here's how to get started:
Go to Account Settings > Model Settings > Attribution Models (or click this link)
Select Clicks + Deterministic Views and choose your platforms
Wait 24-48 hours for initial processing
Note: By selecting platforms, you're consenting to share impression data with us, so only admins can make these changes.
Ready to see the full picture of your marketing performance? If you're already a Northbeam customer, you can turn this on today in your account settings. If you're not, book a demo and we'll show you what you've been missing.
Digital advertising is at a turning point. As third-party cookies disappear, the industry is being forced to rethink how to connect with audiences in ways that are both effective and privacy-compliant.
Enter contextual targeting: a smarter, privacy-first approach that aligns ads with the content consumers are actively engaging with, rather than relying on invasive tracking of their personal data.
This shift isn’t happening in a vacuum. Privacy regulations like GDPR and CCPA are tightening around data use, while browsers phase out cookie-based tracking and consumers grow increasingly wary of how their information is collected.
Together, these forces are accelerating the move toward strategies that respect privacy while still delivering performance.
In this article, we’ll unpack what contextual targeting is and why it’s gaining momentum. We’ll explore how AI is transforming contextual targeting with deeper semantic intelligence and real-time placement, compare it to other advertising methods, and highlight the measurable benefits it brings to advertisers and publishers alike.
Finally, we’ll walk through practical strategies, implementation steps, and real-world innovations, giving marketers a clear roadmap to move forward with confidence towards a future of cookieless advertising.
Why Contextual Targeting Is Gaining Momentum
The digital advertising industry is undergoing a fundamental reset.
With third-party cookies on their way out and regulators tightening their grip on consumer data, advertisers are under pressure to find methods that don’t rely on invasive tracking.
Contextual targeting is stepping into that role, offering a privacy-first solution that still delivers meaningful results.
Privacy-First Shift
Unlike behavioral targeting, which builds profiles from users’ browsing histories, contextual targeting analyzes the content on the page or screen in real time.
Ads are placed according to what someone is reading, watching, or listening to, not who they are.
This eliminates many compliance headaches while reassuring consumers that their personal data isn’t being harvested.
Trust and Relevance
Because ads appear alongside related content, they feel more natural and useful to the user.
A running shoe ad next to an article about marathon training is inherently more relevant than one based on past browsing history.
This alignment fosters trust while strengthening brand safety, since advertisers can control the environments where their ads appear.
Performance Uplift
Multiple studies show that contextually relevant ads outperform generic placements on metrics like engagement, recall, and brand lift.
When the surrounding content and the ad message reinforce one another, users are more likely to notice, remember, and act.
Applicability Across Formats
Importantly, contextual targeting isn’t confined to display ads.
It scales across channels: digital display, in-app, audio, connected TV (CTV), and even emerging formats like digital out-of-home.
That versatility makes it a future-proof strategy for reaching audiences wherever they consume content, without depending on tracking signals that are disappearing fast.
How AI Is Boosting Contextual Targeting
Traditional contextual targeting worked by scanning for keywords on a page; useful, but often blunt.
Modern advertising environments are far more dynamic, requiring greater precision and nuance.
That’s where artificial intelligence (AI) steps in, supercharging contextual targeting with deeper analysis, faster execution, and safer placement.
Semantic Intelligence
AI and natural language processing (NLP) move beyond simple keyword matching to interpret tone, sentiment, and meaning.
For example, the word “Apple” could signal technology or food depending on the surrounding context.
Semantic AI makes these distinctions automatically, ensuring ads don’t just match words but truly align with intent and subject matter. This avoids mismatches that frustrate consumers and compromise brand integrity.
Adaptable Analytics
Online content evolves constantly, from trending memes to breaking news. Static rules quickly become outdated.
AI-driven models continuously retrain on new data, learning the language shifts and cultural references that matter. This adaptability ensures that targeting strategies remain effective even as the digital landscape changes.
Real-Time Placement
One of the most powerful applications of AI is the ability to evaluate and act instantly.
AI-powered contextual advertising systems can scan live or streaming content and insert ads dynamically, in real time.
For example, scene-level contextual targeting on CTV means a sports drink ad can appear the moment a character finishes a workout, delivering in-the-moment relevance at scale.
Precision and Safety
Without relying on personal data, AI ensures that ads are delivered in brand-curated environments that match contextual preferences.
Machine learning models can identify safe versus unsafe placements (e.g., distinguishing between positive news coverage and harmful or sensitive topics), giving advertisers control without slowing down campaign execution.
Together, these advancements elevate contextual targeting from a compliance-driven fallback into a high-performance, future-ready strategy.
Advertisers can achieve both privacy-first marketing practices and unprecedented levels of accuracy, using AI to make contextual ads smarter, safer, and more engaging than ever before.
Benefits & Evidence
Contextual targeting isn’t just a stopgap for the post-cookie era. It’s proving itself as a more effective, scalable, and trusted advertising method.
The benefits show up across compliance, performance, cost, and innovation.
Privacy, Compliance, and Trust
Since contextual targeting doesn’t depend on individual identifiers, it sidesteps many of the privacy concerns now dominating digital advertising.
Regulations like GDPR and CCPA are pushing advertisers to abandon third-party tracking, and contextual targeting naturally fits within these frameworks.
For consumers, the appeal is just as clear: ads are matched to what they’re consuming in the moment, without prying into their personal histories.
That balance fosters trust and helps brands stay ahead of future regulatory changes.
Let’s see the data
Nearly 80% of respondents in a recent study favor transparency in how their personal data is used in digital advertising, and 62% feel uneasy about companies tracking their behavior online.
Enhanced Engagement
Ads placed in relevant contexts consistently outperform generic placements.
Studies have shown that consumers are more likely to remember ads that align with the surrounding content, with measurable lifts in metrics like click-through rate, dwell time, and brand recall.
For example, a travel ad placed alongside vacation planning content feels timely and helpful, whereas the same ad dropped into unrelated content risks being ignored.
Let’s see the data
New research shows that contextually targeted ads can increase consumer brand recall by up to
70%.
Cost Efficiency and Scalability
Contextual targeting leverages automation and AI to deliver ads quickly, at scale, and without the need for costly data integrations or third-party audience segments.
This translates into strong ROI, particularly as AI continues to improve contextual accuracy and reduce wasted impressions. Brands can stretch their budgets further while still achieving performance benchmarks.
Let’s see the data
Research shows that contextual targeting can increase purchase intent by
63%
compared to audience- or channel-level targeting.
CTV Innovations
Connected TV (CTV) is one of the fastest-growing ad channels, and contextual targeting is unlocking a new level of precision within it.
Scene-level targeting allows ads to be placed at the exact moment they’re most relevant: think a snack ad during a cooking show.
This in-scene relevance increases engagement while preserving the lean-back experience viewers expect from streaming content.
Let’s see the data
Studies show that contextual targeting on CTV boosts consumer ad recall by
4x.
Taken together, these benefits demonstrate that contextual targeting is not just privacy-compliant, but performance-driven.
It offers a rare combination of consumer trust, measurable results, and technological innovation, making it one of the most future-proof strategies in the advertising toolkit.
Strategies & Best Practices
To unlock the full value of contextual targeting, brands need a thoughtful strategy that blends contextual intelligence with strong creative, cross-channel consistency, and continuous measurement.
Here are our top strategies and best practices:
Hybrid Targeting
The most effective campaigns don’t rely on contextual signals alone. When combined with first-party data, like purchase history, loyalty program engagement, or email behavior, contextual targeting becomes more nuanced.
A brand can serve ads that not only align with the content but also reflect known customer preferences, creating a balance between personalization and privacy.
Creative Alignment
Even the smartest targeting falls flat if the creative feels off. Ads should mirror the tone, intent, and energy of the surrounding content.
For instance, a lighthearted podcast episode might call for playful visuals or copy, while a serious news article might demand a more restrained approach.
This alignment improves user receptivity and strengthens brand perception.
Cross-Channel Deployment
Contextual strategies aren’t limited to web display. They can be applied across connected TV, audio streaming platforms, mobile apps, and even emerging formats like digital out-of-home.
Cross-channel consistency ensures audiences encounter relevant, context-driven messaging in multiple environments, reinforcing recall and increasing conversion potential.
Governance and Transparency
To protect brand integrity, marketers should clearly define the governance and structure of their contextual ad strategy.
For example, a food brand might want to appear next to recipe content but avoid content discussing food shortages.
Just as important is transparency: explaining targeting logic to internal stakeholders and clients builds trust and prevents misalignment.
Continuous Optimization
Advertisers should treat contextual targeting as an iterative process: testing contextual versus behavioral or hybrid approaches, experimenting with creative variations, and tracking outcomes beyond clicks.
Attention metrics, recall studies, and brand lift surveys can reveal the true impact of contextual placements, enabling smarter optimizations over time.
By weaving these practices together, advertisers can turn contextual targeting into a high-performing, future-proof pillar of their media mix.
Rather than replacing old strategies outright, contextual targeting works best when integrated into a broader ecosystem that values privacy, relevance, and adaptability.
Implementation Guide
A thoughtful implementation process ensures that contextual targeting not only runs smoothly but also delivers measurable results.
Below are four steps to get your contextual targeting strategy up and running.
1. Choose AI-Native Platforms
Start by selecting platforms built with AI and contextual advertising analysis at their core, not just keyword targeting.
AI-native solutions are better equipped to understand nuance, adapt in real time, and scale placements across formats like display, video, and CTV.
2. Define Taxonomy
Before launching, establish a taxonomy that outlines the themes and content categories that fit your brand.
This includes both positive contexts you want to prioritize (e.g., fitness content for a sportswear brand) and exclusions you want to avoid (e.g., sensitive or negative news coverage).
A well-structured taxonomy ensures campaigns stay aligned with brand values.
3. Measure Meaningfully
Go beyond impressions and clicks. Contextual targeting’s value shows up in metrics like attention, recall, brand lift, and ROI.
By tracking performance at both the placement and creative level, marketers can prove the impact of contextually relevant advertising and secure buy-in from stakeholders.
4. Iterate on Strategy and Creative
Contextual targeting should be an ongoing process. Use performance insights to refine your taxonomy, optimize creative, and test new combinations of contextual and first-party signals.
Regular iteration ensures campaigns remain relevant as content ecosystems, consumer behaviors, and regulatory landscapes evolve.
Done right, implementation turns contextual targeting from a theory into a scalable, ROI-positive practice that future-proofs advertising programs.
From Compliance to a Competitive Edge
As third-party cookies fade, the benefits of contextual targeting without cookies have become clearer than ever, and contextual targeting has evolved from a fallback option into one of the most strategic approaches in digital advertising.
By focusing on the content people are actively engaging with, it enables privacy-first marketing that aligns with both consumer expectations and tightening regulatory standards.
When done well, contextual targeting drives higher relevance and engagement, placing ads in environments where they feel timely, useful, and trustworthy.
AI makes this scalable across display, mobile, audio, and CTV, delivering cost-effective placements without sacrificing precision or brand safety.
For marketers and publishers, the message is clear: contextual targeting doesn’t just replace cookies, it future-proofs campaigns by building trust, improving performance, and adapting to the privacy-first digital ecosystem.
Brands that invest in contextual strategies today will be best positioned to thrive in the cookieless advertising world of tomorrow.
As third-party signals fade and privacy controls rise, the question for brands isn’t if they should pivot their data strategy, it’s how.
The data landscape has changed dramatically: cookies are crumbling, walled gardens are tightening, and consumers expect greater transparency around how their information is used.
Marketers now face a complex puzzle. Different data sources come with different consent models, levels of quality, and degrees of scale.
Get the mix wrong, and you risk wasted budget, ineffective targeting, or worse: eroding the trust you’ve worked hard to build with customers.
This guide is designed to cut through the noise.
We’ll define the three core types of customer data (first-, second-, and third-party), show where each shines, and explain how to combine them responsibly.
You’ll also find practical frameworks for migrating toward first-party strength, plus governance and measurement checklists to ensure your strategy is both effective and compliant.
What Each Data Type Is (and Where It Comes From)
When marketers talk about customer data, the conversation usually centers on three categories: first-party, second-party, and third-party.
Each type differs in how it’s collected, how much control you have over it, and what it’s best used for.
First-party data
This is the gold standard. First-party data is information you collect directly from your customers through your own channels and systems.
Examples of customer data types include:
On-site behavior such as page views, clicks, or cart activity
Transaction history and purchase frequency
Customer service interactions (emails, chats, calls)
Data from preference centers, surveys, or loyalty programs
Because it’s gathered with direct consent, you control the context, accuracy, and recency. First-party data forms the foundation for personalization, retention, and long-term trust.
Second-party data
Second-party data is essentially someone else’s first-party data that you access through a trusted partnership. Think of it as data sharing with permission.
Examples of customer data types include:
A retail brand sharing loyalty data with a complementary CPG partner
A travel site exchanging booking information with an airline
Co-marketing campaigns where audiences are segmented and matched
This type of data is highly relevant and often more accurate than third-party sources, but it requires clear contracts, governance, and strong alignment between partners.
Third-party data
Third-party data comes from external aggregators who collect information from a wide range of sources: websites, apps, surveys, public records, and more. Marketers often use it for scale and reach.
Examples of customer data types include:
Audience segments for advertising platforms
Demographic or interest-based enrichment
Market intelligence panels and benchmarks
The tradeoff: third party data quality and accuracy can vary, availability is shrinking due to privacy regulations, and reliance on third party data can create compliance risks.
While third-party data can still play a role in broad awareness or filling specific gaps, its dominance is quickly fading.
First vs Second vs Third-Party Data: Advantages, Risks, and Best-Fit Scenarios
Each type of customer data comes with clear strengths, inherent risks, and specific situations where it performs best.
Knowing where each one shines (and where it doesn’t) helps marketers design smarter, more compliant strategies.
Criteria
First-Party Data
Second-Party Data
Third-Party Data
Accuracy
Very high: collected directly from customer interactions.
High: sourced from trusted partner’s first-party data.
Variable: aggregated from many sources with inconsistent quality.
Scale
Limited: constrained by your own audience size.
Moderate: depends on the size and relevance of partner audiences.
High: broad reach across demographics, geographies, and interests.
Consent Strength
Strong: explicit opt-in and clear compliance alignment (GDPR, CCPA).
Strong (when contracts are clear): governed by partnership agreements.
Weak: often indirect, facing growing regulatory restrictions.
Portability
High: can be activated across channels and integrated with clean rooms.
Moderate: requires secure exchanges and compatible systems.
Low: limited portability across platforms; often tied to walled gardens.
Cost
Generally low: you already own it, though capture/management systems carry overhead.
Moderate: may involve contracts, tech setup, or exchange costs.
High: purchased from providers, with recurring costs for updates or access.
Co-marketing campaigns, prospecting into adjacent audiences, channel enrichment.
Broad awareness, market sizing, upper-funnel testing, enrichment within specific platforms.
First-party data
Strengths:
First-party data is the most accurate and up-to-date, since it’s drawn directly from your customer interactions.
It’s collected with explicit consent, aligning closely with compliance requirements like GDPR and CCPA.
Because you own the relationship, it allows for closed-loop measurement, tying marketing actions directly to business outcomes.
Risks/limits:
The main limitation is scale. Your audience size defines the boundaries of your first-party data.
To grow its usefulness, you need seamless capture experiences (clear opt-ins, loyalty programs, preference centers) and compelling value exchanges so customers willingly share information.
Best fit:
First-party data is best for personalization, lifecycle retention strategies, seed audiences for modeling, and as the ground truth in clean-room collaborations.
Second-party data
Strengths:
Second-party data combines the accuracy of first-party data with the reach of partnerships.
Because it’s exchanged directly between companies, it often carries high relevance: your partner’s customers are likely to resemble your ideal prospects.
It also opens up category-level insights you might not capture on your own.
Risks/limits:
These relationships come with contractual and technical overhead.
You’ll need strong governance, alignment on use cases, and infrastructure like data clean rooms or secure exchanges.
Misalignment between partners can limit value or create compliance headaches.
Best fit:
Second-party data works best for co-marketing efforts, finding lookalike audiences through adjacent partners, and adding enrichment for specific channels (e.g., expanding an email list with partner insights).
Third-party data
Strengths:
Third-party data’s value lies in speed and scale.
Aggregators offer broad audience reach across demographics, geographies, and interests.
It can also deliver upper-funnel visibility and market intelligence that would otherwise be hard to access.
Risks/limits:
Accuracy and freshness vary widely, since the data often comes from disparate sources.
Growing regulation and browser privacy changes are reducing availability and increasing compliance risks.
It’s also difficult to port third-party data consistently across channels.
Best fit:
Third-party data is most effective for broad awareness campaigns, filling gaps where first- or second-party data isn’t available, and providing high-level market sizing or competitive context.
Use Cases & Examples
Marketers rarely rely on just one type of data. In practice, the strongest strategies blend first-, second-, and third-party data depending on the goal.
Here’s how each comes into play across common scenarios:
Acquisition
First-party: Start with the customers you know best. Using your own purchase history or high-intent site activity as a seed audience allows for more accurate modeled expansions.
Second-party: Partner data can help you discover adjacent audiences. For example, a fitness apparel brand might leverage a nutrition company’s loyalty data to find new customers with aligned interests.
Third-party: Broader segments from data providers still have a role in testing new markets or scaling awareness quickly, though quality checks and validation are essential.
Personalization
First-party: Behavioral signals (browsing patterns, past purchases) combined with declared preferences (survey responses, opt-ins) are the foundation of personalization engines.
Second-party: Enriching with partner insights (say, a travel site layering hotel preferences from an airline partner) sharpens relevance and reduces wasted impressions.
Third-party: While less common in personalization, third-party attributes can provide context, such as layering demographic markers or lifestyle segments onto first-party records.
Attribution & Measurement
First-party: Your own data is the source of truth for connecting campaigns to business outcomes.
Second-party: Partner data helps measure shared efforts, like determining the performance of a co-branded promotion across channels.
Third-party: Independent panels or benchmark datasets can add external context, helping validate your performance against industry norms.
Data Collaboration
First-party: Secure data clean rooms let you match your own data with a partner’s in a privacy-safe way, without directly sharing raw records.
Second-party: Selective exchanges with trusted partners expand reach while maintaining governance controls.
Third-party: Some walled gardens allow limited third-party enrichment inside their platforms, useful for audience validation or contextual layering.
Strategy & Decision Framework
A smart data strategy isn’t about choosing one type of data over another, it’s about sequencing and combining them in a way that balances precision, scale, and compliance.
Here’s a framework to guide decision-making:
Start with First-Party Strength
Your foundation should always be first-party data.
Audit the identifiers you already capture (emails, device IDs, loyalty program logins), review your consent records, and map every capture point across web, app, and offline channels.
Then, design high-value collection experiences. Think gated content, member benefits, or personalized offers that give customers a clear reason to share data.
Add Second-Party Selectively
Once your first-party base is strong, look for opportunities to partner.
The best partners are those with complementary audiences; not direct competitors but brands whose customers overlap with yours in meaningful ways.
Define the use case up front, outline how long the data will be shared, and agree on success metrics before you exchange a single record.
Use Third-Party Surgically
Third-party data should no longer be a default.
Instead, apply it sparingly in contexts where its utility clearly outweighs the risks. For example: testing into a new market or layering demographic context for high-level audience insights.
Always benchmark performance against your first-party data to validate incremental value.
Governance by Design
Strong governance is what keeps your strategy resilient.
Document every data source, its purpose, and its history. Establish retention rules and deletion expectations from day one.
Work closely with your legal and security teams to ensure compliance, and treat privacy not as a box to tick but as a core brand value.
Measurement & Governance
Collecting data is only valuable if it’s trustworthy, well-governed, and tied to measurable business outcomes. Marketers should track performance and enforce safeguards in parallel.
Key Performance Indicators (KPIs)
To understand whether your data strategy is working, monitor:
Capture rate, match rate, and consent opt-in: Are you collecting enough data, and is it permissioned?
Profile completeness: How rich and usable are the customer records you hold? How effective are your identity resolution processes?
Lift vs. control metrics: Track how data-driven campaigns perform compared to holdouts: conversion rate, average order value (AOV), customer acquisition cost (CAC), and retention.
Media efficiency: Does better data reduce wasted spend and improve return on ad spend?
Data quality: Keep an eye on freshness, duplication, and conflict rate so your systems aren’t operating on outdated or contradictory records.
Controls and Governance Practices
Measurement means little without compliance and governance baked in:
Consent logs and purpose binding: Maintain clear records of when and how data was collected, and ensure usage aligns with stated purposes.
Subject rights workflows: Make it easy to fulfill access, correction, or deletion requests.
Data minimization and retention policies: Collect only what you need, store it for a defined period, and delete on schedule.
Clean-room policies and partner due diligence: Establish clear rules for collaboration, vet partners carefully, and confirm that privacy-enhancing technologies are in place.
Model validation cadence: Regularly test and recalibrate models that rely on your data, ensuring accuracy and fairness over time.
Strong measurement paired with robust governance ensures that your data mix not only performs but also stays compliant and resilient as privacy standards evolve.
Tools & Workflow
Turning raw data into business value requires the right infrastructure. A modern marketing stack should cover collection, collaboration, activation, and analytics, with privacy built in at every step.
Foundation
Start with systems that centralize and standardize your own data:
Customer Data Platform (CDP) or CRM to unify records across touchpoints.
Tag management and server-side collection to ensure accurate event tracking while reducing reliance on cookies and client-side scripts.
Collaboration
To safely expand beyond your walls, invest in technologies that support secure data sharing:
Data clean rooms that allow you to match and analyze datasets without exposing personally identifiable information.
Secure partner exchanges for controlled second-party data sharing.
Privacy-enhancing technologies (PETs) like encryption and anonymization to minimize risk in collaborative environments.
Activation
Once data is unified and governed, it can power real-time engagement:
Journey orchestration tools that personalize cross-channel experiences.
DSP and paid media integrations for efficient audience targeting.
Web and app personalization engines to deliver tailored content, offers, or product recommendations.
Analytics
Finally, analytics tools close the loop and measure impact:
Incrementality testing to validate lift and avoid over-attributing success.
Audience health scoring to monitor the quality and long-term viability of your datasets.
Implementation Plan
Shifting to a privacy-first, data-driven strategy doesn’t happen overnight.
If you’re wondering how to shift from third-party to first-party data, the most effective approach is phased: start with a baseline, build securely, test deliberately, and scale what works.
Phase 1: Audit
Begin by mapping what you already have. Identify all current data sources, the consent mechanisms tied to them, and any system dependencies.
Look for quick wins, such as fixing broken tags or improving opt-in language, to boost capture rates right away.
Phase 2: Build
Strengthen your first-party foundation.
Standardize event tracking, establish persistent identifiers, and ensure consent records are accurate and accessible.
At the same time, prepare for collaboration and responsible data activation by setting up clean room access and defining clear partner selection criteria.
Phase 3: Pilot
Run controlled tests with limited scope.
This might include a single second-party partnership to expand reach or a carefully chosen third-party dataset for enrichment.
Always benchmark these efforts against first-party-only cohorts to measure true incremental value.
Phase 4: Scale & Govern
Once you’ve proven impact, expand.
Broaden successful partnerships, refine enrichment, and embed learnings into standard workflows.
At this stage, governance becomes critical: formalize contracts, define retention and deletion rules, and set a cadence for ongoing validation to ensure compliance and data quality over time.
A phased roadmap not only reduces risk but also creates momentum, giving your team early wins while building toward long-term resilience.
Future-Proof Your Data Strategy
In a world where privacy is tightening and third-party signals are fading, not all data is created equal.
The path forward is clear:
Lead with first-party data. It offers accuracy, trust, and long-term resilience.
Use second-party data strategically to expand relevance through permissioned, high-value partnerships.
Apply third-party data sparingly, focusing only on contexts where it truly adds incremental reach or market insight.
Measure incrementality and enforce governance so that your data mix remains effective, compliant, and future-proof.
Marketers who embrace this balanced approach will do more than survive the loss of third-party cookies: they’ll build durable customer relationships, smarter activation strategies, and a stronger foundation for growth.
As third-party identifiers fade and consumer expectations for privacy rise, brands face a pressing challenge: how to gather data that is both permissioned and precise.
Zero-party data is information that customers intentionally and proactively share with a brand. It includes preferences, intentions, and context offered directly and with full consent.
Unlike inferred or observed signals, zero-party data is transparent by design, giving marketers a clear foundation for personalization without compromising trust.
In this article, we’ll explore why zero-party data matters in a privacy-first world and how it complements first-party behavioral data.
We’ll outline the collection experiences and value exchanges that actually work, show how to activate customer-declared insights across the journey, and map out the governance standards that keep programs compliant and effective.
By the end, you’ll have a framework for turning customer declarations into measurable business impact.
What Zero-Party Is Data & Why It Matters
Zero-party data (ZPD) is information that customers intentionally and proactively share with a brand.
Unlike first-party data, which is observed through behaviors like clicks or purchases, zero-party data is volunteered directly. It encompasses preferences, intentions, personal context, and even how customers want to be recognized.
Because it’s offered with explicit consent, it’s the most transparent and purpose-built signal set marketers can use for personalization.
And the business case for zero-party data has never been stronger.
As third-party identifiers continue to disappear and privacy regulations tighten, brands can no longer rely on external signals to reach the right audience.
Those that invest in building direct, two-way relationships with their customers earn higher trust, stronger conversions, and more efficient spend by cutting out wasted impressions and irrelevant targeting.
Zero-party data also provides the missing context that first-party data strategy can’t capture on its own.
First-party data shows what people did; zero-party data reveals why they did it. Whether that’s budget constraints, style preferences, or purchase intentions.
Combined, the two give marketers a complete view that closes critical gaps in intent, timing, and personalization.
Finally, zero-party data carries a built-in privacy-first advantage.
Because it is consented data at the point of capture and tied to a clear value exchange, it’s easier to defend under evolving regulations and simpler to sustain over time.
When brands demonstrate transparency and respect for preferences, customers are more likely to share again, creating a cycle of trust that powers long-term growth.
Zero-Party vs First-Party vs Second-Party vs Third-Party Data
Not all data is created equal. To understand the role of zero-party data, it helps to place it in context alongside first-, second-, and third-party sources.
Each type has its own strengths and limitations, and together they form the spectrum of information available to marketers today.
Attribute
Zero-Party Data
First-Party Data
Second-Party Data
Third-Party Data
Accuracy
Highest: volunteered directly by the customer
High: observed behaviors, but may not reveal intent
High: sourced from a trusted partner’s first-party data
Variable: depends on aggregation quality
Consent Strength
Explicit: provided knowingly at point of capture (quiz, survey, preference)
Strong: implied or explicit via direct relationship (e.g., transactions)
Moderate: governed by partner contracts and agreements
Weak: often indirect; under increasing regulatory scrutiny
Freshness
Very fresh: updated when the customer chooses to share
Fresh: collected in real time from owned channels
Moderate: depends on partner update cadence
Variable: can be outdated or inconsistent
Scalability
Limited: depends on customer willingness to provide data
Moderate: scales with your own audience size
Moderate: scales with size/relevance of partner audiences
High: broad reach across demographics and geographies
Legal Defensibility
Very strong: easiest to defend under evolving privacy laws
Strong: compliant if consent is well managed
Moderate: defensibility depends on partner’s governance
Weak: most at risk from regulation and platform restrictions
Zero-Party Data
Zero-party data is the most transparent of the group.
It includes volunteered preferences, purchase intentions, and contextual details that customers share proactively, often through a preference center, quiz, or survey.
Because it comes with explicit consent, it offers the highest clarity for personalization.
The trade-off is volume: without a strong value exchange, customers won’t always take the time to provide it.
First-Party Data
First-party data strategy captures the behaviors and transactions a brand directly observes, such as website activity, email engagement, or purchase history.
It’s generally reliable and scalable, but it lacks insight into the customer’s underlying motivations.
You may know what someone clicked or bought, but not why.
Second-Party Data
Second-party data comes from partnerships. It’s essentially another company’s first-party data, shared under a formal agreement.
This can be especially useful for audience expansion or market testing, provided permissions are clear and transparent.
Third-Party Data
Third-party data is aggregated or brokered, often sourced from multiple providers.
Historically, it’s been the fuel for digital targeting at scale, but its usefulness is rapidly declining.
Privacy regulations, browser restrictions, and rising consumer skepticism have made third-party data less dependable and riskier to use for precision personalization.
When evaluating which type of data to rely on, marketers should apply a decision lens:
Accuracy: How closely does the data reflect reality?
Consent strength: Was it given directly and knowingly by the customer?
Scalability: Can it be collected consistently across the audience?
Freshness: How often is it updated and validated?
Legal defensibility: Can you use it with confidence under evolving regulations?
Zero-party data may not always provide the scale of first-party data, but it consistently scores highest on accuracy, consent, and defensibility, making it the gold standard in a privacy-first era.
Collection Experiences & Value Exchange
Best practices for collecting zero-party data aren’t about asking as many questions as possible. They’re about designing the right experiences at the right time, and making the value exchange crystal clear.
Customers are more willing to share when they understand exactly what they’ll get in return, whether that’s better product recommendations, faster service, or exclusive perks.
The most effective ZPD programs weave these interactions seamlessly into the customer journey so that providing data feels natural, not like an interrogation.
High-Performing Collection Moments
Preference Centers
Think of these as profile hubs tied to a customer’s account or email.
Building preference centers that convert is a skill: a well-designed preference center should be easy to find and easy to edit, empowering customers to update their communication choices, product interests, or purchase frequency at any time.
Progressive Profiling
Rather than front-loading every question during sign-up, ask for one or two high-value fields at natural points in the journey: during onboarding, after a purchase, or when someone downloads content. Over time, you build a richer profile without overwhelming the customer.
Quizzes & Product Finders
Interactive quizzes for data capture are short, mobile-friendly, and outcome-oriented.
A skincare brand might ask about skin type to recommend products; a retailer might use a product finder to suggest bundles or style guides. These tools deliver immediate value while capturing structured preference data.
Surveys & Micro-Polls
Lightweight in-journey prompts can gather quick insights in exchange for something tangible: a discount, early access, or a piece of premium content. The key is keeping these surveys or polls short and tightly linked to the benefit.
Conversational Forms & Chat
Guided Q&A, often through chatbots, can feel more natural than static forms. The responses are stored as structured data points in customer profiles, making them easy to activate downstream.
Loyalty & Membership Flows
Programs that trade perks for durable data, like favorite categories, preferred channels, or replenishment frequency, encourage ongoing engagement. Members get better offers, and brands get richer insights.
Design Principles
The most successful ZPD collection experiences follow a few simple but essential principles:
Clear value exchange: Tell users upfront what they’ll get (better fit, faster help, sharper deals).
Keep it brief: Limit each interaction to three to five concise questions; use progressive capture to build depth over time.
Purpose limitation: Only ask for data you will actively use, and show customers how it will improve their experience.
Plain language & accessibility: No dark patterns, jargon, or clutter; prioritize inclusive design and mobile-first usability.
Testing plan: Continuously test copy, incentives, question order, and which fields are required versus optional to optimize engagement.
When brands respect customer time and attention, collection becomes an exchange, not a transaction.
Activation Across the Journey
Collecting zero-party data is only half the story. The real impact comes from activating zero-party data across channels and across the customer lifecycle: using what people tell you to deliver more relevant, respectful, and effective experiences at every stage.
Done right, activation not only boosts performance but also signals to customers that their input is heard and valued.
Awareness / Prospecting
Preference-based lookalikes can help identify new audiences who resemble your best-fit customers.
Just as important, declared preferences allow you to suppress misaligned audiences, avoiding wasted spend and protecting brand perception by not targeting those unlikely to engage.
Consideration
When shoppers are weighing options, zero-party data enables you to personalize content modules, category pages, and promotional offers to the specific needs they’ve declared, whether that’s a style preference, a budget limit, or a use-case scenario.
Conversion
Zero-party data can be the trigger for decision aids such as comparison charts, personalized bundles, or financing options.
By aligning offers to declared intent or constraints, you reduce friction at the point of purchase and increase the likelihood of conversion.
Retention
After the sale, customer-declared preferences fuel tailored replenishment reminders, curated product recommendations, and communication sent via the customer’s preferred channels and timing. This keeps engagement relevant and helps prevent churn.
Advocacy
Zero-party data doesn’t just drive sales, it can also activate loyalty.
When customers share their interests or communities of choice, you can invite them to participate in reviews, referral programs, or community forums that align with those values.
From awareness through advocacy, the goal is the same: turn customer preferences into actions that enhance experience, deepen trust, and deliver measurable results.
Measurement & Governance
Zero-party data programs succeed when they’re both measurable and trustworthy.
Without a framework for tracking impact and governing data use, even the best collection strategies risk falling flat or eroding customer trust.
The key is to define clear KPIs and establish strong governance practices from day one.
Zero-Party Data Program KPIs
Opt-in & completion rate: Track how many customers begin and complete each data-collection experience, broken down by channel and audience segment.
Attribute fill rate & profile completeness: Measure how consistently customers are populating the most valuable fields in their profiles.
Accuracy & drift checks: Use techniques like reprompt cadences, contradiction detection, or “last validated” timestamps to ensure data remains fresh and reliable.
Uplift vs. control: Compare ZPD-powered journeys against control groups to quantify the lift in conversion rate, average order value (AOV), retention, customer acquisition cost (CAC) efficiency, and customer lifetime value (CLV).
Preference adherence: Monitor how often your brand honors declared channel, time, and topic preferences. Rising complaint or unsubscribe rates often indicate gaps here.
Zero-Party Data Governance Essentials
Consent capture & logs: Maintain centralized records of consent, preference changes, and revocations, with full history available for auditing.
Taxonomy & purpose mapping: Tie each data field to specific, documented uses and accountable teams. This prevents “data hoarding” and ensures clarity of purpose.
Data minimization & retention: Collect only what you need, apply expiry and re-validation rules, and quarantine stale attributes to avoid misuse.
Bias & fairness reviews: Regularly evaluate both the questions you ask and how you use the data to ensure fairness and inclusivity, minimizing the risk of discriminatory outcomes.
By coupling clear performance metrics with rigorous governance, brands can prove the business value of zero-party data while maintaining the transparency and accountability that keeps customer trust intact.
Tools & Workflow
To turn zero-party data into meaningful outcomes, brands need the right tools and workflows to connect the dots from capture to activation. A strong tool stack ensures that data moves seamlessly, stays accurate, and powers personalization at scale.
Experience Capture
Start with the tools that gather input: preference centers, survey and quiz builders, and conversational forms or chatbots.
These experiences should be easy to deploy, flexible to customize, and able to store outputs in structured formats.
Consent & Preference Management
Centralized consent records form the backbone of a privacy-first strategy.
By keeping a single source of truth for preferences and revocations, brands ensure compliance while making preference data accessible to both orchestration and analytics teams.
Data Foundation
Customer Data Platforms (CDPs) and CRMs, coupled with identity resolution tools, unify declared fields with behavioral history.
This creates a consolidated profile that blends what customers do with what they say, which is essential for personalization.
Orchestration & Personalization
Journey builders, testing frameworks, and dynamic content modules allow teams to act on zero-party data in real time.
Whether it’s customizing a website experience or tailoring an email sequence, orchestration tools ensure that ZPD attributes actually shape customer journeys.
Analytics
Finally, robust analytics close the loop.
Dashboards should track KPIs like opt-in rates, profile completeness, and field-level impact, while also flagging accuracy drift.
Alerts and test results help teams iterate quickly and prove the ROI of their efforts.
When each piece of this workflow is connected, zero-party data doesn’t just sit in a profile, it fuels a continuous cycle of capture, consent, orchestration, and measurement.
Implementation Considerations
Building a zero-party data program isn’t just about tools and tactics, it’s about execution.
The most successful initiatives start small, prove value, and then scale with discipline. Here are key considerations for making that happen:
Field Design Workshop
Begin with the decisions you want to improve, not with a laundry list of questions.
Design only the fields that directly power those decisions, and resist the urge to over-collect. This ensures every question has a purpose customers can see.
Pilot & Iterate
Launch with one or two high-leverage experiences, such as a product finder quiz or onboarding flow.
Measure the lift in engagement or conversion, refine your copy and incentives, then expand gradually.
Iteration builds momentum without overwhelming teams or audiences.
Operational Readiness
Assign clear ownership for taxonomy, accuracy checks, and sunset or re-validation cycles.
Zero-party data is not “set it and forget it.” It requires ongoing maintenance to remain useful and compliant.
Cross-Functional Playbook
Create a single source of truth that aligns marketing, product, customer success, legal, and data teams on consent capture, data purposes, and approved uses.
This prevents siloed practices and keeps programs defensible under scrutiny.
Change Management
Train internal teams on how to “ask with purpose,” adhere to preferences, and activate new fields in real time.
The faster teams can act on zero-party data, the more customers will see the benefit, and the more likely they’ll share again.
With the right foundation and governance in place, zero-party data becomes less of a project and more of a cultural shift toward transparency and customer-led personalization.
Why Zero-Party Data Wins
Zero-party data is the clearest, most consent-forward signal available to marketers, and the perfect complement to first-party behavioral insights.
It transforms personalization from guesswork into a customer-led process, where people explicitly share what they want and brands deliver accordingly.
The strongest programs pair value-rich collection moments with strict governance and measurable lift goals.
It’s not enough to capture data; the real impact comes from activating it across channels and journeys so customers see immediate benefits from what they share.
Above all, zero-party data should be treated as a living contract.
Preferences evolve, contexts change, and regulations shift. By re-validating, refreshing, and consistently respecting the data customers volunteer, brands can sustain both trust and ROI over the long term.
In a world where third-party signals are fading, zero-party data is more than just a workaround; it’s the foundation for lasting, transparent, and growth-driving relationships.
One of the most confusing things for advertisers on Meta is why performance seems to swing up and down for no obvious reason. One week your ROAS is incredible, the next week it feels like the account fell off a cliff. Most advertisers assume something’s broken — maybe a creative wore out, maybe the bidding changed, maybe the pixel isn’t firing.
But more often than not, what you’re seeing is simply the Explore–Exploit cycle of Meta’s ad algorithm at work. Understanding this cycle is critical if you want to scale without panic.
What the Explore–Exploit Cycle Means
At its core, Meta’s delivery system is both a search and an exploit algorithm.
Explore mode: The system invests budget to go out and find new people, many of whom aren’t yet ready to buy. During this phase, ROAS will usually look weaker because you’re building demand, not harvesting it.
Exploit mode: Once Meta identifies high-intent buyers, the system leans hard into them. Performance spikes, conversion rates look fantastic, and you might feel like you’ve hit a new permanent level.
Then, just as suddenly, things dip again — because you’ve harvested that pocket of demand and the system goes back into Explore mode.
Why This Feels Like a Roller coaster
For advertisers who only look at day-to-day ROAS, these cycles can feel like chaos. I can’t tell you how many times a client has called me after three poor days in a row, convinced something’s broken. In reality, nothing’s wrong. The system is just back in Explore mode, doing the slower work of creating new demand.
The key is zooming out. Depending on your product’s average order value and sales cycle, it may take 7 to 14 days before the demand you planted during Explore mode actually shows up in conversions.
A Real-World Example
We worked with a home goods brand that had an average order value of around $1,000. Their account was shut down for a week due to a billing issue. During that pause, sales looked great — because they were still reaping demand planted by the ads that had run the week prior.
But the week after turning ads back on? Atrocious performance. Why? Because that pipeline had dried up during the pause. It took another full week of Explore spending before sales stabilized again.
That’s the Explore–Exploit cycle in action. If you’d pulled the plug after that “bad” week, you’d never give the algorithm the time it needs to rebuild demand.
How to Manage the Cycle Without Panic
Measure in Weeks, Not Days Look at 7–14 day trends instead of reacting to daily fluctuations. This smooths out the natural oscillations.
Know Your Customer Journey If your product sells on first touch (like a $20 impulse buy), the cycle is shorter. If it’s a $1,000 purchase, the cycle is longer. Set expectations accordingly.
Don’t Overreact to Dips When you hit a bad week after a great one, remind yourself: this is the system exploring, not failing. Pulling back budgets too soon just starves future growth.
Balance Demand Creation and Harvesting Use prospecting campaigns to fuel Explore mode and retargeting to maximize Exploit mode. Both are necessary — ignore one and the other collapses.
The Bigger Picture
The Explore–Exploit cycle is baked into Meta’s DNA. It’s not a bug, it’s a feature. The system is constantly searching for new buyers and then doubling down on them once it knows who they are.
The advertisers who thrive are the ones who trust the process, ride out the valleys, and plan their strategies around the cycle instead of fighting it.
If you accept that volatility is normal, you stop chasing ghosts in your data and start focusing on the fundamentals: reach, frequency, creative, and consistent investment.
Closing Thoughts
Great weeks don’t last forever, and neither do bad ones. Meta’s algorithm is always oscillating between exploration and exploitation. The trick is not to panic when the pendulum swings.
If you can zoom out, understand the rhythm, and keep your budgets steady through the cycle, you’ll find that your account builds new baselines over time — and that’s how real, sustainable growth happens.
Have you ever clicked on a Google Ad and noticed a “?gclid=” string in the URL?
This little snippet of code is one of the most important keys to understanding your Google Ad performance.
The Google Click Identifier (GCLID) is how Google connects the dots between an ad click and a conversion.
Without it, you can’t reliably tie a purchase, form submission, or sign-up back to the exact campaign, keyword, and creative that drove it. It’s what makes Google Ads’ reporting and optimization features accurate.
Here’s how GCLID works:
Every time someone clicks on your Google Ad, a unique GCLID is added to the landing page URL.
That GCLID tells Google which ad was clicked, when, and under what targeting conditions.
When the person converts, that same GCLID lets Google attribute the result to the right ad spend.
Combined with UTMs, GCLIDs give you both precise Google Ads tracking and a full multi-channel view of performance.
Understanding how GCLID works, and how to make sure you’re capturing it correctly, means more accurate attribution, smarter budget allocation, and a clear picture of ROI.
What Is GCLID?
GCLID stands for Google Click Identifier, a unique tracking parameter that Google Ads automatically adds to your landing page URLs whenever someone clicks on your ad.
A GCLID might look something like this:
https://www.example.com?gclid=XYZ123
This seemingly random string is actually a powerful tracking tool.
Each GCLID is unique to a specific ad click and contains encoded information about the campaign, ad group, keyword, and other targeting details.
The purpose of a GCLID is simple but essential:
Link each ad click to its source so you can see which campaigns and keywords drive conversions.
Track performance across the customer journey, from the initial click to the final purchase or lead submission.
Enable more accurate attribution, helping you understand exactly where your ad spend is delivering the best return.
Because the GCLID is automatically generated and appended by Google Ads, there’s no manual setup required, but you do need to ensure your tracking systems capture and retain it for it to work effectively.
GCLID vs UTMs: How They Work Together
Marketers often confuse GCLID parameters with UTM tags, but they serve different, and complementary, purposes.
GCLID is automatic and generated by Google Ads when GCLID auto-tagging is enabled. It’s used primarily for Google Ads conversion tracking and reporting.
UTM parameters, on the other hand, are manual tags that you add to URLs yourself (or via a campaign builder tool).
UTMs are platform-agnostic and used for cross-channel tracking in analytics tools like Google Analytics, HubSpot, or Northbeam.
The table below highlights GCLID vs UTM:
When combined, GCLID gives you the click-level precision Google Ads needs, while UTMs provide the big-picture view across every marketing channel.
How Does GCLID Work?
GCLID works behind the scenes to connect ad clicks with conversion data, and it does this in just a few quick steps:
A user clicks your Google Ad. When someone clicks on your ad, Google automatically generates a unique Google Click Identifier tied to that specific click.
Google appends the GCLID to your landing page URL. The click sends the user to your destination URL with the GCLID parameter attached.
Your analytics tools capture the GCLID. Google Ads and Google Analytics 4 (GA4) read the GCLID and use it to connect the click to details like the campaign, ad group, keyword, and timestamp.
The GCLID links clicks to conversions. When a user completes a tracked action, such as filling out a form, signing up for a trial, or making a purchase, the GCLID makes it possible to attribute that conversion back to the exact ad interaction.
This process is instant and invisible to the end user, but for marketers, it’s the backbone of accurate Google Ads reporting and optimization.
Why Is GCLID Important?
GCLID isn’t just a random string in your URLs. it’s the key to making your Google Ads budget work harder. Here’s why it matters:
Conversion Tracking
Without GCLID, Google Ads can’t reliably match ad clicks to conversions.
By linking each conversion back to the specific click that drove it, you can measure the true impact of your campaigns and justify your ad spend with GCLID for conversion tracking.
Offline Conversions
Many conversions happen offline, like a phone order, in-store purchase, or signed contract.
If you capture the GCLID from the original ad click (e.g., in a CRM form), you can later upload that data into Google Ads to connect offline sales with the campaigns that generated them.
Attribution Accuracy
GCLID provides click-level data that helps you understand exactly which campaign, keyword, and creative led to a conversion. This level of detail allows you to:
Optimize bidding strategies
Pause underperforming ads
Double down on high-ROI campaigns
In short, if you’re not tracking GCLID, you’re flying blind on where your ad dollars are actually paying off.
How to Enable and Track GCLID
Setting up GCLID tracking takes just a few steps, and once it’s in place, you’ll have a stronger foundation for measuring and improving your Google Ads performance.
Enable Auto-Tagging in Google Ads
Auto-tagging automatically appends the GCLID parameter to your ad URLs.
To turn it on:
Sign in to your Google Ads account.
Click the Admin (wrench icon) in the top menu and select Account Settings.
Under Auto-tagging, check the box for “Tag the URL that people click through from my ad”.
Click Save.
You can test this by clicking on one of your own ads and confirming the URL includes a ?gclid= parameter.
Link Google Ads with Google Analytics 4 (GA4)
Wondering how GCLID works in GA4 and Google Analytics? Linking the two tools ensures your GCLID data flows between Google Ads and Analytics for full-funnel reporting.
To link accounts:
In Google Ads, go to Tools & Settings → Linked accounts.
Choose Google Analytics (GA4) and select the property you want to link.
Enable Personalized advertising and Auto-tagging override if prompted.
Capture GCLID in Your CRM for Offline Conversions
If you track leads or sales outside your website (phone calls, in-store purchases, signed contracts), capturing the GCLID lets you later upload offline conversions to Google Ads.
Best practices:
Add a hidden form field on your lead forms to store the GCLID from the URL.
Pass the value into your CRM or marketing automation platform.
When a sale closes, export the GCLID and upload it to Google Ads via the Offline Conversions tool.
Use Google Tag Manager (GTM) to Grab GCLID from URLs
Google Tag Manager can capture the GCLID without extra coding.
Here are the steps:
In GTM, create a URL Variable that pulls the gclid query parameter.
Store this variable in a cookie or send it to your analytics platform.
Use it for attribution and offline conversion uploads.
With these steps in place, every ad click, whether it converts online or offline, can be accurately tracked and attributed back to the campaign that drove it.
Common Issues with GCLID and How to Fix Them
Even if you’ve enabled auto-tagging, GCLID tracking can break without you realizing it, leading to incomplete or inaccurate data.
Here are the most common issues and how to resolve them.
Lost GCLID During Redirects
Issue: When your landing page URL redirects (for example, from http:// to https://, or via geo-redirects), the GCLID parameter can be stripped from the URL.
Fix:
Ensure all redirects pass query parameters to the final destination URL.
Update your server or CMS settings to preserve parameters during redirects.
Auto-Tagging Not Enabled in Google Ads
Issue: Without auto-tagging, GCLIDs will never be appended to your URLs in the first place.
Fix:
In Google Ads, go to Account Settings → Auto-tagging and check “Tag the URL that people click through from my ad”.
Test by clicking on a live ad and confirming the GCLID appears in the address bar.
Tracking Discrepancies Between Google Ads and Analytics
Issue: You see conversions in Google Ads that aren’t reflected in Google Analytics, or vice versa.
Fix:
Confirm your Google Ads and GA4 accounts are linked.
Check that your GCLID in Google Analytics property is set to collect Google Signals data.
Ensure time zones and attribution settings align between platforms.
Troubleshooting Checklist
Use this quick checklist to identify and fix GCLID tracking issues:
Click on a live Google Ad and check for ?gclid= in the URL.
Confirm redirects are preserving query parameters.
Test landing pages in Google Tag Assistant to verify GCLID auto-tagging is working.
Review the linking status of GCLID in Google Analytics and Google Ads.
Check form and CRM setups for proper GCLID capture.
Best Practices for GCLID Tracking
To get the most value from GCLID data, and ensure you never lose critical conversion insights, follow these best practices:
Always Enable Auto-Tagging in Google Ads
Auto-tagging is the easiest way to ensure GCLIDs are appended to your URLs. Without it, you’ll lose click-level tracking in Google Ads.
Test Ad URLs Regularly
Periodically click on your live ads to confirm the ?gclid= parameter appears in the address bar. This helps catch broken tracking before it impacts reporting.
Capture GCLID in Your Backend Systems
Add a hidden form field to your lead capture forms to store the GCLID from the landing page URL. Pass this value into your CRM so it’s available for offline conversion uploads.
Combine GCLID Data with UTM Parameters
GCLID provides precise Google Ads tracking, while UTMs give you a cross-channel view. Using both ensures you can attribute conversions across your full marketing mix.
Preserve GCLID During Redirects
If your site uses redirects, make sure query parameters are passed along to the final landing page so you don’t lose tracking data.
With these best practices in place, you’ll have a reliable, consistent flow of data, giving you confidence in your ROI calculations and campaign optimizations.
The Bottom Line on GCLID
The Google Click Identifier (GCLID) may be just a small snippet in your URL, but it’s a big deal for marketers who want accurate, actionable insights from their Google Ads campaigns.
By capturing and preserving GCLIDs, you can:
Confidently link ad spend to conversions
Track offline sales alongside online actions
Gain click-level attribution for smarter optimization
Whether you’re running small campaigns or managing a large ad budget, getting GCLID tracking right is essential for measuring ROI and making data-driven decisions.
For most advertisers, ROAS is the north star. It’s the number that gets shared in boardrooms, the metric that determines whether a campaign is celebrated or cut.
But after years of working with brands that spend millions each month on paid social, I can tell you: ROAS alone doesn’t tell the whole story. In fact, it can often hide the very signals that determine whether your advertising is growing the business or just chasing its own tail.
Why ROAS Isn’t Enough
ROAS is seductive because it looks so clean. Spend a dollar, get three back — what’s not to love? The problem is, you can generate strong ROAS numbers while shrinking your brand’s future.
Here’s a common scenario: a campaign shows stellar ROAS, but when you zoom out you realize it’s just hitting the same warm audience over and over again. Incremental reach is declining. Year-over-year, fewer new people are hearing about the brand, but you’d never see that looking at ROAS alone.
If you only optimize for purchases, you risk overfishing the same pond. It feels good in the short term, but long term you’re missing the forest for the trees.
The Zero-Click Consumer
Part of the challenge is how consumers actually behave today. We’re living in a “zero-click” environment. People screenshot ads, search the brand on Google, jump into Amazon, or check out a review on YouTube. They might not click your ad at all, but it still played a role in their path to purchase.
That means click-through rates and last-click ROAS are losing relevance.
If you’re not paying attention to reach, frequency, and the quality of your creative, you’re blind to a huge portion of the customer journey.
Reach and Frequency: The Real Fundamentals
Think of reach and frequency as your advertising vital signs.
Reach tells you whether you’re consistently finding new people at scale.
Frequency makes sure you’re not burning out the same audience with repetitive impressions.
On Meta, for example, only about 20% of users are actively “in-market” at a given time. If your campaigns are only optimized for purchases, you’re just fighting over that small slice of users.
You’re leaving 80% of your potential future buyers untouched.
That’s why it’s critical to balance harvesting demand with creating it. The healthiest campaigns don’t just convert the people who are ready right now — they plant seeds with the people who will be ready in a week, a month, or even a year.
Beyond ROAS: Smarter Metrics for Today’s Advertiser
To track this bigger picture, I rely on a few custom metrics:
First-Time Incremental Reach (FTIR): What percentage of your impressions are actually reaching net-new people?
Purchases ÷ Reach: A directional conversion rate that shows how effectively you’re turning reach into sales.
Quality Click Percentage (QCP): Landing page views ÷ link clicks — basically a reverse bounce rate that uncovers issues like slow load times or poor mobile experiences.
New Customer %: The share of purchases coming from people who have never bought before. A rising percentage shows you’re expanding your brand’s base, while a declining one is a warning sign that you’re just recycling the same customers.
None of these replace ROAS.
But together, they create a much clearer view of whether your advertising is truly expanding your brand.
Advertising Is Coming Full Circle
In many ways, paid social today looks a lot like TV advertising decades ago. The question isn’t just “what did this spot return?” but “how many people did we reach, how often, and how strong was the creative?”
The fundamentals are back: consistent reach, healthy frequency, and compelling creative that entertains, engages, and earns attention. ROAS still matters, but it’s just one piece of the puzzle.
Closing Thoughts
If you only measure ROAS, you’re probably missing the very growth you’re trying to buy. Brands that win in today’s landscape are the ones that step back, look beyond short-term performance, and invest in reach, frequency, and creative fundamentals that build demand over the long haul.
That’s not just theory. It’s the reality I see every day managing large-scale accounts. The advertisers who understand this shift don’t just report good numbers — they build brands that last.
Everywhere you look, headlines warn about “new privacy protections” that will nuke your tracking. The legacy of iOS 14.5 looms. People are freaking out.
iOS 26 is expected to drop mid-September, right during the lead up to Black Friday Cyber Monday. But Apple’s own release notes on iOS 26 are sparse.
Is this another watershed update that will upend all our digital strategies?
Or is all this just FUD spread around by ecommerce SaaS tools seeking to capitalize on fear?
Customers are asking us if Northbeam’s parameters are on the chopping block. So we went into the lab and tested it ourselves. The results are boring – in the best way.
Apple’s Crusade: Privacy, or Hegemony?
Marketers have been living with Apple’s privacy standards for two decades.
iOS 16 and subsequent updates layered on more ITP rules, fingerprinting protections, and anti-cookie mechanics.
Every single update set off alarms in the DTC ecosystem. In the past, growth marketers were able to thrive by hacking Meta’s extreme tracking functionality, to squeeze maximum results out of complex targeting setups.
Every update removed depth of tracking, upsetting targeting and attribution strategies that growth marketers relied on. And now, with iOS 26, the cycle repeats.
The difference? This time there’s no actual change in UTM behavior.
Marketers on X were quick to clutch pearls, but we decided to test: how would iOS 26 affect Northbeam data?
Our Independent Testing
Instead of speculating, we ran controlled experiments across multiple devices in our Apple device lab.
Here’s how:
We constructed test URLs with both creative identifiers (nbt, utm_source) and click IDs (fbclid, gclid).
Loaded the URLs in Safari via different paths: clicking in Messages, Mail, Facebook in-app browser, direct typing, private browsing, and with “Advanced Tracking Protection” enabled.
Observed which parameters remained in the Safari URL bar and which disappeared.
We ran this on:
An iPhone on iOS 18
An iPhone on iOS 26 beta
Two Macs on current macOS builds
What We Found
Finding 1: Nothing new in iOS 26 UTM removal behavior is identical between iOS 18 and iOS 26. Anyone saying this is a “new privacy rule” is misinformed.
Finding 2: Creative UTMs are safe For URL parameters that are meant to identify ad creatives — they were not removed by the Safari browser. These include:
nbt (Northbeam’s signature tracking parameter)
utm_source
utm_medium
utm_campaign
utm_term
utm_content
nbt, utm_source, utm_medium, etc.
URL is clicked in Messages app
URL is clicked in Mail app
URL is typed directly into Safari
URL is clicked in Facebook app and opens in Facebook in-app browser
Safari (default settings)
UTM not removed
UTM not removed
UTM not removed
N/A
Safari (private browsing)
UTM not removed
UTM not removed
UTM not removed
N/A
Safari (with Advanced Tracking and Fingerprinting Protection)
UTM not removed
UTM not removed
UTM not removed
N/A
Facebook in-app browser
N/A
N/A
N/A
UTM not removed
Finding 3: Click IDs can get stripped For URL parameters that are meant to identify click IDs, session IDs, or user IDs (generally used by advertising platforms for granular tracking), those are removed by the Safari browser under certain conditions. These include:
fbclid
gclid
msclkid
twclid
dclid
yclid
fbclid, gclid, msclkid, etc.
URL is clicked in Messages app
URL is clicked in Mail app
URL is typed directly into Safari
URL is clicked in Facebook app and opens in Facebook in-app browser
Safari (default settings)
UTM removed
UTM removed
UTM not removed
N/A
Safari (private browsing)
UTM removed
UTM removed
UTM removed
N/A
Safari (with Advanced Tracking and Fingerprinting Protection)
UTM removed
UTM removed
UTM removed
N/A
Facebook in-app browser
N/A
N/A
N/A
UTM not removed
But note: none of this applies to the original ad click itself. If someone taps your Meta ad in the Facebook app, the click ID is preserved. It’s only when people copy/paste or share the URL later that stripping may occur.
What This Means for You
First, don’t buy into the panic.
Northbeam’s parameters remain unaffected. Our nbt tag and standard UTMs are untouched, across all devices and configurations.
Any tracking degradation you’ve heard about is not new. This has been happening since iOS 18.
If you see performance dips in Northbeam, it’s not because tracking broke. It’s because your underlying ad platforms are delivering weaker results.
Platforms that rely heavily on click IDs (Meta’s fbclid, Google’s gclid) are the ones at risk of degraded reporting in edge cases. Even then, the impact is limited to private browsing, Messages links, and advanced settings that only a fraction of users enable.
So if you’ve been worried about iOS 26 wiping out your attribution stack: breathe. It’s a nothingburger.
Why Good Attribution Still Matters
Even if this round of panic is overblown, the bigger picture hasn’t changed. Apple is steadily tightening the walls around user-level data. Platforms are incentivized to over-report their own performance. Ad auctions are only getting more competitive.
With Northbeam multi-touch attribution, you’re not relying on single-point metrics like ROAS or click IDs that can disappear when Apple flips a switch. You’re measuring across every touchpoint: clicks, views, and conversions on Meta, Google, TikTok, YouTube, Shopify, and more.
Instead of guessing which channel drove a sale, or waiting six weeks for an incrementality test, you get a real-time, deduplicated, revenue-backed picture of what’s working.
That clarity is what allows you to scale spend confidently, cut wasted budget, and prove profitability to finance.
This is the reason top ecommerce brands use Northbeam as their source of truth. It’s not just about surviving Apple’s latest update. It’s about future-proofing your entire growth engine.
Bottom Line
In other words: ignore the noise. Focus on building a growth strategy backed by attribution you can trust.
If you’ve ever clicked a Facebook or Instagram ad and noticed a strange string containing “fbclid” in the URL, you’re not alone.
Marketers often come across this mysterious parameter without knowing what it does — or why it matters.
This parameter, fbclid (short for Facebook Click Identifier), plays a critical role in Meta ad tracking.
It allows marketers to tie individual clicks to on-site behavior and downstream conversions, making it essential for accurate attribution and campaign optimization.
In this guide, we’ll break down exactly what fbclid is, how it works, and why it matters. You’ll learn how to capture and track it using tools like Google Analytics and Google Tag Manager, avoid common pitfalls, and follow best practices to ensure you’re getting the most out of every Meta ad click.
What is fbclid?
If you’ve ever looked at a URL from a Facebook or Instagram ad and noticed a string like “?fbclid=XYZ123” tacked on the end, you’ve encountered the Facebook Click Identifier, or fbclid.
This parameter is automatically appended by Meta when a user clicks on an ad or link within its ecosystem.
At its core, fbclid is a unique tracking parameter that helps Meta monitor user behavior after an ad click.
It enables click-level attribution by linking the specific click to actions that occur further down the funnel, such as a page view, add-to-cart, or purchase.
This level of detail is essential for measuring campaign effectiveness and optimizing ad performance.
fbclid vs. UTMs: How They Work Together
It’s important to understand how fbclid and UTM parameters differ, and how they can work in tandem to improve tracking and attribution.
UTMs are manual tags created by marketers to track traffic across platforms like Google Ads, email campaigns, affiliate programs, and more.
They’re flexible and customizable, making them ideal for multi-channel tracking and reporting in tools like Google Analytics.
fbclid, on the other hand, is a Meta-specific parameter that is automatically added to URLs when users click on Facebook or Instagram ads.
It enables click-level attribution by linking user engagement to conversion events within Meta’s ad ecosystem.
While they serve different purposes, the best practice is to use fbclid and UTMs together.
fbclid helps Meta attribute performance within its platform, while UTMs offer visibility across other marketing channels.
When combined, they provide a more complete and accurate picture of how your campaigns are performing, both inside and outside of Meta.
Why fbclid Matters
For marketers running Meta campaigns, fbclid plays a critical role in connecting the dots between ad clicks and business outcomes. Without it, key insights into performance and customer behavior can easily be lost.
Here’s why fbclid matters:
Enables Accurate Attribution in Meta Ads Manager
fbclid allows Meta to track exactly which ad a user clicked before taking an action like completing a purchase or signing up.
This level of attribution helps marketers understand which creatives, audiences, and placements are driving real results.
Connects Click Behavior to Conversion Events and Audiences
By capturing fbclid on your website or landing page, you can feed back valuable conversion data into Meta’s platform.
This enhances event matching, strengthens reporting, and fuels more effective ad optimization.
Improves Retargeting and Lookalike Audience Quality
When fbclid is captured and tied to user behavior, Meta can better retarget warm leads and build high-quality lookalike audiences based on real post-click engagement, not just impressions or views.
Prevents Loss of Traffic Data in Analytics Platforms
Without fbclid (or with improper setup), traffic from Facebook and Instagram can show up as “direct” in tools like Google Analytics.
This muddies your data and makes it harder to attribute success to the correct channels.
In short, fbclid isn’t just a random string in your URLs, it’s the key to unlocking full-funnel visibility and driving better campaign performance on Meta.
How fbclid Works
The fbclid parameter may look like a small string of characters in your URL, but behind the scenes, it’s quietly powering some of the most important attribution and optimization features in Meta’s ad platform.
Here’s a simple breakdown of how fbclid works:
A user clicks on your Facebook or Instagram ad. When someone engages with your ad and clicks through, Meta initiates the fbclid tracking process automatically.
Meta appends a unique fbclid value to the URL. The moment the user is redirected to your landing page, Meta attaches the fbclid parameter to the end of the destination URL
Your website captures the fbclid value. Tools like Google Tag Manager, GA4, or your CRM can be set up to grab this value and store it for tracking or matching later.
The fbclid is tied to user behavior and conversions. As users browse your site, take action, or convert, that unique fbclid helps Meta understand which ad interaction led to those results, enabling better fbclid attribution and retargeting.
How to Use fbclid
To make fbclid work for you, it’s not enough to let Meta add the parameter: you need to capture it on your website and pass it into your analytics or CRM systems or valuable click-level attribution data may be lost.
Here’s how to capture and track fbclid using common tools:
Google Analytics 4 (GA4)
Google Analytics doesn’t automatically display fbclid, but you can set it up to capture and store it: use Google Analytics’ built-in ability to log URL parameters in event data, or create a custom dimension to track fbclid across user sessions.
Once captured, fbclid values can be tied to conversion events and help validate Meta Ad tracking reporting with fbclid.
💡 Pro tip: Create a Google Analytics exploration that segments users by presence of fbclid to analyze Meta-driven behavior.
Google Tag Manager (GTM)
If you use GTM, you can create a variable to pull the fbclid value from the URL and send it to GA4, your CRM, or other platforms.
First, create a URL Variable in GTM to capture fbclid. Then set up a Custom Event Tag to store the variable in GA4 or pass it to other tools. Optionally, write it to a cookie or session storage for use across pages
💡 Pro tip: This is especially helpful when users navigate beyond your landing page and you want to preserve the fbclid across sessions.
Server-Side Tracking or CRM Integration
For advanced setups, you can capture the fbclid server-side or send it directly into your CRM.
Use scripts on your landing page to extract the fbclid and associate it with a user or session. Store it in hidden form fields, database entries, or session cookies.
💡 Pro tip: Send the fbclid with conversion events to platforms like Meta, HubSpot, or Salesforce for offline event matching.
Code Snippet (for Developers or Technical Marketers)
Here’s a simple JavaScript snippet that captures fbclid and stores it in a cookie:
// Capture fbclid in URLsconst urlParams = new URLSearchParams(window.location.search);
const fbclid = urlParams.get('fbclid');
if (fbclid) {
document.cookie = `fbclid=${fbclid}; path=/; max-age=2592000`; // 30 days}
💡 Pro tip: Once in a cookie, the fbclid can be accessed on future page loads or submitted with forms.
Common Challenges with fbclid
While fbclid is a powerful tool for Meta ad attribution, it’s not always plug-and-play.
Many marketers miss out on its full benefits due to setup gaps, misunderstandings, or technical issues.
Here are four of the most common challenges:
1. Lost fbclid During Redirects
Just like UTM parameters, the fbclid can be stripped out during redirects, especially if your landing page URL passes through a link shortener, third-party service, or tracking tool.
2. Incomplete Setup in Analytics Tools
Capturing fbclid requires intentional configuration in tools like GA4, GTM, or your CRM.
If your setup doesn’t explicitly track this parameter, it won’t show up in reports, leading to underreporting of Meta ad tracking performance for fbclid.
3. Misunderstanding Its Role vs. UTMs
A common misconception is that fbclid can replace UTM parameters.
In reality, it’s not an either/or: fbclid serves a unique purpose within Meta’s ecosystem, while UTMs provide broader cross-platform tracking.
4. Privacy Concerns
Some marketers worry that fbclid might contain personally identifiable information (PII).
Fortunately, fbclid is fully anonymized: it’s just a unique identifier used for tracking a session, not a specific person.
Best Practices for Using fbclid
To get the most out of fbclid, marketers need more than just awareness. They need systems in place that consistently capture, preserve, and leverage this data for smarter fbclid attribution.
Here are four best practices to follow:
1. Preserve fbclid Through Redirects
Make sure your landing page setup, including any redirect chains, doesn’t strip fbclid in URLs.
Whether you’re using link shorteners, third-party tools, or campaign tracking layers, test for fbclid retention across every step.
2. Combine fbclid with UTM Parameters
fbclid gives you deep visibility into Meta ad performance, but it doesn’t replace UTMs.
To understand cross-channel journeys and build cohesive fbclid attribution models, you should use both fbclid and UTM tags together.
3. Audit Your Analytics and CRM Setup Regularly
Even small changes in your site structure, forms, or analytics tools can break fbclid capture.
Set a recurring check-in (monthly or quarterly) to confirm that fbclid is still being tracked correctly in GA4, GTM, or your CRM.
4. Use Tools That Unify fbclid With Other Attribution Data
Manual fbclid tracking can only take you so far.
Platforms like Northbeam help reconcile fbclid with your broader attribution stack, from Google Ads to influencer traffic, so you can measure Meta’s true impact in a multi-touch journey.
Make fbclid Work For You
The fbclid parameter may be invisible to most users, but it’s essential to marketers who want accurate, reliable Meta ad or fbclid attribution.
When properly captured and paired with UTM parameters, fbclid provides the click-level visibility needed to understand what’s actually driving conversions, and where your budget is making the biggest impact.
From improving retargeting accuracy to preventing misattributed “direct” traffic, fbclid is a small but powerful tool in your tracking toolkit.
The future may be uncertain, but one thing is not: we have more data at our fingertips than ever before.
And in this era of big data in marketing, how we market has fundamentally changed.
Sorry, Mad Men lovers: the days when marketing decisions were based entirely on intuitive assumptions or creative instincts is fading.
While creativity matters more now than ever before due to the mass proliferation of AI-generated campaigns, decisions in today’s competitive marketing environment have to be backed by cold, hard data.
This is reflected by the rise of data-driven marketing. Within this strategy, decisions must be driven first and foremost by data.
In this blog post, we’ll delve into what data-driven marketing is, why it matters, challenges and opportunities, and practical frameworks to implement data-driven marketing in your overall strategy.
What is data-driven marketing?
At its core, data-driven marketing is the approach of leveraging data to make marketing decisions and measure success.
Sounds simple, right?
But data-driven digital marketing goes beyond spreadsheets and platform data.
What we’re talking about is the distinction between setting a strategy and then using data to measure results, and looking at data at each step of the process.
With a data-driven approach, you would:
Use data to establish a complete understanding of customer behavior, preferences, and pain points
Use data to derive a strategy based on customer data points
Use data to validate and refine your initial data-backed hypothesis
Use data to measure and communicate results
Repeat
A data-driven approach might take advantage of tools like customer segmentation, personalized campaigns, predictive analytics, A/B testing, and more.
The maxim that numbers don't lie has never been more true.
When marketing strategy is based on data rather than theories, you’re more likely to set yourself and your team up for success with each and every dollar.
And rather than eliminate room for experimentation, having a data-backed strategy lets you experiment within a more controlled environment, giving you a better shot at achieving your goals.
How does data-driven marketing compare to traditional marketing?
Traditional marketing was once ruled by gut instinct and broad messaging: marketers made big bets based on limited data, hoping their campaigns would stick.
But today, with a flood of customer data at our fingertips, modern marketing is far more precise.
Here’s how data-driven marketing stacks up against traditional methods:
Data-driven digital marketing isn’t just a modern upgrade, it’s a fundamental shift in how teams plan, execute, and evaluate campaigns.
By embracing data at every step, marketers can stop guessing and start growing with confidence.
Why does data-driven marketing matter?
In today’s competitive landscape, marketing can’t afford to be a guessing game. Data-driven strategies give teams the clarity they need to focus on what works, and cut what doesn’t.
By grounding decisions in real-time data, marketers can:
Optimize ROI by allocating budget toward the channels, audiences, and creatives that actually drive revenue — not just clicks.
Personalize experiences at scale, delivering relevant content based on user behavior, preferences, and intent.
Measure success with precision, using attribution models and performance dashboards to tie every campaign to concrete outcomes.
Ultimately, data-driven marketing turns strategy into science, enabling smarter decisions, stronger results, and sustainable growth.
How is data-driven marketing used?
Data isn’t just for dashboards: it drives real decisions every day.
Here are five ways to put big data in marketing to work:
Customer segmentation → personalized campaigns
By analyzing behavior, purchase history, or engagement patterns, brands can create highly tailored messaging that resonates with specific audience segments.
Predictive analytics → demand forecasting
Machine learning models can anticipate future behavior, like when a customer is likely to purchase again, helping marketers plan inventory, timing, and spend more effectively.
Multi-touch attribution → smarter ROI tracking
Instead of giving all the credit to the last click, multi-touch models show how different channels work together, so you can allocate budget based on true performance.
Churn prediction → retention strategies
By flagging users at risk of dropping off, marketers can trigger win-back campaigns or special offers to re-engage them before it’s too late.
Data-driven A/B testing helps teams understand which images, headlines, or formats perform best, turning creative from a guess into a growth lever.
Data-driven marketing strategies across industries
While the principles of data-driven digital marketing stay consistent, the strategies look different depending on the industry.
Here’s how businesses are tailoring their approach, including data-driven marketing examples across e-commerce, B2B SaaS, Retail, Media & Publishing, and Finance & Insurance:
E-commerce
Brands use browsing behavior, purchase history, and abandoned cart data to deliver hyper-personalized product recommendations, email flows, and promotions.
B2B SaaS
Lead scoring models prioritize sales outreach based on engagement signals, helping teams focus on high-intent accounts and shorten the sales cycle.
Retail
In-store and online data is combined to create unified customer profiles, enabling loyalty programs, localized promotions, and smarter inventory planning.
Media & Publishing
Audience data informs content strategy, ad placements, and subscription offers, ensuring the right content reaches the right reader at the right time.
Finance & Insurance
Predictive analytics helps identify cross-sell and upsell opportunities, detect fraud, and customize offers based on life stage or risk profile.
No matter the vertical, one thing holds true: the more aligned your strategy is with your data, the better your outcomes will be.
How can you implement data-driven marketing?
Data-driven marketing is a cyclical process, not a one-and-done task.
To get the most value from your data, you need a structured approach that supports continuous learning and improvement.
Here’s a six-step framework to follow:
Step 1: Collect
Start by gathering first-party data from your website, CRM, email platform, and ad campaigns. Centralize it using tools like customer data platforms (CDPs) to build a unified view of your customers.
Step 2: Analyze
Use analytics tools (like Northbeam, Google Analytics, or BI platforms) to surface insights. Identify patterns in performance, behavior, and attribution that can inform smarter decisions.
Step 3: Strategize
Translate those insights into action. Adjust your targeting, messaging, and channel mix to align with what the data reveals about your most valuable audiences.
Step 4: Execute
Launch campaigns with clear hypotheses and measurable goals. Whether it's a new audience segment or a refreshed creative concept, treat execution as a testable experiment.
Step 5: Measure
Track performance in real time. Go beyond vanity metrics and measure what matters — conversions, LTV, CAC, and multi-touch attribution.
Step 6: Optimize
Use what you’ve learned to refine your approach. Iterate on campaigns, reallocate budget, and double down on what’s working. Then go back to Step 1 and do it better.
What are the benefits of data-driven marketing?
Data-driven marketing isn’t just a buzzword, it’s a smarter, more efficient way to grow.
Here’s how a data-centric approach pays off for modern marketing teams:
Optimized marketing spend
Stop wasting budget on campaigns that don’t convert.
With data-driven insights, you can focus your spend on the channels, audiences, and creatives that deliver the best return on investment.
Better personalization and targeting
Big data in marketing allows you to move beyond broad segments and craft personalized experiences that resonate.
From dynamic product recommendations to tailored email flows, personalization drives deeper engagement and higher conversions.
Improved decision-making backed by evidence
No more guesswork. If you’re wondering how to use data in marketing, lean on data-driven strategies to give you concrete evidence to support strategic decisions, whether it’s launching a new campaign, shifting budget, or testing creative variations.
Enhanced customer insights
Behavioral data reveals what your customers actually want, not just what you assume.
By understanding browsing patterns, purchase behaviors, and engagement signals, you can refine your messaging and product positioning.
Agile, real-time campaign adjustments
With the right tools, you can monitor performance in real time and pivot quickly.
Data-driven teams don’t have to wait for post-campaign reports — they adjust on the fly to maximize results.
Of course, harnessing data’s full potential comes with its own set of challenges, from data privacy concerns to the pitfalls of misinterpreting metrics.
Let’s look at the common obstacles that can trip up even the most data-driven teams so we can best learn how to use data in marketing.
What are the challenges of data-driven marketing?
While data-driven digital marketing unlocks huge opportunities, it’s not without hurdles.
Teams that dive in without a solid foundation can fall into these common traps:
Overreliance on platform-reported analytics
Relying solely on ad platforms’ metrics often paints an incomplete picture.
Without a multi-touch attribution model, it’s easy to over-credit last-click conversions and miss the bigger story of how channels work together to drive results.
Data privacy and compliance concerns
With regulations like GDPR and CCPA, marketers need to be hyper-aware of how they collect, store, and use customer data.
Mishandling sensitive information can lead to legal issues and erode customer trust.
Misinterpreting correlation as causation
Not every data trend signals a direct cause-and-effect relationship. Jumping to conclusions without proper analysis can lead to misguided strategies and wasted resources.
Human error in manual reporting
When teams rely on spreadsheets and manual data entry, mistakes are inevitable.
These errors can skew insights, misinform decisions, and slow down your ability to act on what’s working.
Despite these challenges, the future of data-driven marketing is more promising, and more powerful, than ever.
With advancements in AI, predictive analytics, and privacy-first technologies, the next wave of tools is reshaping how marketers turn data into results.
What are some future trends in data-driven marketing?
As technology evolves, so does the way marketers leverage data.
The next frontier of data-driven digital marketing is being shaped by smarter tools, stronger privacy regulations, and a shift toward predictive, proactive strategies.
Here’s what’s on the horizon:
AI and machine learning
AI is no longer a buzzword, it’s becoming the backbone of advanced marketing analytics.
Machine learning models can identify patterns at a scale no human could, enabling faster insights, automated optimizations, and even predictive content recommendations.
Marketing intelligence platforms
As data sources multiply, marketing intelligence platforms are emerging as the single source of truth.
Platforms like Northbeam integrate data across channels, visualize performance, and deliver actionable insights, helping teams align strategy, execution, and measurement in one place.
Predictive analytics for proactive decisions
Rather than reacting to past performance, marketers are starting to forecast future outcomes.
Predictive models help teams anticipate customer behavior, forecast demand, and allocate resources with greater confidence, leading to more strategic, data-backed decision-making.
Privacy-first data strategies
With third-party cookies on the decline and data privacy regulations tightening, marketers are shifting toward first-party data strategies.
Tools that respect user privacy while still delivering deep insights — like server-side tracking and consent-based data collection — will define the next era of responsible marketing.
Conclusion
Data-driven marketing isn’t about replacing creativity—it’s about giving it direction. When insight guides imagination, teams move faster, waste less, and build compounding advantage. Start with clean first-party data, align on the metrics that matter, and commit to a test-learn-optimize loop
Equip your team with an intelligence platform like Northbeam to unify data, attribute impact, and surface next steps. Most importantly, make decisions transparent and repeatable so wins can be scaled. The future will reward marketers who treat data as a product and learning as a habit.
Begin now, iterate relentlessly, and let results—not guesses—power your growth. Test boldly, measure clearly.
If you’re up-to-date on your alphabet soup, you may know that multi-touch attribution (MTA), media mix modeling (MMM) and incrementality work together to give you a full picture of your campaign performance. They all provide useful answers as you assess what’s working and what’s not — just to different types of questions.
And if you’re not as familiar with these terms, let’s talk about it. Read on for more information about MTA, MMM, and incrementality so you can understand how they work in concert to give you the information you need to make informed marketing decisions.
Multi-Touch Attribution (MTA)
MTA is major; we have an entire guide dedicated to it.
The TL;DR is this: MTA is a method of marketing attribution that accounts for all of the different touchpoints and activities in a customer’s journey — not just the first or last touch.
While first or last touch attribution gives all of the credit to a single touch on the customer journey, MTA understands that the customer’s path to a conversion isn’t linear, and aims to distribute credit (and attribution) across all relevant touchpoints.
MTA is your go-to tool for the day-to-day. It gives you attribution information on a granular level about campaign performance so you can make decisions on where to spend and adjust your budget on a given day in order to optimize for profitability.
What worked last week may not work any more; market and consumer variables are constantly shifting and making your ad performance fluctuate. MTA lets you stay on top of those shifts across all of your digital channels.
Checking out your MTA on a regular basis helps you keep your finger on the pulse so you can make regular adjustments and know what’s happening based on hourly or real-time performance data.
The cons of MTA? It’s primarily focused on digital touchpoints, so if you do a lot of traditional marketing, it may not capture attribution for all of your activities. That’s where MMM comes in.
You should use MTA if:
You want to know the contribution of each touch point to a final conversion
You want granular, real-time data
You want a go-to resource to check daily to inform your spend decisions
Media Mix Modeling (MMM)
While MTA goes deep on individual touchpoints, Media Mix Modeling (MMM) goes deep on individual channels and tactics. MMM gives you a top-down look at how each channel is performing so you can fill in the gaps of your MTA reporting.
MMM goes beyond the day-to-day to incorporate all of your past and present data as well as seasonality, market trends, and more to inform your strategy and help you allocate resources.
Rather than a tool to help you make everyday decisions, MMM can be thought of as a powerful forecasting and budgeting tool in the longer term. It helps you understand the marginal efficiency of each channel, and where you might start to see diminishing returns.
Let’s say that one of your channels is performing at an 8x ROAS (return on ad spend). With just that data point, you might scale your ads indefinitely and pour more money into that channel. But proper MMM will tell you that if you spend more than a certain amount per day there, you’ll stop seeing returns.
Both pieces of data are needed to make the best decision possible: MMM puts integral parameters around your MTA insights.
A major pro of MMM: it can give you information about both digital and traditional channels, both online and offline efforts. Not only can you see how your ads and influencers are performing, but you’ll get data about your billboards and commercials as well.
Another pro: MMM shows you the limits of what’s possible with the resources you have available — regardless of what your boss says.
Marketing teams are often given budgets that are difficult to work with. With MMM, you can input your budget and see if your goals are possible based on historical channel performance so you can set realistic goals and make your case using data.
The cons? MMM tools are advanced, and you may need support to set up or interpret your outputs. Depending on your provider, they may also take longer to run — while Northbeam provides regular MMM reports, other providers can often only deliver these insights on a quarterly basis.
Still, MMM is powerful. The best MMM tools will give you suggestions on how to shift your budget to optimize for performance based on your goals. You can do multiple MMMs by regions or marketplaces, metrics, and more. You can rework the numbers to see how your channels are performing against new customer acquisition, total revenue, or any other metric you want to measure. MMM is a flexible and dynamic way to understand your marketing attribution.
You should use MMM if:
You want to understand when you’ll start seeing diminishing returns and/or room to scale in a given channel
You want to establish and track a long-term strategy that involves a variety of different marketing channels
You want to capture offline channel performance
Incrementality
Incrementality can be thought of as a calibration tool — a sanity check. It provides a more controlled environment in which you can test and control for different variables, like spend, that may affect how your campaigns or channels are performing.
Here’s an example: let’s say that you run a subway ad and receive 5,000 new users that week. But what if 50% of those users would have signed up anyway, regardless of whether or not they saw the ad?
Incrementality can help you answer the question of organic cannibalization by better controlling for variables and measuring the actual impact of each activity on your core metrics.
While attribution matches two events, like a touch and a conversion, incrementality quantifies the relationship: what is that relationship actually worth?
But measuring incrementality can be time- and resource-intensive to do consistently. It involves a test group and a control group, and a variable you want to test for, like geography. When you compare the performance of both groups, you’ll be able to quantify the incremental lift provided by each campaign.
Because they capture a moment in time, incrementality tests get outdated quite quickly. The tests you ran in January will yield results in February or March, and by then, they may already be outdated.
And because incrementality involves a test group, you will have to turn off marketing for a subset of your audience. This could result in lower sales and marketing performance — maybe that’s worth it to gain a deeper level of understanding, but maybe it isn’t. Keep in mind that a channel has to be large enough to begin with to garner statistically significant results, so this decrease in marketing will not be insignificant.
When running an incrementality test, you also need to be hands off and let things run for weeks or months at a time. That means very little room for changes, tweaks, and adapting on the fly if you want to get clear and accurate results.
It’s important to note that MTA and MMM, when used together, can provide a similar or much higher level of detail than incrementality without the hands-off requirement. MTA can show you all the different touches that happened on the way to a conversion, and MMM can add a layer of strategy and insight about where you should funnel budget to see incremental returns.
Bonus: incrementality results can be incorporated into your MMM model; Northbeam offers this service to train your MMM on your specific and unique data set.
You should use incrementality if:
You’re worried that your paid channels are cannibalizing your organic growth
You want to improve your MMM tool by feeding it incrementality test data
Your marketing team is not in a time-sensitive or resource-sensitive phase of growth
What do we suggest?
The best marketing strategies create room for all three of the tools above at different stages of a company’s growth and maturity. The data that each tool provides is different in key ways, and they each have their own distinct pros and cons, so you can best assess which makes the most sense for your current marketing priorities.
Ever felt like you’re throwing money at ads and just hoping something sticks?
Without a clear picture of return on ad spend (ROAS), it’s hard to tell what’s working, and what’s just burning cash.
ROAS, or Return on Ad Spend, is a simple but powerful metric that shows how much revenue you earn for every dollar you spend on advertising.
Whether you're running Meta campaigns or multi-channel growth programs, ROAS helps you evaluate ad efficiency and make smarter budgeting decisions.
In this guide, we’ll break down exactly what ROAS is, how to calculate it, what a “good” ROAS looks like, and how to improve it.
We’ll also explore common mistakes marketers make when tracking ROAS, and how to avoid them.
What is ROAS, and how can you calculate it?
ROAS measures the revenue generated for every dollar spent.
It’s a straightforward metric that helps marketers understand whether their campaigns are profitable and which channels deliver the best results.
The ROAS formula is calculated simply as Revenue divided by Ad Spend:
ROAS = Revenue / Ad Spend
For example, if your Revenue is $10,000 and your Ad Spend is $2,500, your ROAS would be:
ROAS = $10,000 / $2,500 = 4.0.
In this example, your advertising ROAS is $4 in revenue for every $1 spent.
While calculating the ROAS formula is simple enough, it’s critical to ensure that you’re using the right inputs. Make sure to accurately determine your revenue during a specific period. This could include direct purchases, sign ups, or other types of conversions.
Then, make sure you’re accurately calculating ad spend. Add up all the costs associated with a given campaign, including media buys, creative production, and management fees.
Why does ROAS matter?
ROAS is one of the clearest indicators of whether your marketing dollars are actually delivering results.
Here’s why it matters:
Profitability check: ROAS shows you whether your ad spend is generating more revenue than it costs. If you’re spending $2,500 and making $10,000, that’s a 4x ROAS — and a strong sign of profitability.
Budget optimization: By comparing ROAS across different campaigns or channels, you can identify where to allocate more budget for maximum returns.
Strategic decision-making: A high ROAS indicates profitable campaigns, while a low ROAS signals the need for optimization or a re-evaluation of your strategy.
ROI proof: Tracking ROAS consistently helps you refine your approach, bring data to the conversation, and improve your overall advertising ROAS.
How and when is ROAS used?
In modern marketing, ROAS is more than a performance metric: it’s a decision-making tool.
Marketers use ROAS to evaluate campaign efficiency, shift spend to high-performing channels, and justify budget increases (or cuts) with real numbers.
Marketers use ROAS for:
Channel planning: Comparing ROAS across platforms helps teams assess which channels are truly worth the investment. Paid search might drive conversions efficiently, while paid social delivers awareness but lower ROAS.
In-flight optimization: Running Meta, Google, or TikTok ads? Marketers often monitor ROAS daily or weekly to reallocate spend in real time, turning down underperforming campaigns and doubling down on the winners.
Post-campaign analysis: After a campaign ends, ROAS is a quick way to assess whether it met revenue expectations — and whether it’s worth repeating.
What is a good ROAS benchmark?
ROAS isn’t one-size-fits-all.
What qualifies as a good ROAS benchmark depends on your industry, business model, channel, margins, and sales cycle.
Here’s how different industries approach good ROAS benchmarks — and why their acceptable ranges vary.
Understanding where your business fits on the ROAS benchmark spectrum helps you set smarter performance targets and avoid apples-to-oranges comparisons.
Whether you’re aiming for a 4x return or making strategic bets on long-term value, ROAS is most powerful when used in context.
How can you best apply ROAS?
Knowing your return on ad spend is only half the battle: the real power comes from using it to guide your ROAS marketing strategy.
Track the Full Picture
To apply ROAS effectively, you need more than just ad spend and top-line revenue. True ROAS depends on accurate tracking of:
All associated costs: including creative production, agency fees, platform commissions, and even fulfillment if relevant.
Attribution data: so you know which clicks, impressions, or channels actually contributed to a sale.
Without clean attribution and comprehensive cost tracking, your ROAS may look better (or worse) than it really is, leading to poor decisions.
Automate with the Right Tools
Manual ROAS tracking is time-consuming and prone to error, especially if you’re running campaigns across multiple platforms like Meta, Google, TikTok, and email.
That’s where tools like Northbeam come in. Northbeam automates ROAS calculation using first-party data, cross-channel attribution, and full-funnel insights — helping you see exactly which ads are driving real revenue, not just clicks.
With better visibility, you can:
Identify your most efficient campaigns
Pinpoint low-performing spend
Reinvest confidently for higher returns
What are some common ROAS mistakes and misconceptions?
ROAS is a powerful metric, but it’s only as accurate as the data behind it — and it doesn’t tell the whole story on its own.
To make smart, high-confidence decisions, avoid these common ROAS pitfalls:
Ignoring multitouch attribution
Many marketers rely on a single-touch attribution model, which gives full credit to the last ad a customer interacted with before converting.
But in reality, most customer journeys are multitouch, spanning multiple platforms and touchpoints.
If you ignore that complexity, you risk undervaluing top-of-funnel campaigns or key supporting channels.
Instead, advanced attribution models — like linear, time decay, or data-driven — offer a more holistic view.
Platforms like Northbeam make it easier to apply these models and accurately track ROAS across the full customer journey.
Over-focusing on ROAS
ROAS can show you whether an ad is generating revenue, but it doesn’t reveal whether those customers will stick around.
A campaign with a high ROAS might bring in low-quality, one-time buyers, while a campaign with lower immediate returns could be acquiring loyal, high-LTV customers.
That’s why it’s important to balance ROAS with other metrics like Customer Lifetime Value (CLV) and Customer Acquisition Cost (CAC) to get a more complete picture of marketing effectiveness.
Overlooking hidden costs
If you’re only factoring in platform spend and ignoring expenses like agency fees, creative production, influencer partnerships, or software tools, your ROAS calculations may be overly optimistic.
To get a true measure of profitability, make sure you’re including all relevant costs in your formula — not just what’s visible in your ad dashboard.
Confusing ROAS vs ROI
While they’re both return metrics, ROAS only measures the revenue generated per advertising dollar spent.
ROI, on the other hand, takes into account all costs, not just ad spend, and calculates net profit.
A campaign can have a high ROAS and still lose money from an ROI perspective once fixed and variable costs are factored in.
A clear, accurate understanding of ROAS, and an understanding of the difference between ROAS vs ROI, is essential for evaluating true business impact and long-term growth potential.
How can you improve ROAS?
Once you’re tracking ROAS accurately, the next step is to improve it.
Whether you're optimizing for efficiency or scaling what already works, here are five proven strategies that can help lift your return on ad spend.
1. Refine your targeting
Use detailed audience segmentation and lookalike modeling to reach people who are more likely to convert, not just click.
Smart targeting ensures your budget is going toward the audiences with the highest potential ROI.
2. Test your creative
A/B testing headlines, visuals, calls to action, and formats across platforms can lead to meaningful gains in conversion rates and ROAS.
Even small improvements in engagement or relevance can have a big impact when scaled.
3. Monitor channel performance
Continuously compare ROAS across channels and campaigns to identify what’s working.
Shift budget toward high-performing channels and pause or restructure underperformers to prevent wasted spend.
4. Focus on post-click
If your landing pages aren’t converting, even the best campaigns won’t deliver advertising ROAS.
Streamline your pages for speed, clarity, and ease of conversion — especially on mobile, where friction can quickly kill ROAS.
5. Track performance consistently
The best way to improve ROAS is to measure it often, and act on the insights.
With platforms like Northbeam, you can monitor ROAS in real time, identify patterns across the funnel, and make data-backed decisions with confidence.
MyHD DJ Store faced difficulty identifying which marketing channels delivered the best returns, leading to inefficiencies in ad spend.
Northbeam's platform provided MyHD with accurate, actionable insights by consolidating first-party data and offering advanced attribution models.
MyHD achieved an 84% improvement in blended ROAS, a 21% reduction in customer acquisition costs, and optimized marketing efficiency.
Make ROAS work for you
ROAS, or Return on Ad Spend, is one of the most critical metrics for understanding whether your marketing is truly working.
It helps you measure campaign efficiency, optimize budget allocation, and tie revenue back to ad spend.
By calculating ROAS accurately, comparing it to industry benchmarks, and applying smart optimization tactics, you can make more confident, data-driven decisions.
But tracking ROAS manually, especially across multiple channels and touchpoints, can be time-consuming and error-prone.
Explore how Northbeam provides real-time ROAS tracking, cross-channel attribution, and actionable insights that help marketers scale what works, and cut out what doesn’t.
In this era of big data, the amount of tools that can help you surface marketing insights is nearly limitless. But not all insights are created equally, and there is a major gap when it comes to execution, a wide valley between having access to large amounts of data and being able to use it.
Part of this gap has to do with the limitations of a given data source: every platform and tool has its own way of gathering and visualizing data, so you have to be discerning when you decide which tool to go with. There's also the inherent difficulty of fully understanding a buyer’s journey — what influences what and how cause and effect can be attributed — combined with growing limits on ad platform data.
Not all marketing intelligence software navigates the gap between data and execution successfully.
Let’s cover the ways that the best marketers gather data and talk about what makes Northbeam data different.
Platform Data
Nearly all ecommerce platforms — X, Facebook, Youtube, Google, TikTok, etc. — have their own in-platform analytics suites. These suites are easy to read and access but are often catered towards individual users, not billion-dollar enterprises, and can therefore lack the sophistication needed to inform spending decisions.
If you spend a lot of your time on a given platform, native analytics suites can feel convenient, but they’re only going to give you part of the picture. What if someone saw one of your YouTube ads and later Googled your company and made a purchase? What if someone saw your product on a Pinterest board on their computer and later looked up your company’s Instagram on their phone?
This sounds obvious, but it’s not. Platform-specific analytics suites are handy and straightforward, but fail to reflect the complicated nature of the buyer’s journey. They’re also susceptible to inherent biases: Google doesn’t benefit from telling you that LinkedIn or Twitter are performing well; they want to keep your money on-platform.
For these reasons and others, great marketers know that platform data can only ever be a piece of a greater puzzle — not an end within itself.
Third Party Pixels
Third party pixels or cookies are bite-sized (pun intended) pieces of code embedded in a site to track user behavior. They provide more data than native platform analytics, and are able to give you information like someone’s email address, location, and more. Because they provide personal consumer information, there is growing vigilance surrounding their use, and consumers are more and more wary of allowing cookies as they navigate online.
While third party pixels are powerful, they can’t be consistently relied on, and their usage will be increasingly monitored and restricted over time.
Mozilla, for example, has accessible offerings that block all third party pixels by default, as does Google Chrome. Another limitation is that third party pixels only work on desktop web — not apps, and not mobile.
Third party pixels or cookies are easy to set up and gather data with, but limited in the actual data that they can gather. Like platform analytics, they are part of a greater marketing intelligence picture. Cookies are fine, but only in moderation (pun intended).
Read more about how cookie depreciation will affect marketing analytics on our blog. Spoiler: it won’t affect Northbeam’s data.
UTM Tracking
UTMs (urchin tracking modules) are custom URLs that transmit source information easily and for free. All you have to do is create a distinct URL for different ads and content to start gathering data about how much of your traffic is coming from different sources.
While UTMs are free to create and implement, the data they provide is limited. You can know which campaigns are sending traffic your way, but you are left with little understanding of the full buyer’s journey. UTMs are only available on browsers and can’t track offline conversions.
They are also easy to spot in a navigation bar, and, to be frank, unattractive. This may deter click rate, and savvy consumers may purposely avoid or even amend UTM links. UTMs also require regular, manual creation and maintenance, which can be time-intensive.
UTMs are another piece of the marketing intelligence puzzle. What they lack, like the other data sources above, is a holistic understanding that the buyer’s journey is multi-touch, multi-channel, and more complex than a single click and conversion — at least for most types of purchases. That’s where marketing intelligence platforms come in.
Marketing Intelligence
Today there are more channels than ever before. Pre-digital modes like print, radio, TV, mail, outdoor, and experiential marketing are still around, while new digital contenders like TikTok and YouTube and dozens of others have gained and maintained prominence in today’s marketing landscape.
More channels equals more data. We’re talking about near-infinite amounts of data points that need to come together to help you understand what’s working, what’s not, and what you can do better to improve your marketing strategy. Northbeam alone processes over 300 terabytes of data in a single day — that’s billions of data points — through its machine learning models.
Marketing intelligence platforms aggregate data across channels and visualize it in a way that is not just intuitive but actionable. They help close the gap between insights and execution, and bring disparate parts of your marketing mix together to form a bigger picture that tells a story. These platforms acknowledge the wider buyer’s journey and try to piece the puzzle together for you — saving you hours and eliminating human error.
That being said, they are not immune from error. Each marketing intelligence platform has its own unique algorithms and formulas for understanding and representing attribution to different channels. With infinite data points at play, even minor differences in accuracy between providers can result in millions of dollars lost or gained.
And many marketing intelligence platforms draw data directly from platforms themselves, leaving them open to volatility if these platforms have service issues, interruptions, or changes in their own algorithms. Data could be there today and gone tomorrow, leaving some marketing intelligence providers scrambling to keep up.
That’s why it's crucially important that you understand how each marketing intelligence software works so you can decide what makes the most sense for you and your company.
What makes Northbeam's data different?
Northbeam uses proprietary machine learning models to generate first-party multi-touch attribution data. This data is more accurate, more actionable, and more insightful than any amalgamation of in-platform or third-party data you could get as a marketer. With this data, you can run experiments faster, attribute ad results more accurately, and compete more efficiently in an expensive performance marketing environment.
This isn't just all your ad platform data collected in one place: it's new data, polished with machine learning. Northbeam’s in-house PhDs built its proprietary machine learning model in 2019 and have been fine-tuning and improving it ever since, training it on massive amounts of data every day to improve its performance over time.
While some marketing intelligence platforms succeed in providing actionable insights and making connections between touchpoints, this data is often retroactive — a week or even a month old. Making decisions based on yesterday’s data is fine, but it’s not great, and today’s marketer wants to be great. This is where Northbeam stands out.
With first-party data, you can attribute conversion to ads in near-real-time. Northbeam’s machine learning models allow it to not just present but predict how various customer touch points will play out in the future, letting you run simulations and automatically shift spending in informed ways in order to succeed.
It's also future-proof: Northbeam doesn’t rely on third party pixels, protecting you from volatility, bugs, and the ever-changing compliance landscape. A new iOS update or algorithm change doesn’t affect your data, and neither would the deprecation of cookies altogether.
Unlike other marketing intelligence solutions, Northbeam’s machine learning models are built with privacy and security in mind, keeping data anonymized and aggregated so you can be GDPR compliant today instead of worrying about it in the future.
The best part is that Northbeam’s machine learning model gets better over time. As it learns your unique data and channel performance, its predictions become more fine-tuned, delivering more value and accuracy with use.
“You can get the most up-to-date insights based on what you’re trying to understand, whether it’s revenue, spend, or attribution. We get you the depth of the information you’re looking for as quickly as possible,” said Steven Yampolsky, Head of Data Engineering at Northbeam.
“We go to a great depth to ensure that our data is as accurate as it can be. There’s a high push for accuracy to make sure we’re not biased and we’re using all the information we have to the best fidelity possible. That’s what makes Northbeam’s data different.”
But you don’t have to be a machine learning expert like Steven to use Northbeam. Our software is built with marketers in mind, with easy-to-use and customizable dashboards to get you the right data at the right time so you can turn insight into action.
Questions to Ask When You're Shopping for Marketing Intelligence Software:
As you’re choosing the marketing analytics platform that works best for you and your organization, ask the following questions so you can ensure you’re getting the best bang for your buck:
How real-time is your platform’s data? How often is it updated? Can you forecast?
How robust is your platform’s algorithm for calculating attribution and touches?
How accurate is your attribution model? What is your margin of error?
How resilient is your data when it comes to social media platform bugs and issues?
How future-proof is your data when it comes to changes in the compliance and privacy landscape?
How secure is your data when it comes to encryption and compliance regulations?
You’ve done your research, compared your options, and made the decision to switch to Northbeam — congratulations! In our reasonably biased opinion, you’re on the right path.
But, onboarding onto a new platform can take a minute. Let’s talk through what you can expect when switching to Northbeam — there may be some surprises, but only the good kind.
The TL;DR is this: Northbeam uses its own proprietary machine learning models to generate first-party attribution data that is more accurate than traditional platform or third party data.
If you’re used to getting data directly from platforms, third party pixels, UTM tracking, or even other marketing intelligence platforms that have their own algorithms, your data will look a little different when you migrate to Northbeam.
But we’re scrupulous about our data and the AI behind it — if you’re curious, you can read more about how our AI works.
Another major benefit of switching to Northbeam: because we don’t rely on third party pixels, your data is future-proofed. Algorithm or operating system updates such as the changes we saw Apple make to iOS within the last few years won’t affect the accuracy of our data, and neither will the deprecation of cookies.
Our model gets better over time as it learns your various channels and data points, so you can also expect accuracy and insight improvements as the tool learns you (and you learn the tool).
Ad Performance
Your ad performance may look different in Northbeam — in fact, it probably should. Ad performance differences are expected as you move to a more accurate platform. These differences come from Northbeam’s attribution model, attribution window, and how we conceptualize the customer journey.
Northbeam’s attribution model and attribution window are different from legacy models that platforms like Meta, Google, Pinterest, and others use. This creates a difference in how revenue is allocated to different channels, and will result in your performance looking different across campaigns.
Whereas individual platforms lean towards taking credit for conversions, Northbeam takes into account the entire customer journey to look at every touch that resulted in a conversion, not just the last or first touch.
Northbeam’s multi-touch attribution (MTA) model divides credit between touchpoints to give you a holistic picture of what is really driving sales. You can read more about our MTA models here.
Differences in Total Revenue
When comparing Northbeam’s Total Revenue and Orders to your internal source of truth (like a Shopify Dashboard) it’s very common to see differences in the data. Each platform has a slightly different revenue calculation.
Want to make sure that shipping, taxes, and/or refunds are accounted for in your total revenue? Our team can make adjustments so you see the data you need to see to make informed decisions.
Some technical tips:
Check to make sure you are in the right accounting mode (more below)
Check to make sure your filters are set correctly based on what you want to include and exclude in your total revenue calculation
Check to make sure Northbeam is set to the same timezone as your internal order table
Accounting Modes
Northbeam has two accounting modes for looking at your data: Cash Snapshot and Accrual Performance. Each accounting mode has a different way of allocating revenue and transactional credit, so they’re important to understand when you switch to Northbeam.
In the Cash Snapshot mode, revenue and transactional credit are given when the transaction occurs, or when an order is placed. This is useful for understanding the money that comes in on any given day — aka, cash flow.
Note that if you’re in Cash Snapshot mode, your return on ad spend (ROAS) metric will be shown as marketing efficiency ratio (MER). This is a semantic difference and the two metrics can be used similarly. MER can be most accurately described as a blended ROAS.
In the Accrual Performance mode, revenue and transactional credit are given when the contributory marketing touchpoints occur. Contributory marketing touchpoints include any interaction that results in a website visit, such as clicks from an ad, direct visits, or clicks from an influencer link.
Accrual Performance is mainly used to understand the direct return of your marketing dollars and the full impact of marketing channels on your business. It is more popular among Northbeam users because it gives credit to each touch and lets you get an accurate assessment of ad performance.
TL;DR: Use Cash Snapshot mode to understand on which days revenue was created, and use Accrual Performance to understand which touchpoints contributed to that final revenue.
What are all these new metrics?
If you’re coming from in-platform analytics, there may be some metrics on Northbeam that you’ve never seen before. While you don’t have to take full advantage of each and every data point that Northbeam serves up, it’s useful to have a cursory understanding of the different numbers you have at your disposal.
Here are some of the big ones:
% of New Visits
This metric shows you the percentage of visits to your site that come from new versus returning customers. You may be able to see this number on other platforms, but Northbeam gives you some added functionality.
Whereas you may be able to see % of New Visits on a UTM basis or broadly across your website with other tools, Northbeam lets you see it for each campaign or even each unique ad. You can also use Northbeam to see your revenue per new visit at different levels of granularity: revenue per new visit per channel, revenue per new visit per campaign, etcetera. There are loads of ways to serve up this data in a way that helps you make decisions.
New Customer %
What percentage of orders come from new versus recurring customers? This is a great metric for understanding how effectively different channels are driving new customers.
Returning customers are cheaper and easier to acquire, so platforms are often incentivized to include them in campaigns in order to skew conversions upwards. Even if you target a campaign entirely at new customer acquisition, there will typically be some leakage. Some channels do a much better job of targeting new users than others, and traditional ad platforms don’t show you this data.
New Customer % is a way to look under the hood and find out which channel campaigns are actually serving your acquisition goals — and which ones are relying on returning customers to drive performance.
First Time Customer Return on Ad Spend (ROAS)
How well are your ads bringing in new customers? First Time Customer ROAS removes returning customers from your ROAS calculation so you can see how your ads are performing in terms of new customer acquisition only.
First Time Customer ROAS also adds useful color to New Customer %. It takes that New Customer % and puts dollars and cents behind it so you can make informed spend decisions based on real and targeted ROI.
Lifetime Value (LTV) ROAS
While many platforms offer ad attribution windows from 1-day to 7-days, they cannot show you the fully realized value of your ads on a longer attribution window without heavy modeling.
How is the ad you launched two weeks ago doing? Thirty days ago? Three months ago? An LTV attribution window gives you complete information on the full impact of your ads, and Northbeam can even bridge the gap if a given user changes devices or phone numbers during that period.
LTV ROAS gives ROAS an indefinite attribution window to see the effects of your campaign and how long those effects last; it shows you the lifetime value of your marketing campaigns and efforts without time constraints.
Return on Ad Spend (ROAS) Lift
ROAS Lift takes your LTV ROAS and divides it by your ROAS. But what does this mean?
Let’s say your 1-day ROAS for an ad is 3x, meaning that you generated three times what you spent on ads. If you were to broaden that attribution window to look at the entire lifetime of an ad (LTV), perhaps that ROAS goes up to 4.5x as it gains more conversions over time.
ROAS Lift would then be calculated as 4.5 / 3 = 1.5x. Your ROAS “lifts” by 1.5x when you look at it from an lifetime attribution perspective.
ROAS Lift shows any efficacy that might be missed on top-of-funnel channels like TikTok when looking at 1-day or shorter term ROAS. ROAS Lift can help you validate your brand plays by showing how ROAS improves over time beyond initial measurements — and help you make tactical adjustments if your upper funnel media isn’t seeing a growing impact over time.
Ecommerce Conversion Rate (ECR)
Everyone should be familiar with the concept of a conversion rate, but Northbeam’s ECR metric is a little bit different. Because we can hook up to your Shopify and add a layer of MTA, conversion rate can be broken down by channel, by ad and campaign, and even by new versus returning customers.
Is your conversion rate good because it’s all repeat customers? Is it lower than expected but still acceptable given its high percentage of new customers?
Like the other metrics on this list, ECR unlocks critical context that marketing decision makers aren’t used to having at their disposal with other tools.
In the dynamic world of digital marketing, understanding customer behavior online is paramount. Pixel tracking is a crucial technology for sophisticated marketers that enables them to unlock detailed insights about users and measure their campaign performance with ease.
How do pixels work?
Pixels, or “tracking pixels,” are tiny, often-invisible images or pieces of code embedded on a webpage or email. When a user visits a website or opens an email, the pixel sends a signal back to the server. This signal, or “ping,” carries data about the user’s interaction, such as the time of their visit, the pages they viewed, and any specific actions they took.
How are pixels different from cookies?
There are a lot of ways to glean user information. While tracking pixels, first party cookies, and third party cookies all serve similar purposes, they do so in different ways. Understanding these differences is crucial for marketers who want to leverage the right tools in the right way to maintain compliance and gather the data they need.
Use this table below to differentiate between tracking pixels, first party cookies, and third party cookies.
Why use tracking pixels?
Pixel tracking offers several vital benefits to marketers, including but not limited to:
Performance measurement - by tracking user actions, like email opens, clicks, and time-on-page, marketers can measure the effectiveness of their campaigns with high precision.
Audience segmentation - data from tracking pixels can help create detailed user profiles and segment a given audience based on behavior. This enables more personalized and targeted marketing efforts
Pixels also have various use cases when it comes to digital marketing. Some of the big ones are:
Website analytics - use pixels to understand how users navigate a website, which pages they visit, and how much time they spend on each page
Ad campaigns - use pixels to measure the performance of display ads, social media ads, and other paid campaigns to optimize spend and improve ROI
Email marketing - use pixels to track open rates, click-through rates, and user engagement with your email content
E-commerce - user pixels to monitor how users interact with your products and website to refine your sales strategies. Pixels can surface browsing patterns, add-to-cart actions, and other significant clicks
What is the Northbeam Pixel?
Northbeam takes advantage of pixels and pixel tracking to generate actionable insights and near-real-time information about user behavior.
Like pixels in general, the Northbeam Pixel is a snippet of code that allows Northbeam to collect important behavioral information about your website visitors. This information then feeds into Northbeam’s backend device graphic, allowing us to track customer journeys from site visit to purchase, along with all the other marketing touchpoints in between.
As digital marketing technologies continue to evolve, pixel tracking is expected to become even more sophisticated. Technologies like artificial intelligence and machine learning will enhance our ability to predict user behavior and personalize marketing efforts in unprecedented ways. It’s key to stay ahead of privacy concerns and regulations to understand how they will affect pixel data.
With its advanced features and ease-of-use, the Northbeam Pixel represents the next generation of tracking technologies, offering enhanced accuracy, comprehensive data collection, and near-real-time reporting so marketers can optimize their strategies and drive better results.
A customer might first discover your brand through a social ad, read a blog post days later, click a retargeting email, and finally convert after a branded search.
If you rely on single-touch attribution, giving 100% of the credit to either the first or last click, you miss the bigger picture.
That’s where Multi-Touch Attribution (MTA) comes in.
By distributing credit across every touchpoint, MTA reveals the true value of each channel in driving conversions.
Instead of guessing which interactions matter most, you gain a holistic, data-driven view of the entire funnel.
In this guide, we’ll break down what multi-touch attribution is, why MTA is critical in modern marketing, the different models you can use, and how to implement them effectively.
You’ll also see how advanced attribution platforms like Northbeam help solve the challenges that often limit other solutions.
What Is Multi-Touch Attribution?
Multi-Touch Attribution (MTA) is a marketing measurement method that assigns credit for a conversion across every touchpoint a customer interacts with along their journey. rather than giving 100% of the credit to the first or last click.
Wondering how multi-touch attribution works? At its core, MTA is designed to answer a simple but critical question: Which marketing channels and interactions actually contribute to a sale?
Instead of oversimplifying performance by rewarding only one step, MTA distributes credit proportionally across multiple touchpoints, such as an initial social ad, an email reminder, a retargeting click, and a branded search, to give marketers a more accurate view of what’s driving results.
The core purpose of MTA is to uncover the true influence of each channel in moving prospects down the funnel.
This helps teams optimize spend, refine strategy, and understand how upper-funnel activities (like awareness campaigns) work together with lower-funnel drivers (like paid search or direct visits) to generate conversions.
Why MTA Matters for Marketing
Today’s buyer journeys rarely follow a straight line.
A customer might discover your brand through a TikTok ad, research products via a blog post, sign up for a newsletter, and only weeks later click a retargeting ad before finally purchasing.
If you’re using a single-touch model, all of that complexity gets reduced to one moment: either the very first click or the very last.
The shortcomings of single-touch models include:
Skewed insights: Last-click attribution tends to overvalue lower-funnel channels like search or retargeting, while first-click attribution may inflate the role of early awareness channels.
Misallocated budgets: If only one touchpoint receives full credit, marketers risk over-investing in the wrong channels and undervaluing others that actually play a critical supporting role.
Incomplete customer understanding: Single-touch models can’t show how channels interact across the funnel, making it harder to identify which strategies truly drive momentum toward conversion.
The benefits of multi-touch attribution models are:
Holistic performance measurement: By distributing credit across multiple touchpoints, MTA captures the real contribution of each channel in the path to purchase.
Smarter resource allocation: With a clearer sense of which campaigns build awareness, nurture interest, and close deals, marketers can confidently adjust budgets to maximize ROI.
Stronger customer journeys: MTA highlights the interplay between channels (e.g., how paid social might spark interest that email later converts) so teams can design more effective cross-channel strategies.
Here’s how first-touch, last-touch, and multi-touch attribution models would show credit differently for the same sale:
Consider the following customer journey:
A customer first discovers your brand through a Google Search ad
They browse your products on Amazon but don’t buy anything
They see a TikTok ad that reminds them of your product later
They open a discount email and click into your website
They make a direct purchase on your website
Here’s how different attribution models would assign credit for the same purchase:
In short, MTA matters because it acknowledges marketing for what it really is: a multi-step, multi-channel collaboration.
Types of Multi-Touch Attribution Models
Not all multi-touch attribution (MTA) models are created equal. Each framework takes a different approach to distributing credit across touchpoints.
Choosing the right one depends on your goals, data maturity, and the complexity of your customer journeys.
Here are the most common MTA models, with their advantages, drawbacks, and ideal scenarios:
Linear Attribution
Definition: Credit is divided equally across every touchpoint in the customer journey.
Pros: Simple to understand and implement; ensures every channel gets recognition.
Cons: Over-simplifies performance by treating all interactions as equally influential.
Best For: Brands just starting with MTA, or when you want a “baseline” view of how channels contribute together.
Time Decay Attribution
Definition: Touchpoints closer to the conversion get more credit, while earlier interactions receive less.
Pros: Reflects the reality that later-stage actions (like a retargeting click) often have a stronger influence on conversion.
Best For: Shorter sales cycles or campaigns where recency is a reliable indicator of influence.
U-Shaped Attribution (Position-Based)
Definition: A large portion of credit goes to the first and last touchpoints, with the remainder split among the middle touches.
Pros: Balances the importance of both awareness (first click) and conversion (last click).
Cons: Middle touches get minimal recognition, even if they played a critical role in nurturing.
Best For: Journeys where discovery and conversion are most important milestones, but you still want to capture some mid-funnel activity.
W-Shaped Attribution
Definition: Similar to U-Shaped, but adds significant credit to a key mid-funnel milestone (such as a form fill or product page view), alongside the first and last touches.
Pros: Recognizes three critical stages: awareness, lead creation, and conversion.
Cons: Still assumes a fixed distribution that may not reflect reality for every business.
Best For: B2B or high-consideration purchases with clear mid-funnel handoffs, such as lead qualification or demo requests.
Algorithmic / Data-Driven Attribution
Definition: Uses machine learning to dynamically assign credit based on the actual contribution of each touchpoint, leveraging historical and real-time data.
Pros: Most accurate and customizable; adapts as customer behavior changes.
Cons: Requires significant data volume and advanced technology; harder to explain to stakeholders.
Best For: Mature marketing teams with robust data pipelines, complex journeys, and the need for precision.
In practice, no single model is universally “best.”
Many brands start with simpler rules-based models (like Linear or U-Shaped) and evolve toward data-driven approaches as their data capabilities grow.
Single-Touch vs. MTA vs. MMM
Marketers often treat attribution as an “either/or” decision, but in reality, different methods serve different purposes.
Single-Touch Attribution
What it does: Assigns all credit to the first or last touchpoint.
Why it’s valuable: Simple to implement, easy to explain to stakeholders, and can provide a quick snapshot of which channels most often open or close conversions.
Multi-Touch Attribution (MTA)
What it does: Distributes credit across multiple touchpoints to reveal the interplay between channels.
Why it’s valuable: Offers granular, customer-level insights into what actually drives conversions.
Media Mix Modeling (MMM)
What it does: Uses aggregated, historical data to measure the overall contribution of each channel to sales.
Why it’s valuable: Provides a big-picture view of long-term channel effectiveness, including offline and walled-garden channels where user-level data isn’t available.
How Single-Touch, MTA, and MMM Work Together
MTA is best for tactical optimization: reallocating budget across campaigns and channels in near real time.
MMM is best for strategic planning: understanding how investments shape growth over months or quarters.
Single-touch can still be useful as a quick gut-check, but shouldn’t be the only lens you use.
In other words: MTA gives you the microscope, MMM gives you the telescope, and single-touch still has a place as a simple directional compass.
The strongest marketers use all three to see clearly at every level of decision-making.
MTA Challenges and Considerations
While Multi-Touch Attribution can provide unmatched clarity into customer journeys, it’s not without hurdles.
To get reliable insights, marketers need to be aware of the three most common challenges:
1. Data gaps due to privacy restrictions
Regulations like GDPR and CCPA, plus platform policies such as Apple’s App Tracking Transparency (ATT), limit the availability of user-level data.
This makes it harder to connect every touchpoint and can leave blind spots in attribution models.
2. Cross-device attribution issues
Customers rarely stay on a single device: they might click an ad on mobile, research on a laptop, and convert later on a tablet.
Without strong identity resolution, these multi-device journeys can look like separate users, distorting credit assignment.
3. Model selection pitfalls
No model is perfect. Choosing the wrong one for your business can either overcomplicate analysis or lead to misleading conclusions.
For example, relying on time-decay in a long B2B sales cycle may undervalue top-of-funnel touches that actually drive momentum.
The key takeaway: MTA delivers immense value, but its effectiveness depends on high-quality data, thoughtful model selection, and the right technology to close gaps.
How to Implement MTA Effectively
Getting Multi-Touch Attribution right isn’t just about picking a model, it’s about building the right foundation and using the right tools.
Here are the four core steps to make MTA successful:
1. Set clear goals
Define what you want to learn: Are you optimizing budget allocation? Testing new channels? Improving ROI visibility?
Specific objectives ensure your attribution framework delivers actionable insights.
2. Ensure data integrity
Clean, consistent, and complete data is the lifeblood of MTA.
Make sure you’re capturing touchpoints across all channels, including web, email, paid, organic, and offline interactions.
3. Choose the right platform
Attribution is too complex to manage manually. Advanced platforms like Northbeam bring automation, accuracy, and scale to the process.
Unlike generic analytics tools, Northbeam connects performance across the entire funnel, unifies cross-channel data, and adapts to the privacy-first future.
4. Validate and refine
No model is “set and forget.” Regularly compare attribution insights with business outcomes to ensure accuracy.
Iterate as your customer journey evolves; what works for one campaign or season may not apply to the next.
With clear objectives, clean data, and the right technology, MTA becomes more than a reporting tool: it becomes a growth engine.
Making Multi-Touch Attribution Work for You
In a world where customer journeys span dozens of channels and devices, accurate attribution is no longer optional, it’s essential.
Multi-Touch Attribution (MTA) provides the clarity that single-touch models lack, ensuring every stage of the funnel gets the recognition it deserves.
The right model depends on your goals:
Linear for simplicity
Time Decay for recency
U- and W-Shaped for milestone-driven journeys
Algorithmic approaches for precision at scale.
What matters most is choosing strategically and evolving as your marketing matures.
With a thoughtful approach, and advanced platforms like Northbeam to streamline the process, MTA becomes more than a measurement tool. It becomes the foundation for smarter decisions, stronger customer experiences, and more efficient growth.
In today’s privacy-first, multi-channel marketing landscape, the ability to confidently connect spend to results is more important, and more challenging, than ever.
Marketers are tasked with allocating budgets across a growing range of channels, from search and social to TV, out-of-home, and beyond, all while navigating shrinking access to user-level data.
Media Mix Modeling (MMM) has long been a go-to method for answering the big questions: Which channels are driving growth? How should we shift spend to maximize ROI?
But what is Media Mix Modeling in 2025? By analyzing historical performance data at an aggregated level, MMM provides a clear, unbiased view of how each piece of the marketing puzzle contributes to overall outcomes.
But the rules have changed. Traditional MMM offers clarity, yet modern challenges demand smarter, more adaptive models that can keep pace with shifting market dynamics and deliver actionable guidance in real time.
In this Media Mix Modeling guide, we’ll explore the fundamentals of MMM, why it remains a vital tool in the marketer’s measurement toolkit, and how next-generation, machine learning-powered MMM is reshaping the way brands plan, measure, and optimize their media mixes.
What Is Media Mix Modeling?
Media Mix Modeling (MMM) is a statistical approach used to estimate the impact of different marketing channels on overall sales or other business outcomes.
Instead of looking at individual customer actions, MMM analyzes historical data, such as advertising spend, sales performance, and external factors like seasonality or economic trends, to determine how each channel contributes to results.
Think of it as taking the big-picture view.
Whereas granular attribution methods (like Multi-Touch Attribution, or MTA) track individual user paths across channels, MMM works from the top down, using aggregated data to identify broad patterns and long-term channel effectiveness.
This makes it especially valuable when privacy regulations, walled gardens, or offline channels make user-level tracking difficult or impossible.
In recent years, the concept has evolved: next-generation MMM leverages machine learning and adaptive algorithms to move beyond static, quarterly models.
These modern approaches can account for non-linear relationships between spend and results, detect diminishing returns, and update recommendations as new data comes in.
By combining robust statistical foundations with real-time adaptability, next-gen Media Mix Modeling in 2025 guides marketers to make smarter, faster budget decisions across every channel in their mix.
MMM vs Other Attribution Models
While MMM methodology focuses on the big-picture impact of each marketing channel, Multi-Touch Attribution (MTA) digs into the granular details of individual customer journeys.
Both approaches aim to answer the same fundamental question — what’s driving results? — but they do so in very different ways.
MMM is ideal for capturing the value of channels where user-level tracking is limited or impossible, such as TV, radio, print, or even some walled-garden platforms.
It’s also better suited for long-term strategic planning, helping you forecast ROI and allocate budgets across the full marketing mix.
MTA, on the other hand, is most effective when you have access to detailed, user-level data from digital channels.
It allows you to see the specific touchpoints (ads, emails, clicks) a customer interacted with before converting, making it valuable for short-term campaign optimization.
Many sophisticated marketing teams use MMM and MTA together: MTA for tactical, in-flight adjustments and MMM for overarching strategy and cross-channel budget planning.
How Media Mix Modeling Works
At its core, Media Mix Modeling is about turning historical performance data into actionable insights.
The process generally follows these five steps:
1. Gather Historical Data
The MMM methodology begins by collecting aggregated spend and outcome data across all marketing channels, both online and offline.
This often includes:
Media spend by channel (e.g., TV, paid search, display, social, OOH)
Business outcomes (e.g., sales revenue, leads, signups)
External variables like seasonality, holidays, competitor activity, and economic indicators
2. Fit a Statistical or Machine Learning Model
Traditionally, MMM uses regression analysis to identify correlations between marketing investments and business results.
Modern MMM platforms also incorporate machine learning algorithms to capture more complex, non-linear relationships, such as diminishing returns when spend reaches saturation in a given channel.
3. Adjust for Seasonality and Externalities
The model accounts for predictable seasonal patterns (like holiday spikes) and one-off external events (like supply chain disruptions or viral trends) so these factors don’t get mistaken for marketing impact.
4. Analyze Interactions Between Channels
MMM also evaluates interaction effects, for example, how TV ads might boost the performance of paid search, or how brand campaigns influence conversion-focused channels.
5. Forecast and Recommend
Once calibrated, the model can simulate different spend scenarios, forecast the likely impact on business outcomes, and recommend budget reallocations to improve ROI.
In modern practice, next-gen MMM platforms repeat this process frequently, sometimes weekly, so marketers aren’t relying on outdated quarterly or annual models.
Why MMM Is Essential
In today’s rapidly shifting marketing landscape, Media Mix Modeling remains one of the most resilient measurement tools available to marketers.
While many attribution methods have been disrupted by privacy regulations, platform data restrictions, and the decline of third-party cookies, MMM continues to deliver reliable, actionable insights.
Below are four major reasons why MMM is essential to modern marketers:
MMM is Resilient in a Privacy-First Era
Because MMM relies on aggregated spend and outcome data (not user-level tracking) it’s largely unaffected by signal loss from cookie deprecation, iOS privacy updates, or walled garden policies.
This makes it a future-proof choice for marketers navigating growing data limitations.
MMM Provides Holistic, Cross-Channel Coverage
MMM looks at the entire marketing ecosystem, including channels that are notoriously hard to measure at the individual level.
Whether it’s digital platforms like paid search and social, or offline investments in TV, radio, print, and out-of-home (OOH), MMM provides a unified view of performance across the full mix.
MMM Enables Strategic Budgeting & Planning
Beyond measuring past performance, MMM helps marketing leaders forecast ROI, plan annual or quarterly budgets, and determine the optimal allocation of spend across channels.
By modeling different “what-if” scenarios, teams can make informed decisions that balance efficiency and growth.
MMM Provides Long-Term Impact Measurement
Unlike short-term attribution tools, the MMM methodology captures the lagged effects of marketing, such as a brand awareness campaign that boosts conversions weeks later.
This gives marketers a more accurate picture of a channel’s true value over time.
When executed well, MMM doesn’t just tell you what worked in the past — it gives you a roadmap for where to invest next.
The Next Evolution: Next-Gen MMM with Machine Learning
Traditional Media Mix Modeling has always been powerful, but it was built for a slower era of marketing, where quarterly or annual updates were enough to guide budget decisions.
In today’s fast-moving environment, those static models can quickly become outdated.
Modern MMM platforms are changing the game by integrating machine learning (ML) into the process. These next-generation models go beyond simple regression analysis to capture the complexity of modern marketing dynamics.
Key advantages of ML-driven Media Mix Modeling in 2025 include:
Continuous Adaptation: Instead of waiting months for a model refresh, ML-based MMM updates frequently (sometimes weekly) to incorporate the latest performance data. This ensures recommendations reflect the most current market conditions.
Non-Linear Interactions & Diminishing Returns: Machine learning can detect and model nuanced relationships between channels. For example, it can quantify how TV campaigns amplify search performance, or when increased spend in a channel starts yielding smaller incremental gains.
Near-Real-Time Forecasts & Automated Recommendations: Advanced MMM platforms can simulate multiple budget scenarios on demand, delivering rapid, data-backed guidance for reallocation. Some even automate the process by feeding recommendations directly into campaign management tools.
An example of this evolution is Northbeam’s MMM+, a machine learning-powered model that retrains weekly, accounts for non-linear effects, and delivers actionable budget rebalancing suggestions in near real time.
This blend of statistical rigor and adaptive intelligence allows marketers to shift from reactive measurement to proactive, always-on optimization.
In short: Smarter MMM benefits from the speed and sophistication of machine learning to give marketing leaders an edge, making it possible to adjust strategy as quickly as the market changes.
Implementing Smarter MMM: A Step-by-Step Guide
A successful Media Mix Modeling initiative requires both solid data foundations and a disciplined approach to analysis.
Here’s how to get started:
1. Assemble Rich Historical Data
Collect several months (ideally years) of aggregated data on:
Marketing spend by channel (digital and offline)
Business outcomes such as revenue, conversions, or leads
External variables like seasonality, holidays, competitor activity, and macroeconomic trends
2. Choose the Right Modeling Approach
Select the statistical framework that best fits your needs:
Linear regression for a straightforward, interpretable model
Bayesian modeling for incorporating prior knowledge and uncertainty estimates
Machine learning-based MMM for adaptive, complex relationship modeling
3. Validate and Iterate
Run diagnostics to ensure your model is robust.
Check for overfitting, assess predictive accuracy, and test different variable combinations. Refine your approach based on performance.
4. Translate Insights into Budgets and Forecasts
Use the model’s findings to guide budget allocation, forecast channel performance, and run “what-if” simulations.
Prioritize reallocations that deliver the highest incremental ROI.
5. Leverage Advanced Capabilities
Modern MMM platforms can generate real-time forecasts and provide automated budget system guidance, allowing you to make faster, data-backed decisions without waiting for quarterly reviews.
💡 Pro Tip: Revisit your model regularly. Newer ML-powered MMM solutions, like MMM+, retrain weekly for sharper accuracy and more relevant recommendations.
MMM Best Practices & Emerging Trends
Whether you’re implementing MMM for the first time or upgrading to a more advanced platform, these best practices will help you get the most MMM benefits from your investment and prepare you for what’s next.
Our top five MMM best practices are:
1. Prioritize Adaptability
Markets move fast. Choose a platform that supports frequent model updates so your insights reflect the most recent conditions.
Weekly or even rolling updates ensure your budget decisions are always based on fresh, relevant data.
2. Foster Cross-Functional Collaboration
MMM benefits are best seen when marketing, finance, and data operations are aligned.
Marketing teams bring campaign knowledge, finance ensures budgetary alignment, and data teams maintain the integrity of the underlying datasets.
This collaboration helps ensure the model’s recommendations are both actionable and strategically sound.
3. Explore Scenario Modeling
Smarter MMM platforms allow you to simulate different budget allocation strategies before you commit spend.
Scenario modeling lets you test potential changes, forecast results, and identify the mix that delivers the best ROI.
4. Leverage AI-Driven Dose Response Curves
Dose-response curves model how incremental spend in a given channel impacts performance, helping you pinpoint the “sweet spot” before diminishing returns kick in.
AI-powered curves make these insights more precise and adaptable to shifting conditions.
5. Keep an Eye on Server-Side MMM
As privacy regulations tighten, server-side MMM is emerging as a powerful alternative.
By processing data securely on the server, marketers can integrate more channels, maintain compliance, and future-proof their measurement framework.
The future of MMM is agile, collaborative, and deeply integrated with AI, enabling marketers to pivot faster and plan smarter than ever before.
MMM Benefits Start Now
For decades, Media Mix Modeling has been a cornerstone of marketing measurement, and it’s not going anywhere. What’s changing is how it’s done.
Advances in machine learning have transformed MMM from a static, quarterly exercise into an agile, always-on decision engine that adapts as fast as the market moves.
By combining MMM’s holistic, privacy-resilient view with the adaptability of next-gen models, marketing leaders can make sharper budget decisions, capture the full value of every channel, and confidently plan for the future.
Marketing return on investment (ROI) is a crucial metric to understand your marketing performance. This guide will cover what it is, how to calculate it, what to consider, and how to improve your ROI if it's not performing as you’d hoped.
Put simply, ROI measures the return generated from marketing activities relative to the amount of money spent. Calculating and understanding the ROI from all of your different marketing efforts can give you a great picture of how you’re performing — and help make sure you’re getting the best bang for your buck.
What is marketing ROI?
Marketing ROI is the profit or loss generated by different marketing activities. It is a critical component of understanding performance, and can help support profitability for your organization. It can enable you to make decisions about which campaigns to invest in and which to turn off, and support you as you make the case for budget for activities that really move the needle.
A high ROI indicates that a marketing campaign is generating a significant return compared to its cost, while a low or negative ROI suggests that it may be time to go back to the drawing board.
By calculating and analyzing your ROI, your team can more accurately allocate budget, measure performance, optimize strategy, and communicate its performance and needs with stakeholders.
How to calculate marketing ROI
You can calculate marketing ROI with a straightforward formula:
Marketing ROI = Profit from marketing / marketing costs
So if your LinkedIn ad campaign cost $1,000 and it generated $2,500 in revenue on your website, your ROI would be calculated as:
Marketing ROI = 2,500 / 1,000
Marketing ROI = 2.5
Some people prefer to express marketing ROI as a percentage. The formula would be the same except you would multiply the output by 100 and your marketing ROI would equal 250%.
A good marketing ROI varies depending on the industry, product, and type of campaign. You should work with your team to set a benchmark for ROI over time and try to improve it as you progress. For reference, a marketing ROI of 5:1 (meaning you earn $5 for every $1 you spend) is considered a solid rule of thumb.
What to consider when calculating ROI
It’s important to note that ROI changes depending on your time frame. Perhaps your LinkedIn ad generated $2,500 in its first day, but on a two-week timeframe it generated closer to $7,500. Your 14-day ROI would then be 7.5x your initial spend — that’s great!
ROI can also vary depending on how you define marketing costs. You should include direct spend in the denominator, but you can also include things like software and tool costs, personnel salaries, and more. This could be helpful if you are calculating ROI not on a campaign level but on a macro level, across all your campaigns for a given time period.
For example, If you wanted to know the ROI of everything you did last quarter or last year, you may decide that including salaries and tools will give you the most accurate picture.
Finally, attribution can be tricky with ROI. How do you know if someone clicked on your LinkedIn ad after they saw your Google ad first? How do you know which campaign to attribute marketing revenue to?
To complicate the picture, different platforms may take sole ownership of conversions when, in reality, a confluence of factors, impressions, and touches ultimately leads to a purchase. Tools like multi-touch attribution (MTA) can help with attribution questions like this.
When calculating your marketing ROI, it’s important to keep in mind timeframe, attribution issues, and any relevant marketing costs to get the clearest picture of performance.
How to improve your marketing ROI
If your marketing ROI isn’t where you want it to be, there are a number of interventions that can help you get back on track.
1. First, start by setting clear objectives. Your marketing ROI may be below the goal ratio of 5:1, but perhaps it is standard for your industry, product, or channel. Get a holistic picture of performance and set objectives based on your historicals — not numbers you read online. Use your current ROI to set a reasonable and attainable goal for the near future.
2. Consider using advanced analytics and marketing intelligence tools to track and analyze campaign performance so your ROI isn’t skewed by platform analytics or incomplete attribution data.
3. Focus on high-ROI channels — identify the channels that consistently deliver high ROI for your marketing efforts and allocate more budget to those channels.
4. Test and optimize your lower-performing channels to see if you can improve your ROI through small, consistent, and creative iterations.
5. Try to improve your customer targeting and refine your target audience. Effective customer targeting reduces wasted spend and increases the likelihood that leads will convert to customers as a result of your marketing efforts. Strong data and marketing intelligence platforms can help you refine your targeting.
6. Optimize your conversion paths. Leads are typically taken to a page when they click on your ads, so ensure that page is optimized for conversions. A seamless user experience and clear call-to-action (CTA) can significantly improve the success of your marketing campaigns — and your ROI.
How to think about ROI
Marketing ROI is a vital metric for any strong marketing team. It enables you to fully understand the impact of each marketing dollar and make informed decisions about spend. Use the definitions, formulas, considerations, and strategies for ROI improvement in this guide to help you get a firm grasp on ROI concepts so you can use them to your advantage and improve your overall marketing effectiveness.
In a rapidly-evolving marketing landscape, staying focused on the efficiency and success of your investments is key to maximizing returns and improving your performance.
If you haven’t heard of AppLovin yet, it’s time to crawl out from under that rock. Long known in the mobile gaming world, AppLovin is now breaking into direct-to-consumer (DTC) advertising — and early adopters are seeing impressive results.
With a massive user base, fresh ad formats, and a self-service platform in beta, AppLovin is quickly becoming one of the most exciting new channels in performance marketing. And it’s not just hype — top brands are reporting Meta-like results at a fraction of the cost.
So what exactly is AppLovin, and why is it working so well for ecommerce brands? Let’s break it down.
A new player in the DTC ad game
AppLovin is a mobile ad network originally built for monetizing mobile games and apps. Its network reaches 1.4 billion users — on par with platforms like TikTok — and has traditionally been used to promote other games. But that’s changing fast.
AppLovin is now opening its doors to ecommerce advertisers, offering a suite of performance-driven tools, ad placements, and a robust algorithm that seems tailor-made for DTC brands.
Why AppLovin works for ecommerce
The psychology is different — in a good way
Mobile gamers aren’t doomscrolling — they’re engaged, focused, and relaxed. When someone beats a high score or clears a level, they’re in a positive emotional state — and that’s when your ad shows up. You’re inserting your brand into a dopamine-rich context, not between two stressful news stories or a chaotic social feed.
This makes a huge difference. Ads feel less like interruptions and more like surprises. That novelty drives higher engagement — and better performance.
The reach is broad
Americans now play more mobile games than they watch cable TV. Over 200 million adults in the U.S. play mobile games weekly, and AppLovin’s network gives you access to all of them. That’s a massive opportunity for building frequency and brand awareness outside the saturated social ad ecosystem.
The ad formats are fresh, but familiar
AppLovin’s ad placements support vertical video and static creatives, and because the context is so different from social, you can often re-use top-performing Meta or TikTok assets without major changes. That’s rare in emerging channels — and it means lower creative lift and faster testing.
The tech is built for performance marketers
From the algorithm to the onboarding process, AppLovin feels like it was built for DTC. Their team has already put together an ecommerce wiki, and their roadmap is aggressively focused on delivering what performance marketers actually need: scale, efficiency, and data transparency.
How to add AppLovin to your marketing mix
AppLovin isn’t a replacement for your core channels — but it’s a powerful complement that can help you reach new audiences and diversify your acquisition strategy.
Here's how to get started:
Start With What You’ve Got
Repurpose your best vertical video or static creatives from Meta, TikTok, or YouTube Shorts. Lean into hook-driven content and test simple iterations to find what resonates.
Track Everything
AppLovin opens up a brand-new slice of time in the customer journey — users playing games. When you track AppLovin touchpoints through Northbeam, you’ll get a complete view of how these ads contribute to conversions across platforms. Whether someone sees your ad in a game and converts via branded search three days later, or after getting hit with a Meta retargeting ad, Northbeam helps you connect the dots.
Think Frequency + Novelty
Your goal isn’t just conversion — it’s attention. AppLovin gives you a way to layer on impressions in a high-engagement setting, helping your brand break through in a fragmented landscape.
The Bull Case for AppLovin
Performance marketers are always chasing the next edge — and, as of April 2025, AppLovin may be it. It combines:
High reach
Fresh context
Low creative lift
Affordable CPMs
Early-mover advantage
And when paired with a measurement platform like Northbeam, it becomes even more powerful. You get full visibility into how AppLovin is contributing to your performance across the funnel — so you can scale with confidence, not guesswork.
Northbeam has been called everything in the book: a marketing analytics platform, an MTA tool, an attribution platform, an ecommerce analytics platform, a data tool, and about a million other phrases. While none of these titles are untrue, we prefer the term “marketing intelligence” to capture our breadth of features and functionalities
As the marketing landscape shifts and grows, a new need arises for platforms that will synthesize multiple data points and sources together to derive not just information but intelligence: actionable insights. These platforms are catching on with sophisticated marketers that are tired of ever-expanding spreadsheets and platform analytics.
But what exactly is marketing intelligence?
Marketing intelligence defined
The phrase “marketing intelligence” refers to a robust and comprehensive system that gathers, processes, and analyzes all the data relevant to your marketing activities. These platforms then use that data to provide intelligence to help you make informed decisions and optimize your marketing strategy.
The key components of a marketing intelligence platform are as follows:
Data collection and integration
Analytics and reporting
Attribution modeling
Real-time monitoring
Customer insights
Optimization recommendations
Data collection and integration
Marketing intelligence platforms should ideally be able to integrate with all of your data sources so you can view information on all of your campaigns in one place. They aggregate data across social media, email campaigns, ads, website analytics, e-commerce platforms, and beyond to provide you with a unified picture of performance and a holistic view of your marketing activity.
Analytics and reporting
Marketing intelligence platforms provide tools to not just ingest this data but view it in a productive and actionable way. Proper analytics and reporting help you understand the effectiveness of different channels, campaigns, and strategies so you can adjust accordingly in real-time.
The most sophisticated platforms will also offer a layer of customizability so you can see the data you want to see how you want to see it, and create unique reports for different teams and teammates across your organization depending on their needs.
Attribution modeling
Marketing intelligence platforms should offer powerful attribution modeling beyond first- and last-touch. Tools like multi-touch attribution (MTA), media mix modeling (MMM), incrementality, and others contribute to the full picture of attribution so you can understand what is driving successful outcomes, and just as importantly, what isn’t.
Real-time monitoring
Customer preferences and behavior shift so quickly that it’s critical to get the data you need now, not next month. The best marketing intelligence platforms can process and display data on a near-real-time basis, making sure that you’re always up to speed on progress and performance so you can make the best decisions in the moment, when it matters.
Customer insights
Tailored customer insights help you understand the who, what, when, where, how, and why of your customer behavior. Detailed analytics can show you where your customers are coming from and every touch they make along their way to a purchase. They’ll let you break down performance by new and returning customers, and about a million other ways too, depending on what you want to know.
Optimization recommendations
Look for a marketing intelligence platform that doesn’t just ingest and surface data, but shows you exactly what to do with it. These platforms should have a seamless user experience and an intuitive way of surfacing insights that makes sure that the right action is the clearest path forward. Whether that’s with in-platform recommendations, an expert account manager at your side, or both, you should feel supported in making the most of your data for powerful decision-making. The most mature platforms will help you do that.
Learn more about marketing intelligence
While many platforms exist today that will help you do more with your marketing data, not all of them can be accurately classified as marketing intelligence platforms. While the category is new, the functionality is anything but: marketing intelligence aims to provide you with the best data so you can make the best decisions with ease.
In today’s competitive landscape, marketers need more than intuition to succeed. Tracking the right marketing metrics is crucial in order to make more informed decisions, optimize campaigns, and drive sustainable growth.
But with so many metrics available, it can be overwhelming to know where to focus.
This guide will break down the top six marketing metrics you should prioritize for long-term success — and how Northbeam can help you track and leverage these insights effectively.
Metric #1: Customer Lifetime Value (CLV)
Customer Lifetime Value (CLV) is the total revenue a business can expect from a single customer over the entire duration of their relationship.
CLV is critical for understanding the long-term profitability of your customers. By focusing on increasing CLV, businesses can improve customer retention, enhance customer experience, and allocate resources more effectively.
To calculate CLV, you’ll need to account for the average purchase value, purchase frequency, and customer lifespan. Platforms like Northbeam simplify this process by analyzing customer data and providing actionable insights to maximize lifetime value.
CLV in action: An e-commerce brand identifies that high-value customers tend to purchase accessory items within three months of their initial purchase. By implementing a targeted email campaign with accessory recommendations, the brand increases repeat purchases and boosts CLV by 15%.
Metric #2: Return on Ad Spend (ROAS)
Return on Ad Spend (ROAS) measures the revenue generated for every dollar spent on advertising.
ROAS is a key indicator of campaign performance and efficiency. It helps marketers identify which channels and campaigns deliver the highest returns, ensuring that ad spend is allocated strategically.
To calculate ROAS, divide the revenue generated by an ad campaign by the cost of that campaign. Northbeam’s attribution capabilities provide a clear view of ROAS across channels, enabling you to optimize ad spend in real-time.
ROAS in action: A direct-to-consumer skincare brand identifies that TikTok ads yield a higher ROAS than Google Ads. By reallocating 20% of their budget to TikTok campaigns, they achieve a 25% overall increase in revenue from paid media.
Metric #3: Marketing Efficiency Ratio (MER)
Marketing Efficiency Ratio (MER) is the ratio of total revenue to total marketing spend.
MER provides a high-level view of how efficiently your marketing efforts drive revenue. Unlike ROAS, which focuses on individual campaigns, MER gives a holistic perspective, making it particularly useful for evaluating overall marketing performance.
To calculate MER, divide total revenue by total marketing spend. Use tools like Northbeam to monitor this ratio and identify opportunities to optimize your marketing mix.
MER in action: A subscription box company notices that MER decreases during months with high email engagement. They focus on improving email frequency and content quality, boosting overall MER by 10%.
Metric #4: Customer Acquisition Cost (CAC)
Customer Acquisition Cost (CAC) is the cost of acquiring a new customer through marketing and sales efforts.
CAC is essential for assessing the sustainability of your growth strategy. By keeping CAC low relative to CLV, businesses can ensure long-term profitability.
To calculate CAC, divide the total cost of marketing and sales by the number of new customers acquired. Northbeam’s advanced analytics help identify areas where CAC can be reduced, such as optimizing ad campaigns or improving targeting.
CAC in action: A fitness apparel brand finds that ads targeting lookalike audiences on Facebook generate lower CAC than other campaigns. By doubling down on these audiences, the brand reduces CAC by 20% over three months.
Metric #5: Conversion Rate (CVR)
Conversion Rate (CR) is the percentage of users who take a desired action, such as making a purchase or signing up for a newsletter.
CR is a direct indicator of how well your marketing efforts are turning prospects into customers. Higher conversion rates mean better ROI for your campaigns.
Calculate CR by dividing the number of conversions by the total number of visitors and multiplying by 100. With Northbeam, you can track conversion rates across channels and campaigns to pinpoint areas for improvement.
CR in action: A SaaS company identifies a low CR on its pricing page. Testing a simplified pricing table and adding testimonials increase the conversion rate by 12%.
Metric #6: New vs. Returning Customers
This metric compares the ratio of first-time buyers to repeat customers over a given period.
Understanding the balance between new and returning customers helps businesses optimize acquisition strategies while nurturing loyalty for sustainable growth.
Use tools like Northbeam to segment your audience and monitor the performance of campaigns to acquire new customers versus retaining existing ones.
New vs. Returning Customers in Action: A home decor brand tracks a dip in returning customers through Northbeam. They launch a loyalty program offering discounts on second purchases, increasing the returning customer rate by 25% within a quarter.
Achieve marketing success with these top metrics
Tracking these metrics consistently is key to achieving long-term marketing success. By focusing on metrics like CLV, ROAS, MER, CAC, conversion rates, and new vs. returning customers, businesses can make data-driven decisions that enhance performance and profitability.
Northbeam’s advanced analytics and attribution tools make it easy to track and optimize these metrics, providing a comprehensive view of your marketing performance. Request a demo and discover how Northbeam can empower your business for long-term success.
Incrementality is one of those marketing buzzwords that gets thrown around a lot, but ask five marketers what it means, and you’ll get five different answers.
For some, it’s synonymous with lift measurement. For others, it’s a vague concept bundled in with attribution.
But incrementality isn’t just jargon.
It’s a rigorous, experiment-driven methodology that helps marketers answer a critical question: Did this campaign actually drive results, or would they have happened anyway?
In an era where privacy regulations, cookie deprecation, and platform data restrictions are eroding traditional attribution models, incrementality has emerged as a must-have measurement tool.
It cuts through the noise, isolates true campaign impact, and empowers marketers to make smarter, data-backed decisions.
In this guide, we’ll define what incrementality really is, explore how incrementality tests work, discuss their applications and challenges, and explain how incrementality fits alongside attribution models like Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA) for a holistic measurement strategy.
What Is Incrementality?
Incrementality is the measure of the additional impact, or "lift," that a marketing activity has on a desired outcome, beyond what would have happened without it.
In other words, it answers the critical question: Did this campaign actually drive more conversions, or would those sales have happened anyway?
Unlike traditional attribution models, which often rely on tracking clicks and views to assign credit, incrementality seeks to establish causality, not just correlation.
It does this by comparing the performance of a treatment group (exposed to the marketing effort) against a control group (not exposed to the marketing effort) in order to isolate the true effect of the campaign.
For example, if a paid social campaign generates 1,000 purchases, but the control group, which wasn’t shown the ads, still produces 800 purchases, the incremental lift is only 200 purchases.
That’s the portion of impact you can confidently attribute to the campaign.
This causal measurement approach allows marketers to separate “would-have-happened-anyway” actions from genuine campaign-driven outcomes, providing a clearer, more accurate picture of marketing effectiveness.
How Incrementality Works
Now that we’ve covered what incrementality is, let’s talk about how it works.
At its core, incrementality analysis is a form of experimentation that borrows directly from the scientific method.
It’s a structured way to answer: Did my marketing effort cause a measurable lift in results?
The process follows a simple but rigorous framework:
Hypothesis: Define a clear hypothesis, such as “Running Meta ads will increase conversions by 10% compared to no ads.”
Test Design: Split your audience or regions into treatment (exposed to the marketing effort) and control (not exposed) groups.
Measure Outcomes: Track performance across both groups without making mid-test changes.
Conclude Impact: Analyze the difference in outcomes to determine the true lift (the “incrementality”) caused by your campaign.
Common Types of Incrementality Experiments
There are various types of causal marketing experiments that measure incrementality, including:
Randomized Control Trials (RCTs): The gold standard of experimentation, where individuals are randomly assigned to treatment or control groups. Most often used in digital campaigns like Meta Ads.
Holdout Tests: A portion of the audience is “held out” from seeing specific ads to serve as the control. For instance, holding out 10% of your audience from a Google Ads campaign.
Geo-Testing: Geographically splitting markets into treatment and control groups. This method is popular for offline media like TV or radio, where randomizing individuals isn’t feasible.
These controlled, causal marketing experiments are critical for distinguishing causality from coincidence, providing marketers with reliable, actionable insights into what’s truly moving the needle.
Why Incrementality Matters in Modern Marketing
Attribution models like Multi-Touch Attribution (MTA) have long been the go-to for measuring marketing performance.
These models attempt to track every touchpoint a customer interacts with — clicks, views, and engagements — to assign credit for conversions.
But in today’s privacy-conscious world, attribution is becoming less reliable (if you don’t use Northbeam, that is).
The deprecation of third-party cookies, iOS 14’s App Tracking Transparency (ATT) updates, and other privacy regulations have significantly reduced marketers’ ability to track individual user behavior across platforms.
As visibility fades, attribution models often fall back on shaky assumptions, leading to over-attribution of conversions to the last-clicked channel or missing data altogether.
That’s where incrementality steps in.
Unlike attribution, which correlates touchpoints with outcomes, incrementality isolates causality.
It doesn’t rely on user-level tracking to infer which ad drove a sale. Instead, it measures the actual lift generated by marketing efforts through controlled experiments.
This makes it an essential tool for:
Proving ROI in a post-cookie world: With deterministic tracking on the decline, incrementality analysis offers a privacy-safe way to demonstrate true campaign effectiveness.
Budget justification: CMOs and marketing teams can use incrementality results to confidently defend their budgets, showing exactly how much impact campaigns are driving beyond organic or baseline performance.
Smarter budget allocation and channel testing: Incrementality experiments help identify which channels, creatives, and campaigns deliver real business value, informing future spend and optimization strategies.
In an era where marketing measurement is growing more opaque, incrementality offers clarity.
It empowers marketers to cut through the noise of vanity metrics and focus on what truly matters: driving incremental business growth.
Incrementality Challenges and Considerations
While incrementality testing is a powerful measurement tool, it’s not without its challenges. Running a clean, reliable test requires careful planning, resources, and a strong understanding of statistical principles.
Here are some key considerations:
1. Sample Size and Statistical Significance
To detect true lift, incrementality tests need a large enough sample size to ensure statistically significant results.
Small audiences or short test durations can lead to inconclusive findings, where observed differences might just be random noise.
For brands with lower conversion volumes, achieving statistical power may require longer test periods or broader audience targeting.
2. Spillover Effects
In a real-world environment, it’s difficult to perfectly isolate treatment and control groups.
A customer in the control group might hear about a promotion from a friend or encounter an ad indirectly through organic channels.
These spillover effects can blur the lines between exposed and unexposed groups, diluting the measured lift and complicating interpretation.
3. Operational Disruptions
Incrementality tests demand strict control over campaign variables.
Once a test begins, mid-test changes, such as tweaking budgets, creatives, or targeting, can compromise the experiment’s validity.
For many fast-moving marketing teams, this “no changes allowed” rule can be operationally challenging, requiring discipline and buy-in from stakeholders.
4. Expertise Required for Test Design and Analysis
Incrementality testing isn’t a “set-it-and-forget-it” exercise.
Designing a robust test framework, managing control variables, and interpreting the results require a level of statistical and analytical expertise.
Missteps in setup or analysis can lead to flawed conclusions, undermining the very clarity incrementality is meant to provide.
But despite these challenges, the payoff is worth it for many marketers. With the right planning and tools, incrementality tests can deliver insights that provide an honest and actionable view of marketing impact.
Incrementality vs MMM vs MTA
Incrementality testing isn’t the only way to measure marketing impact, but it solves for gaps that other methods may leave behind.
To understand where incrementality fits, it’s important to compare it with Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA): two of the most common measurement approaches.
Why Best-in-Class Marketers Combine All Three
No single measurement method is perfect on its own.
MTA provides immediate, granular insights into digital performance but struggles with data gaps in a privacy-first world.
MMM offers a comprehensive, long-term view of how all marketing and external factors influence business outcomes but lacks real-time agility.
Incrementality fills in the blanks by establishing causality for specific campaigns or channels, delivering mid-term insights that neither MMM nor MTA can reliably provide alone.
How to Get Started with Incrementality Testing
Ready to measure the true impact of your marketing efforts?
Start by selecting channels where spend is significant and results are ambiguous.
Paid media platforms like Meta Ads, Google Ads, and TV advertising are ideal candidates because they often have measurable outcomes but suffer from attribution noise.
Focus on campaigns where you need to justify budget or validate their true contribution to conversions.
Step 2: Design a Control/Treatment Framework
Decide how you’ll split your audience into treatment (exposed to the campaign) and control (not exposed) groups.
There are two primary approaches:
Audience-Based Holdouts: Randomly exclude a portion of your target audience from seeing the ads (ideal for digital platforms).
Geo-Testing: Divide geographical regions into treatment and control groups (commonly used for offline media like TV or radio).
Ensure the groups are comparable in terms of demographics, historical performance, and potential exposure to avoid skewed results.
Step 3: Run the Test Long Enough to Reach Statistical Significance
Patience is key.
Incrementality tests require sufficient sample sizes to produce reliable insights. The duration of the test depends on factors like audience size, conversion rates, and desired confidence levels.
Avoid making any mid-test changes to budgets, creatives, or targeting, as this can invalidate results.
Step 4: Analyze Findings and Apply Them to Strategy
Once the test concludes, compare performance between the treatment and control groups to calculate the incremental lift.
These insights should directly inform:
Budget allocations (increase investment in high-lift channels)
Campaign optimization (refine strategies that underperform in lift)
Strategic planning (understand true ROI for future initiatives)
Incrementality testing doesn’t have to be daunting. Platforms like Northbeam streamline the entire process by integrating incrementality insights with your ongoing attribution models.
This allows you to continuously measure true campaign lift while maintaining a comprehensive view of customer journeys across channels.
Measure What Matters
In a marketing landscape where attribution is becoming less reliable and measurement gaps are widening, incrementality testing offers a clear path forward.
By focusing on causality, not just correlation, incrementality in marketing isolates the true impact of your campaigns, cutting through the noise of superficial metrics and incomplete tracking data.
While Multi-Touch Attribution (MTA) and Marketing Mix Modeling (MMM) play essential roles in a robust measurement strategy, incrementality fills a critical gap.
Best-in-class marketers are combining all three to build a measurement framework that’s resilient, data-driven, and future-proof.
Today’s customer journeys are anything but straightforward.
A shopper might discover your brand on TikTok, research on Google, compare on desktop, and finally purchase through an email offer on their phone, all within a single week.
With so many channels, devices, and touchpoints in play, it’s no wonder marketers struggle to connect the dots.
That’s where customer journey mapping comes in.
What is customer journey mapping? At its core, it is the practice of visualizing and analyzing how customers move through the stages of awareness, consideration, purchase, and beyond.
Done well, it transforms scattered interactions into a clear, data-driven picture that helps marketers improve ROI and customer experience at the same time.
In this guide, we’ll cover:
What customer journey mapping is and why it matters
The core components every map should include
Practical examples of how marketers use journey maps
A step-by-step process for building your own
Common pitfalls and how to avoid them
Tactics to optimize your map over time
In this guide to journey mapping for marketers, you’ll learn how to turn messy, multi-channel customer paths into an actionable framework that aligns teams, reduces friction, and drives growth.
Definition & Why It Matters
Customer journey mapping is a visual, data-informed representation of how a customer persona moves through the stages and touchpoints needed to achieve a goal.
That goal could be making a purchase, subscribing to a service, becoming a loyal advocate, or something else relevant to your business.
Instead of thinking about marketing as a set of disconnected campaigns, journey mapping gives you a holistic picture of how real people experience your brand.
Why does this matter? Because journeys are rarely linear. Customers bounce between channels, devices, and content types before making a decision.
A good journey map helps you:
Reveal friction points and “moments that matter.” Pinpoint where customers drop off, hesitate, or need more reassurance.
Align teams across functions. Marketing, product, sales, and customer support all get on the same page about what customers actually experience.
Inform smarter content and media strategies. Match the right message and channel to the right stage of the journey.
Boost conversion and retention. By removing friction and doubling down on high-impact moments, you naturally increase ROI, customer lifetime value (CLV), and long-term loyalty.
There’s also a direct financial benefit: journey maps guide budgeting and prioritization.
Instead of spreading spend evenly or chasing the loudest channel, you can see exactly which stages and touchpoints drive the most value, and which ones aren’t worth continued investment.
How Customer Journey Mapping is Used
Customer journey maps aren’t just pretty visuals, they’re practical tools that marketers can use to make smarter, more targeted decisions every day.
When you can see the journey laid out, it becomes much easier to match the right message, offer, or channel to the exact stage a customer is in.
Here are a few of the most common applications:
Campaign Sequencing
Use the customer journey map to align messaging with stage-specific needs.
For example, focus on credibility and education during awareness, then shift to urgency or differentiation during consideration and purchase.
Conversion Rate Optimization (CRO) Roadmap
Identify the high-friction steps that cause the biggest drop-offs.
By prioritizing tests where customers get stuck, you maximize the return on optimization efforts.
Lifecycle Marketing
Design onboarding, activation, retention, and win-back campaigns that address the full customer lifecycle, not just acquisition.
Channel Orchestration
Assign the right channels to each stage.
Paid social might be ideal for early awareness, while email, SMS, or loyalty programs are better suited for retention and advocacy.
To make this more concrete, here are two quick examples:
eCommerce brand:
A customer might see a UGC ad
Click through to the product detail page (PDP)
Add to cart
Checkout
Receive post-purchase education emails
Eventually leave a review or referral
Subscription service:
A prospect discovers the brand via an influencer
Signs up for a free trial
Receives a guided onboarding sequence
Hits a paywall
Receives churn-prevention messaging
Later reactivates after canceling
In both cases, journey maps make it clear where marketers should invest resources, what messages will resonate, and how to keep customers moving forward.
Core Components of an Effective Customer Journey Map
A strong customer journey map balances structure with insight. Here are the core components to include in your customer journey map:
Personal and goal: Start by defining who you’re mapping and what success looks like. Is it a first-time shopper making a purchase? A subscriber activating their account? A repeat customer advocating for your brand? Narrowing the scope ensures the map stays actionable.
Stages: Break the journey into key phases: awareness, consideration, purchase, onboarding, and retention/advocacy. These stages give your map structure and help everyone speak the same language when discussing customer behavior.
Touchpoints and channels: List all the interactions across owned (website, app, email), paid (ads, sponsorships), and earned (reviews, referrals) channels. Don’t forget offline moments if they matter to your brand.
Customer lens: Capture the human side of the journey: the jobs-to-be-done, common questions, emotions, objections, and triggers at each stage. This context is often what separates a useful map from a surface-level one.
Friction and opportunities: Identify where customers drop off, get stuck, or lose interest. Just as importantly, highlight the opportunities where the right content or offer could move them forward more smoothly.
Ownership and KPIs: Assign responsibility for each stage or touchpoint. Who’s accountable for improving product page-to-cart conversion? Who owns retention emails? Tie each area to measurable KPIs so insights translate into action.
Together, these components transform a journey map from a static diagram into a living playbook for growth and CX.
How to Create a Customer Journey Map
A customer journey map is only as good as the process behind it.
The goal isn’t to produce a beautiful diagram: it’s to build a tool that reflects real customer behavior and sparks action across your team.
Here’s how to map the customer journey:
1. Define Scope, Persona, and Success Metrics
Start small. Pick one persona and one primary journey (for example: a first-time customer completing their first purchase).
Decide what success looks like (conversion, activation, or repeat engagement) so the map has a clear focus.
2. Gather Data from Multiple Sources
Start quantitative: Web and app analytics, attribution data, funnel conversion rates, time between steps, support tags.
Then move on to the qualitative: Surveys, interviews, session replays, customer support tickets, sales notes, or on-site polls.
Combining hard numbers with customer voice ensures your map captures both what’s happening and why.
3. List Stages and Touchpoints
Break the journey into its phases (awareness, consideration, purchase, onboarding, retention/advocacy) and add the touchpoints within each.
Don’t forget micro-conversions like viewing a product detail page, adding to cart, or opening an email.
4. Add Customer Context
For every stage, note the customer’s motivations, anxieties, and questions.
Highlight the “moments that matter” where reassurance, education, or incentives can make the difference between drop-off and progress.
5. Document Friction and Hypotheses
Call out where customers abandon the journey or get stuck.
Then propose testable ideas for fixing the problem; for instance, simplifying checkout steps or adding social proof to product pages.
6. Assign Owners and KPIs
Make sure every stage has a clear owner and measurable metric. This ensures insights don’t die on the page.
7. Validate with Stakeholders
Share the draft map with marketing, product, customer support, and even sales or finance.
Cross-functional input strengthens the map and helps with buy-in when it’s time to act.
8. Implement and Iterate
Treat the map as a living document.
Run experiments to address friction, track results, and update the map quarterly as your product and market evolve.
Common Mistakes and Misconceptions
Customer journey mapping for marketers is powerful, but only if it’s done right.
Many teams fall into the same traps, which can leave them with a pretty diagram that gathers dust instead of driving results.
Here are the most common mistakes to avoid:
Lack of Data
Building a map based on hunches or assumptions is a recipe for misalignment.
Real insights come from pairing analytics with the customer’s own voice through surveys, interviews, and support feedback.
Over-Complexity
If your map has so many touchpoints, arrows, and annotations that no one can read it, it won’t get used. Keep it focused and simple enough to be actionable.
Clicks-Only
A journey isn’t just a series of digital interactions.
Ignoring customer emotions, objections, or even offline moments leaves huge blind spots.
Static Artifact
A map that never gets updated quickly becomes irrelevant.
Journeys evolve as your product, channels, and customer behaviors change. Your map should too.
No Ownership or KPIs
Without assigning responsibility and measurable goals, the insights stay theoretical.
Every stage should have a clear owner and metric to track progress.
The takeaway: a good journey map is grounded in data, human-centered, simple to use, and always evolving.
How to Improve or Optimize Your Customer Journey Map
The best way to strengthen your journey map is to tackle the common pitfalls head-on.
Here’s how to turn each mistake into an opportunity for improvement:
Instrument the Map
Instead of guessing, ground your map in reality.
Overlay metrics like conversion rates between stages, average time to progress, drop-off hotspots, and lifetime value (LTV) by path.
This makes your map a data-powered decision tool.
Start Small, Go Deep
Avoid overwhelming the team with an unreadable tangle of touchpoints.
Begin with one persona and one primary journey. Once you’ve proven value, like boosting first-time purchases or cutting churn, expand gradually.
Test Surgically, Beyond Clicks
Journeys aren’t just a series of clicks.
Use experiments and A/B tests to address customer motivations, anxieties, and objections at key stages.
Feed the results back into the map so it reflects both behavior and emotion.
Operationalize Your Map
A map that never changes is quickly outdated.
Build a cadence to review metrics and refresh the map quarterly, and tie updates directly into your marketing or product roadmap so it stays active and relevant.
Prioritize Actions by Impact
Assign clear owners and focus first on the friction points with the biggest revenue stakes.
When responsibility and KPIs are in place, insights move from theory to measurable progress.
And underpinning all of this is reliable data.
Tools like Northbeam provide cross-channel attribution and performance insights, helping you validate paths, quantify stage impact, and track improvements over time.
From Map to Momentum
Customer journey mapping takes what often feels like a fuzzy, unpredictable experience and turns it into a clear, prioritized plan for growth.
By visualizing the stages customers move through, and layering in real data, ownership, and KPIs, marketers can move beyond guesswork to make decisions that improve both ROI and customer experience.
The best advice is to start small: build an initial map for one persona, validate it with data, and refine it through iteration.
From there, you can expand to more journeys and continue optimizing, using the map as a living tool that guides strategy across teams.
How to Navigate the Surge in Digital Ad Costs with YouTube Ads + Northbeam
Digital media is more expensive than ever. Meta’s rising Cost per 1,000 impressions (CPMs) alongside the skyrocketing cost per click (CPC) of Google Search, Shopping, and Performance Max (PMAX) are creating challenges for many advertisers.
Plus, since Meta is often positioned as an awareness and consideration channel as part of holistic marketing strategies, the rising costs are sending advertisers in search of cheaper “higher-funnel” media with similar (or better) performance.
Enter YouTube Ads.
YouTube advertising inventory can be purchased through Google Ads as “Video” ads. While Media buying teams are often intrigued about buying YouTube inventory, they rarely understand how to think about YouTube and measure its impact.
To be a better YouTube advertiser, you need to understand four things:
YouTube users rarely click on ads
Although you buy YouTube inventory with Google Ads, you can't think about YouTube through a traditional click-based lens like with Search and Shopping.
Clicks from YouTube tend to be a bonus side-effect of advertising, and shouldn’t be one of the primary key performance indicators (KPIs) considered when running YouTube campaigns.
The problem is that most digital advertising platforms are designed to measure media performance based on user click behavior. Without clicks, significant attribution is lost in campaign reporting.
Instead of clicks, your goal with YouTube is to generate the most engaged impressions and views for the lowest possible cost.
Since, when someone is on YouTube and sees an ad, they're likely already watching a video they chose to watch. The ad is interrupting their intended experience, which was to stay on YouTube and watch their content.
The ads you serve them need to be designed around this experience and the user's expectation. They probably have little desire to click on anything unless it's another YouTube video. So, expecting them to click on your ad will set yourself up to be disappointed and ultimately, unsuccessful.
When using Google Ads for reporting, Impressions, Views, View Through Rate (VTR), Cost per View (CPV) are some of the KPIs you should be referencing.
You can't rely solely on KPIs inside Google Ads
YouTube Ads are difficult to track within Google Ads using metrics like Conversions and ROAS. If you rely on these types of metrics to guide your media buying decisions, you’re probably leaving a ton of money on the table.
Utilising Northbeam is a no-brainer. The long lookback windows alongside one-day view attribution and proprietary modelling allows advertisers to see and understand far more revenue attributed to YouTube than most other sources.
You also get a central source of truth for your Media Efficiency Ratio (MER) and Customer Acquisition Cost (CAC) metrics, since Northbeam uses your store’s revenue as an anchor to compare against all of your ad spend.
There’s a few other things to look at while scaling your YouTube campaigns.
▪ GA4: A free, useful tool for analyzing on-site behavioral reporting. Although you likely won’t get much in the way of revenue attribution for YouTube from GA4.
▪ Post-Purchase Surveys: As customers "How did you first hear about us?" directly on the order confirmation page, and list all of your marketing channels as options. This is a measure of customer-perspective marketing channel influence.
▪ Lift Measurement: Google surveys users on YouTube for statistically significant lift measurement. These can be expensive and should probably be your last choice.
▪ Search Lift Studies: With the help of your Google rep, you can identify measurable lift from Search, correlated to your YouTube Ads campaigns.
▪ Google Search Console: Branded search term data changes. If your YouTube campaigns are getting your brand to stick in peoples’ minds, they’ll inevitably go to Google and search for your brand.
▪Google Trends: Branded search term and category data changes (see real example below):
▪ Geo Study: One control location, one test location. Measure the difference in branded searches, traffic, and other signals that indicate users are being influenced by your YouTube Ads.
▪ MER/Customer Acquisition Cost (CAC): These should improve over time as more people, having become aware of your brand, start purchasing.
▪ Email/Short Messaging Service (SMS) Platform: Monitor opt ins. Typically, email and SMS opt ins will increase with more relevant traffic, which comes from YouTube mostly in the form of branded search.
Impression Frequency is a key component
Users probably won’t remember your brand or product if they see your ads only once or twice. It takes time for your message to stick inside people’s minds. If you're targeting the right audience with the right message, aim for an average impression frequency per user over a seven day period between four and six (measured within Google Ads).
That means you want every user to see your ads at least four to six times per week. User engagement tends to rise with higher frequencies, up to a point.
Try testing Ad Sequence campaigns for ensuring the same users are exposed to several different ads at a high frequency.
Creative quality needs to be high
Your ads won't perform if creatives aren't good enough. You can tweak the settings in Google Ads a million different ways, but your creatives are what make or break a campaign.
Here are some tips to getting YouTube Ad creatives right:
➡ Hook your viewers in the first three seconds
➡ Test running ads 30 seconds or longer
➡ Use audio and voice overs whenever possible
➡ Test different videos in different aspect ratios (square, vertical and horizontal)
➡ Use your brand prominently (logos, colours, messaging throughout)
➡ Use a call to action at the end of the video
➡ Use different videos to walk users through all steps of the customer journey:
Brand feature video (introduce your brand to viewers in a memorable and engaging way)
Product-focused video (features, benefits, product demo)
Social proof video (customer UGC is great for this)
Offer video (showcase your offer and why viewers should buy from you)
YouTube is not just another advertising channel; it's a strategic asset that, when used correctly, can significantly reduce costs while expanding reach. By focusing on engagement metrics, integrating advanced tools, and producing standout creatives, you can leverage YouTube to its fullest potential.
This Blog was written by contributing guest writer Craig Graham. Check him out below:
You don’t need me to tell you that AI is a game changer. Just as the Industrial Revolution reshaped the way we worked in the 18th and 19th centuries, the emergence of artificial intelligence moves us into a new era. AI represents a paradigm shift in the way we interact with machines and process data, promising to upend industries, economies, and our daily lives. Rapid advancements in AI technology have already begun to change the way businesses operate, with automation and machine learning algorithms (more on these later) streamlining manual processes and augmenting our existing capabilities.
As marketers, AI offers us unparalleled opportunities for innovation. From personalized customer experiences and ultra-targeted advertising campaigns to predictive analytics and campaign automation, AI empowers us to unlock new channels for growth. In this guide, we’ll go over what artificial intelligence is, including the most crucial concepts you’ll need to know as a modern marketer in 2024 and beyond.
What is Artificial Intelligence?
Broadly defined, artificial intelligence (AI) is a field of computer science that focuses on creating systems and machines that can perform tasks and solve problems that would typically require human intelligence. Such tasks can range from recognizing patterns, making predictions, learning from experiences, and even understanding languages. At its core, AI aims to recreate synthetic, human-like intelligence in machines. This generally involves developing algorithms and models that enable computers to perceive their environment, reason about it, and make decisions accordingly. Digital assistants (Siri, Alexa), GPS guidance apps, self-driving vehicles, and generative AI tools (such as Open AI’s ChatGPT) are just a few examples of how artificial intelligence has changed the way we live and interact with machines in our daily lives.
Since AI aims to provide machines with human-like capabilities, it has the potential to help us save significant amounts of time by filling in for tasks that we’re not ideally suited for. Unlike humans, AI can sift and sort through large amounts of data to identify patterns that may have been missed by the human eye, automate otherwise tedious and manual tasks, and even mimic our language patterns (chatbots and LLMs). This has helped us tackle complex problems previously thought to be impossible due to the vast amounts of processing power required to solve them, such as using machine learning models to examine tissue samples and identify abnormalities at the cellular level, assisting pathologists in diagnosing diseases more quickly and accurately. In the financial sector, AI and ML algorithms have been invaluable in detecting fraudulent and criminal transactions by analyzing patterns and anomalies in large datasets to ensure the security of transactions. AI has even been used to identify suspicious betting patterns in the NBA, leading to a lifetime ban of a former player.
AI is generally split into two broad categories: weak AI and strong AI. Weak (or narrow) AI refers to AI that automates specific tasks. Although it can outperform humans, this is only true for the specific thing it was designed for such as recognizing patterns. Almost all of the AI systems that exist today are examples of weak AI, including marketing automation platforms, chatbots, and social media algorithms. Strong AI, or sometimes referred to as artificial general intelligence (AGI) is a type of AI which possesses human-like intelligence and adaptability, solving problems it’s never been trained for. When we see dystopian sci-fi films warning us of AI (Skynet anyone?), we often see strong AI that has pushed past human intelligence into a type of superintelligence. It’s important to note AGI is mostly hypothetical at this point, and it’s unclear whether we can ever create it because AGI would require machines to have consciousness and self-awareness, far beyond our current technological abilities.
Machine Learning: Continuous Improvement
The main approach to building AI systems is through machine learning: a subset of AI that focuses on developing techniques that enable machines to learn from large amounts of data by identifying relationships and patterns in the data. A ML algorithm uses various statistical techniques to “learn” to get progressively better at a task without having to be explicitly programmed for that task. The algorithm uses historical data as an input (such as previous marketing spend) to predict new output values (future expected sales revenue). ML uses a few different types of “learning” techniques including supervised learning (expected output for the input is known due to labeled data sets), unsupervised learning (expected outputs are unknown due to unlabeled data sets), and reinforcement learning (algorithms learn by interacting with an environment and receiving positive or negative feedback). As marketers, there are four types of machine learning methods that we should be aware of.
Neural Networks, the most popular way of conducting machine learning, are a series of algorithms that process data by mimicking the structure of the human brain. Neural networks consist of layers of interconnected nodes that analyze and pass information between each other by working together to decipher large datasets. They generally have an input layer which receives the raw data, followed by several layers which each apply some sort of data transformation before passing the result to the subsequent layer. After making its way through these processing layers, a final layer will produce the network’s output which could be a classification or some type of prediction. By adjusting the strength of connections between these nodes and layers, neural networks learn to recognize complex patterns within data, make predictions based on new inputs (that it has never seen before), and even learn from mistakes.
Deep learning refers to a type of multi-layered neural network that uses a large number of hidden layers (think dozens or even hundreds) that process input data to capture increasingly abstract features of that data to identify complex patterns. The “deep” in deep learning refers to the number of layers in the neural network - the more layers, the “deeper” the network. This makes deep neural networks well-suited for certain narrow AI tasks such as recognizing images, understanding human speech, and translating between different languages. As marketers, there are two key applications of deep learning and neural networks that you should know about:
Computer vision involves the automatic extraction, analysis, and interpretation of useful information from media formats such as images and videos. Deep learning has significantly advanced this field with the development of convolutional neural networks (CNNs), which are specifically designed for processing grid-like data by detecting spatial hierarchies and features like edges, textures, and objects. Computer vision can help digital marketers in several ways:
Content Management: Computer vision can automatically tag creative assets, for instance categorizing by product lines, making it easier to find, store and retrieve visual assets related to specific products.
Content Creation: GenAI (much more on this in the next section) can create realistic images from textual descriptions, convert sketches into photos, or even generate high-quality artwork. Instead of having to pay for expensive photo shoots, marketers can now generate or modify visual assets with the click of a button.
Customer Engagement: New technologies such as visual search allows customers to search for products using images instead of text. Platforms such as Pinterest and Google Lens enable users to find similar items, thereby enhancing the shopping experience and driving incremental revenue. Augmented reality (AR) can also be used to allow customers to visualize products in different environments, which is particularly useful in certain segments such as fashion and home decor where an initial trial is crucial before making a purchase.
Natural Language Processing (NLP) focuses on the interaction between computers and human language, enabling machines to understand and even generate human language. Deep learning models, especially transformers (ChatGPT stands for Generative Pre-Trained Transformer), have pushed the boundaries of what is possible in NLP. Marketers already know that deep learning models like ChatGPT can create blog posts, product descriptions, social media copy, but here are a few other relevant use cases powered by NLP:
Chatbots and Virtual assistants: AI-powered chatbots can handle customer queries in real-time, providing instant support and improving the customer experience without needing to hire a large human team. These systems use NLP to process, understand, and generate responses to customer inquiries, freeing up human agents for more complex tasks. A good example would be the various chatbots used by major airlines, or the pop-up chatbots found on SaaS and eCommerce websites.
Sentiment Analysis: NLP can pick up on public sentiment and gauge how customers are responding to marketing activities. This includes analyzing customer reviews, social media posts, and other forms of feedback to figure out how to respond appropriately. This generally involves tracking the ratio of positive and negative sentiments so marketers can respond to customer concerns promptly.
GenAI: Fear for your job?
Let’s talk about the most hyped form of AI: Generative AI. GenAI is a broad label describing any type of AI that can produce text, images, video or audio clips by learning from training data and generating new, unique outputs that mimic the statistical properties of the training data. GenAI uses NLP and machine learning to create this new content, but it differs from other ML applications because its purpose is to produce wholly new things as opposed to simply recognizing and classifying data like other narrow AI (search algorithms for example). Two prominent types of generative models power most of the tools we use today:
Generative Adversarial Networks (GANs): a class of models designed to generate synthetic data that closely mimics real data. Introduced in 2014, GANs consist of two main components: a generator neural network that generates new data samples, and a discriminator neural network that evaluates the authenticity of the generated data. The training process is “adversarial” because these two neural networks are asked to play each other in game-like scenarios until the discriminator can no longer reliably tell the difference between real data and synthetic data produced by the generator.
Transformer-Based Models: Most people think of ChatGPT when they think of GenAI, which is a transformer-based model that uses an attention mechanism to weigh the importance of different words in a sentence. These models undergo a two-phase training process: in pre-training, the model is given a massive amount of text data to learn language patterns, grammar, and so on. During this phase, the model learns how to predict the next word in a sentence, given the words already preceding it for context. Afterwards, the model is fine-tuned on a smaller dataset to specialize in specific tasks such as text summarization, translation, or answering questions.
GenAI has the capability to significantly enhance marketing operations and strategies by allowing marketers to create personalized content at scale, improve customer engagement, streamline processes, amongst countless other use cases. Personalization and customization of content have become table stakes in an increasingly digital media landscape; generative AI offers us an avenue to quickly and easily produce large amounts of content to meet this demand for authentic experiences. Consider the immensely successful “Share a Coke” campaign: Coke found an ingenious way to create personalization for the masses, but it was a massive creative and logistical endeavor to design and produce hundreds of different cans. With GenAI, we can produce similar results without hiring multiple agencies and partners to pull it off. For example, Carvana used GenAI to generate 1 million+ unique videos that reminded customers of the day they met their Carvana vehicles. The marketing team realized that people often have special bonds with their cars, so they used machine learning models to collect basic data such as the car’s model, color, year, purchase data, and location - enriched with cultural events around that time and place to create bespoke “joyrides” to commemorate and celebrate that bond. The best part? It took less than half a workday to process and create all the videos. With GenAI, companies without the resources of Coca-Cola can pull off these types of campaigns within a reasonable amount of time and tighter budget constraints.
Brands and companies across different sectors have taken notice and are using GenAI to improve and increase customer engagement. Spotify is piloting AI-driven voice translation by providing additional languages for top podcasts, all in the podcaster’s voice. The technology leverage’s OpenAI’s voice generation to match the original speaker’s style, making for a more natural listening experience than traditional dubbing so Spotify is able to bridge language barriers and cater to international markets far more effectively than before. This is a step above the mundane chatbots and virtual assistants that have begun to dot the eCommerce landscape, but even these are seeing an evolution. Recent developments in multi-modal AI will further expand what AI chatbots can do to solve customer problems. Imagine a customer submitting a photo of a faulty product and the AI chatbot quickly and accurately providing a resolution based on previous customer service interactions fed into machine learning models. Furthermore, these bots are available 24/7 and can handle large numbers of inquiries simultaneously, offering immediate assistance which reduces wait times and improves customer satisfaction with your brand.
Arguably the most valuable aspect of GenAI for marketers is the potential to enhance creativity and the quality of our output while also reducing the cost of cognition to produce that work. This transformative technology spans across a wide variety of tasks: GenAI is already assisting marketers with crafting blog posts, analyzing consumer feedback, and designing new ad concepts for A/B tests. For creatives, the brainstorming process can be made much easier with GenAI by feeding a basic prompt to get a wide range of ideas. AI can suggest numerous variations on marketing activations, copy, taglines, all from a given theme or concept. Anyone who has experienced creative or writing blocks knows how invaluable it can be able to expand the pool of ideas quickly. Overcoming this “first-mile” problem of finally putting the proverbial pen to paper has caught the attention of major advertisers, as the CEO of WPP recently noted that GenAI can bring up to 10-20 times savings.
As marketers, we should be excited for the potential of GenAI to augment our own creative capabilities. Interestingly enough, a growing number of workers are instead concerned: a recent Forrester survey found that 36% of workers were worried about losing their jobs to AI and automation within the next 10 years. In our opinion, it’s still too early to worry about AI replacing us as knowledge workers. AI is still too prone to errors and generating false information, commonly referred to as “hallucinations.” You may have heard of the 2023 court case Mata v Avianca, in which attorneys submitted a brief researched using ChatGPT that unfortunately contained multiple fake extracts and case citations in a New York court. The lawyers were unaware that ChatGPT can hallucinate and failed to check that all the extracts and cases actually existed, and consequences were swift. The court dismissed the case, sanctioned the lawyers and fined their firm for acting in bad faith. Until GenAI can get to the point of becoming indistinguishable from real human output without errors, we’ll all unfortunately still have to answer emails and check Slack.
Final Takeaways
Whether we like it or not, AI will be top of mind for many marketing leaders for the next few years. With the ability to streamline processes, personalize customer experiences, and generative innovative content, AI offers marketers exciting opportunities for growth and efficiency. GenAI will become even more of a force multiplier in the future, so our best advice is to become comfortable and adept with these tools. Despite concerns about job security, the current capabilities of AI serve more as an optimizer rather than true replacement for human ingenuity and creativity. The next generation of marketers will be very skilled at partnering with machines; make sure you don’t get left behind. It’s crucial to stay informed about advancements and integrate these tools into your workflows and teams.
The Super Bowl isn’t just a championship game—it’s the annual Hunger Games of advertising, where brands throw obscene amounts of money at consumer eyeballs, hoping for a few seconds of undivided attention. In this gladiator pit of marketing, the landscape of digital advertising is constantly shifting, and Northbeam has taken a deep dive into the trends shaping ad spend, auction costs, and campaign performance in the lead-up to the big game.
And because we love a good comparison, we’re putting Super Bowl advertising head-to-head with Black Friday & Cyber Monday (BFCM), the so-called "Ecommerce Super Bowl," to see how the two stack up. The insights we’ve uncovered will help brands sharpen their strategies so they can get more bang for their marketing buck—or at least avoid lighting their ad budgets on fire.
The State of Marketing Before Super Bowl 2025
All data and insights presented in this report are derived from Northbeam users and brands, providing a real-time pulse on how top advertisers approached this year's Super Bowl marketing landscape.
Before the Super Bowl weekend arrived, the digital marketing landscape was already shifting in ways that would impact advertiser strategies. TikTok, once a dominant force, faced a setback as looming ban threats led to a dip in its Super Bowl ad spend share. This opened the door for YouTube, which overtook TikTok to become the third-largest advertising platform for the event, trailing only behind Meta and Google. Meanwhile, AppLovin emerged as a surprising new contender, capturing around 3% of total ad spend—just behind YouTube (3.8%) and TikTok (3.3%).
Despite the ongoing debate and hype surrounding X (formerly Twitter), advertiser interest remained stagnant. The platform held onto a modest 0.13% share of total Super Bowl week spend, showing no notable year-over-year change.
On a broader scale, advertisers collectively increased their Super Bowl week budgets by approximately 7% compared to the previous year. Interestingly, despite the higher spend, overall efficiency remained stable, posting only a modest 0.8% improvement YoY.
The Ebb and Flow of Super Bowl Ad Spend
Like clockwork, Super Bowl ad costs start creeping up the Thursday before game day, peaking over the weekend before mysteriously dropping on Sunday itself. It’s as if advertisers get cold feet at the finish line, giving those who hold out an unexpected pricing advantage. For some context, here is what spend looked like across channels in 2024:
Despite the last-minute cost fluctuation, brands report that their best ad performance tends to happen in the days leading up to the game rather than on Super Bowl Sunday itself. This makes sense—by the time the game rolls around, consumers are more focused on buffalo wings and touchdown dances than clicking on ads.
Interestingly, the Food & Beverage sector leads the charge in Super Bowl ad spend, with the biggest daily jump occurring from Friday to Saturday. However, by Sunday, the increase slows down—perhaps because everyone is already fully stocked with chips and beer, and no amount of last-minute marketing is going to change that.
Traditional media is still kicking, though digital dominates. In 2024, TV placements accounted for almost 5% of the total media mix, while Connected TV (CTV) placements barely scratched 0.5%. Despite the ongoing digital revolution, traditional TV remains a go-to for brands looking for that "big moment"—even if most viewers are on their phones tweeting about the commercials instead of actually watching them.
How Brands Allocated Their Budgets Across Platforms
When it comes to ad spend distribution, Facebook and Google are still the cool kids at the lunch table, eating up over 74% of total spend. Meanwhile, traditional TV, hanging on by a thread, makes up just over 4%, proving that while old habits die hard, they do eventually fade.
Influencer marketing is gaining traction, but it’s still playing in the kiddie pool with under 2% of total ad spend. However, the data shows a small but notable increase in influencer marketing investments as game day approaches, suggesting that brands are warming up to the idea of leveraging social media stars for last-minute hype.
The Cost of Visibility: Auction Price Trends
The closer we get to Super Bowl Sunday, the more advertisers have to pay for the privilege of appearing in front of consumers who are already drowning in ads. Most platforms see a predictable surge in CPMs leading up to the weekend, but here’s the twist: YouTube and Snapchat actually experience slight declines in auction costs over the weekend.
Why? Likely because more inventory becomes available, meaning brands willing to pivot to these platforms could snatch up some last-minute deals while their competitors shell out premium rates elsewhere. Meanwhile, Google and Facebook remain the Fort Knox of digital advertising—consistently expensive, but still where most brands are putting their money.
Super Bowl vs. BFCM: Which Offers Better Returns?
If Super Bowl advertising is the high-stakes poker game of marketing, BFCM is the full-blown shopping apocalypse. BFCM’s spending behavior is defined by an all-out buying frenzy, with ad efficiency peaking as eager consumers throw money at their screens. In contrast, Super Bowl advertising is more of a slow burn, with spending and efficiency spread out more evenly over the week.
Check out the tail on this chart for BFCM 2024:
Most brands see their biggest spend jump between Friday and Saturday of Super Bowl week, whereas BFCM experiences a steady, methodical build-up. Efficiency is also a key differentiator—BFCM’s shopping-driven engagement results in immediate ROI, whereas Super Bowl ad impact tends to be more drawn out, potentially influencing consumer behavior over a longer period.
How to Measure the Impact of Super Bowl Ads
What Should We Be Benchmarking?
Measuring the success of a Super Bowl ad campaign isn’t as simple as checking a few vanity metrics. Brands should take a two-pronged approach: comparing Period over Period (to assess performance changes leading up to and during Super Bowl week) and Year over Year (YoY) (to determine long-term trends and growth compared to previous Super Bowls).
Understanding Super Bowl Week Success
At the end of the day, business success is the ultimate metric. Revenue, traffic, and on-site efficiency will provide the clearest picture of whether the Super Bowl ad spend actually moved the needle. However, peeling back the layers reveals deeper insights:
Marketing Efficiency
Return on Ad Spend (ROAS): Both blended and first-time customer-specific ROAS should be analyzed to determine the financial return on Super Bowl campaigns.
Cost to Acquire a Customer (CPA / CAC): A major benchmark in determining efficiency shifts due to Super Bowl-specific messaging and offers.
Conversion Rate (ECR) & Revenue per Visit: These metrics show if users are just browsing or if they are actually making purchases.
Creative Performance & Engagement
Not all ads are created equal—some flop, some go viral, and others just generate steady engagement. Evaluating creative success means looking at:
Cost per Click (CPC) & Cost per Mille (CPM): Are we paying more per click/impression compared to pre-Super Bowl periods?
Cost per New Visit (CPNV) & New Visit %: Did the campaign bring in new customers or just re-engage the usual audience?
Click-Through Rate (CTR) & Cost per Engagement: A strong creative piece will increase these numbers, proving the messaging resonated with consumers.
The Invisible Lift: Organic & Direct Traffic Impact
Super Bowl ads—especially on traditional formats like TV and out-of-home—aren’t always trackable through direct clicks. But that doesn’t mean they don’t work. Brands should analyze:
Direct Traffic & Organic Search Traffic: Did the Super Bowl campaign boost unprompted brand searches and direct visits?
Organic Social Traffic: If users discuss, share, or tag the brand after seeing the ad, that’s a win—even if it’s not directly attributable.
New Customer %: What portion of total sales came from first-time buyers influenced by Super Bowl exposure?
Multi-Touch Attribution: The Full Customer Journey
Looking at only first-click or last-click data gives an incomplete picture. A combination of First-Touch (to assess new journey starts) and Multi-Touch Attribution (to understand how ads assisted conversion) provides a more accurate measurement of true campaign impact.
Measuring Brand Search Lift & Omni-Channel Impact
When Super Bowl campaigns succeed, consumers tend to Google the brand name more. Tracking changes in:
Total Search Impression Volume (paid and organic)
Paid Brand Search Impression Share
Additionally, Super Bowl ads can have a halo effect across other sales channels, including Amazon and third-party marketplaces. Brands should analyze whether there’s a correlation between increased spend on direct channels and spikes in these external platforms.
The Long Tail Effect: Tracking Performance Beyond the Super Bowl
Finally, brands shouldn’t expect their Super Bowl efforts to disappear once Monday rolls around. Depending on the business model, the impact of Super Bowl marketing can extend for weeks or even months. Monitoring efficiency beyond the immediate timeframe will ensure brands truly understand the long-term value of their investment.
Lessons for Brands: How to Maximize Super Bowl Advertising Impact
So, what’s the playbook for brands looking to maximize their Super Bowl ad dollars? First, don’t just blindly dump your entire budget into game-day ads. The data shows that efficiency often peaks before the Super Bowl itself, meaning early spend could yield better results. If you must advertise over the weekend, platforms like YouTube and Snapchat may offer an unexpected cost advantage.
Secondly, while traditional TV still has its place, it’s no longer the undisputed king of Super Bowl advertising. With Connected TV gaining traction, brands should be thinking about how to leverage emerging digital formats rather than relying on the same old playbook.
Finally, remember that Super Bowl advertising is a marathon, not a sprint. Instead of going all-in for one day of expensive exposure, consider how your campaign can build anticipation before the game and keep momentum going afterward. At the end of the day, a well-planned strategy will always outperform a last-minute Hail Mary.
Super Bowl advertising might be a high-stakes game, but with the right data-driven approach, brands can ensure they’re making smart plays that lead to real results—no halftime show required.
This number isn’t adjusted to account for all the new merchants Shopify has onboarded this year, so that volume increase could be driven by brands new to the platform. Therefore this isn’t the most useful benchmark.
Salesforce reports 5% online sales growth year over year across the BFCM season, with Friday and Saturday as the strongest days. Cyber Monday was the weakest day, with only 3% year over year growth. (Is Cyber Monday dead? I asked my mom if she did any Cyber Monday shopping, and she complained that I don’t call her enough. So the verdict is still out, but performance this year doesn’t look great.)
MasterCard’s Spendingpulse gave an assessment that most experts seem to agree with: ecommerce sales on Black Friday increased 8.5% year over year, while in-store sales only increased 1.1%.
The experts are suggesting most brands would see between a 1% and 8.5% year over year increase in revenue. But is it that simple? Let's look at the numbers from the "Cyber 5", the five days from thanksgiving to Cyber Monday.
“Cyber 5” Growth Benchmarks
We looked at petabytes of ecommerce data and calculated percentile ranks of company performance based on year over year changes in revenue and first-time transactions (one of our favorite acquisition measures.) If your brand saw greater than a 3.7% increase in revenue and a 1.8% increase in first-time transactions across the Cyber 5, congratulations - you are at least in the 50th percentile of ecommerce businesses.
So, by these measures, if you did better than 5% increases in revenue year over year, you beat the worldwide average.
Don’t listen to “build in public” schmucks on X - use this chart to evaluate how you actually did this year, compared to everybody else.
Black Friday Cyber Monday Performance Benchmarks
Here’s charts showing ad performance metrics, comparing this year’s Cyber 5 versus last year’s.
Notable changes:
Facebook’s increasing share of budget is having brutal effects on clickthrough rate, cost per click, return on ad spend and customer acquisition costs. Performance marketers trust Facebook for conversions. Under intense pressure in a low-consumer-confidence environment, it’s no surprise budgets are consolidating to a trusted source of conversions.
What is surprising is the stunning almost 20% drop in clickthroughs. Although conversions technically increased, double-digit increases in CAC and double-digit reductions in ROAS probably nullify any conversion gains we saw on Facebook this year. No wonder TikTok is fighting for improved conversion tracking - we’re reaching critical Facebook mass.
TikTok budgets pulled back by 13% year over year. Although this is still one of our favorite growth platforms, the metrics don’t look good. 10% CPM increases, 12% CPCs, -12% conversion rate.
While this looks bad, I place blame on the advertisers themselves. Many still have no damn clue how to make a good TikTok ad, and these people are pulling down the averages with mountains of wasted spend.
Return on ad spend is down everywhere. This is just how it is. These ad platforms demand increasing profits from their advertising businesses. The profit motive drives these platforms to continually push up the prices of their placements. We will never see cheaper advertising than what we are experiencing at this moment. These data support that efficiency in your ad spend, via smart attribution and omnichannel experimentation, is the only way to win.
Industry performance during the Cyber 5.
Now the good stuff. Here’s two charts showing year over year changes, day by day, during the Cyber 5, broken down by industry, featuring blended revenue and first time transactions. Read ‘em and weep.
Some highlights:
Home & Garden took a dump. What happened you guys? I have a guess: growth of U.S. home ownership is tanking. No need to buy gardening gloves when you live on a seventh-floor walkup.
Beauty & Personal Care is slaying. These are typically the smartest Cyber 5 brands in the game. I bet Jones Road Beauty outperformed this by a significant amount. By these measures, most beauty brands using Northbeam likely ended up in the 75th percentile and above for growth.
It’s a good year for self improvement. Interestingly, both Health & Wellness and Sporting Goods & Fitness industries saw better first time transaction increases than they did blended revenue growth year over year. Is this driven by reduced prices? Lower AOV products? The new year will tell.
What about our white paper predictions on the Cyber 5?
Prediction: regardless of economic indicators, consumers are anxious about money and are likely to spend less than usual.
Result: false. Sure, we did spend more as a nation, but there’s some sketchy indicators. “Buy now, pay later” is up 47% year over year, and Cyber Monday is apparently irrelevant. We’re comfortable saying this weren’t as good as they could be.
Prediction: This will be the most fiscally expensive, creatively challenging, and competitively crowded performance advertising market we’ve ever seen.
Result: 100% true. Just look at those metrics above. Ads are more expensive or less efficient by double digits on like every channel.
Prediction: Cyber 5 sales will kick off the first week in November but consumer behavior won’t spike until around the 20th of November.
Result: true. I’ll do you one better: the data suggests that conversion rates spike on both Fridays in November prior to the holiday. Both 2022 and 2023 charts show as much. Plus, the upwards growth in conversion rate - our proxy for consumer buying interest - began to rise at the same pace after the 6th of November. This gives some weight to the “run sales early” argument, but the volume still pales in comparison to Black Friday itself.
Verdict: we were pretty damn close on most of our assessments. Next time we’ll be even more niche and focused in our predictions. I’m feeling lucky.
The Breakdown:
Here’s what you should know:
Year over year growth was not equal across all industries. The widely shared “8.5%” year over year growth number you see reported everywhere is an aggregate of many industries. Our reporting shows that growth is uneven - don’t measure your performance against numbers that aren’t relevant to your industry.
You should be studying what beauty and personal care brands did on Black Friday to attract repeat purchasers. Look at the year over year revenue growth numbers in the charts above, they’re killing it. These strategies often include smart early emailing campaigns and gift with purchase. Bonus data: Beauty and Personal Care saw their year over year blended revenue increase 39.7% in October. They’re doing something right.
Cyber Monday is losing power, so stop planning around it. Even looking at brands who did well during the Cyber 5, several of them saw their lowest year over year growth on Monday. As ALL shopping begins to pivot online, a day dedicated to it seems redundant. Also, it’s easier to apply discount codes and specific offers to online orders.
Thanksgiving is a decent day for sales, actually. We’re always so focused on Friday, but in several industries, Thanksgiving was the best day for sales. Maybe all your customers weren’t in food comas after all. Make Thanksgiving a part of your Cyber 5 media buy.
Black Friday shopping sucks and people care less about it - can you create excitement again. When was the last time you were legitimately excited to do holiday shopping? Long gone are the fistfights over Furbies we used to see in the early aughts. But are we right to point the finger at consumers? Black Friday discounts used to be steep enough to inspire violence. These days marketers are expecting 200% year over year gains on a 15% discount offered on a product bundle that’s pointless for customers but convenient for the 3PL. We’ve lost our Christmas spirit.
Next year’s Cyber 5 will be even worse. We’ll be in the immediate aftermath of the most consequential American election of a generation and the stifling ad spending that comes with it. Consumer confidence is set to drop, not improve. Not to mention CPMs continue to rise in price. There’s no relief coming, so don’t plan for it. Plan instead to be smart and efficient now.
Final comments
This isn’t the Cyber 5 we deserved, but it’s the one we got. Performance was divided across industry and creative lines. Brands who use proper attribution outperformed everybody else. Creative remains king - the brands with quality, authentic ads won while the “60% off!!!” screamers yelled into a void. This Cyber 5 defined what our algorithm-driven future will look like, so we hope you were paying attention.
Celebrity Chef Gordon Ramsay swears by HexClad for a reason.
This patented cookware is a hybrid of your favorite kitchen staples, combining the superpowers of stainless steel, cast iron, and nonstick into convenient and high-power cookware.
Founded in 2013, HexClad quickly grew its following with celebrities like Halle Berry, Oprah Winfrey, and Cameron Diaz singing its praises. In 2021, Gordon Ramsay invested in HexClad and became its lead global ambassador and spokesperson, leading to a record-breaking year for the company.
But maintaining meteoric growth year-over-year isn’t an easy task, which makes the subsequent years even more of a feat for the HexClad marketing team.
We sat down with Cameron Bush, Director of Growth Marketing at Hexclad to learn how Northbeam helps their marketing team not only get efficient but stay efficient.
Looking for a partner, not a provider
HexClad’s marketing team tried a lot of tools over the years to get the information they needed.
“It was a patchwork solution of Google Analytics, in-platform metrics, and static sheets that the marketing team was updating,” Cameron said. “And then we did trials with different marketing intelligence software; I often joke that most MTAs are just re-papered Google Analytics,” Cameron said.
Adding Northbeam to HexClad’s tech stack wasn’t just putting a new SaaS tool in the portfolio — it was a paradigm shift for the team.
“Switching to Northbeam was a pivotal moment in our switch to focusing on the top of the funnel because we finally had the confidence that we were able to measure it,” Cameron said. “The language of the team really matched the language we speak as far as what growth looks like, and we knew the data science rigor was there.”
Cameron found that finding the right MTA tool, as opposed to the heavy lift of building one yourself, is crucial to doing your best work. Having a strong partner in Northbeam is what sets a team like HexClad apart from the competition.
“Some vendors know a lot about what they know, whether that's math or data science or whatever it is, but they don’t know how to turn it into marketing a product and speak our language in that way. And that was something that Northbeam cracked the code on early on," Cameron said. "We’ve tried all kinds of agencies, vendors, and softwares and Northbeam were the ones that really knew what we were doing as a growth marketing operation.”
The predictive modeling of Northbeam’s MMM+ tool is mirrored by the tactics of the Data Science team. Their deep well of expertise and hands-on approach provides additional insight into precisely how to leverage your data into more efficient spending, whether you know what to look for or not.
“Week to week, when I hop on a call with the team, they always have a new sheet that they’re walking me through and I think: I didn’t even know how to ask you for this, but you’ve already created it,” Cameron said.
“That kind of proactivity has been so helpful… Northbeam has been an integral building partner for the entire HexClad operation.”
Maturity brings a much harder battle
After one year of 50% growth and another of 150% growth, HexClad remains bullish but realistic about the road ahead.
“Once we hit maturity and all the big changes we’ve made over the last few years panned out, we had to change gears and iterate and it became an exponential process of improvement,” Cameron said. “Once you hit that steady state it’s a much, much harder battle.”
“MMM has been a huge jumping off point. I wouldn’t know where to begin each month to decide what we spend on which days and why, and then set benchmarks for each of those in MTA. It’s all a huge puzzle that we’re putting together with a very lean team.”
“This is the year where a lot of those inklings and decisions and difficult marketing tests that have been pushed down the road are all coming to a head, and Northbeam has been a partner in that," Cameron said. "Trying to decide where to spend that next dollar is always our daily battle, and Northbeam is the only place we go to make that decision.”
Having a reliable system in place that accurately measures and predicts your spending is crucial when scaling year-over-year. Whether your goals are to maintain ambitious growth metrics like HexClad, or even when ramping your efforts down.
Cameron agrees: “We’ve tested hundreds and hundreds of pieces of creative over the last six months… and Northbeam has not steered us wrong yet.”
The metrics that make it happen
Let’s get into the weeds: which metrics does HexClad’s marketing team check daily?
“My obsessive metric is going to be ROAS, of course, but I love seeing first time versus returning customer ROAS. Not a lot of platforms give us that kind of visibility. We set a lot of our acquisition budgeting based on our first time customer orders, first time order value, and first time CAC, and then we can subtract the returning customer ROAS piece and credit retention for doing that,” Cameron said.
“That increase in returning customers year-over-year was directly measurable for us. Our email opt-in rates actually fell, but the conversion rates on those people that opted in increased massively because we were giving offers for second-time orders.”
“That’s something we’re looking to change now: we’re going to go back towards acquisition offers as well. Those flows, they just convert so well,” Cameron said.
“We watch our data rigorously every single day and we do huge creative testing exercises at the top of the funnel. A big part of measuring that is MMM. We wouldn’t have faithfully put 70% of our budget into Meta if we didn’t have some serious data to support that.”
“Once it proved itself week in and week out we were able to confidently scale and make projections that we genuinely believed in," Cameron said.
"And I think that’s not something that most growing direct-to-consumer businesses, particularly at this scale, are able to accomplish. Because it’s terrifying, you know, spending millions in a week.”
It’s unwise to continually increase your ad spend over time when you cannot effectively measure your return on investment. Northbeam provides the benchmarks you need to know if spending money is worth it; especially when your growth is at 150% and your ad spend is in the millions.
Like Cameron said: “you can’t spend those millions if you don’t absolutely know that you’re going to be able to pay for them at the end of the month.”
HexClad scaled their ad spend by 83% in 2022 and 213% in 2023, improving their MER both times.
“We’ve done a lot of work with the Northbeam team to get us to this place, and it hasn’t failed yet, so we’re going to keep doing it.”
In an industry that is constantly evolving with new tools, legal privacy crackdowns, and SaaS snake oil salesmen coming out of the woodwork, it’s hard to know which tools will actually provide a significant value add.
“Moving to MMM+ is going to be massive for how we operate every day… all the product development that is going on to show us things like the half-life of a dollar is some of the coolest work I’ve ever seen. I didn’t know it could be done,” Cameron said.
“This year we’re looking for 50-60% growth; we’re taking it easy,” Cameron said. “We’re looking at both Shopify and Amazon sales because that spillover is massive; if you chart paid media spend against Amazon revenue, it really grows when we start pushing the top of the funnel. And the majority of our revenue that’s coming out of Amazon is coming from direct search.”
People know HexClad — their addressable market has been, well, addressed. This is a position that any brand would love to be in.
“We’re in this new phase of realizing that we have a top-of-funnel beast and a ton of awareness that we’re really grateful to have because I don’t think a lot of brands are able to get to that well-measured top-of-funnel scale that actually ends up supporting the business,” Cameron said.
“We’ve been very, very lucky to have been profitable for the last three or four years, and to be able to use that profit to continue growing has been just incredible. I’ve never seen anything like it," Cameron said.
“We love being on the cutting edge of our industry, but also on the cutting edge with the software we use. Northbeam is helping us move into the direction we need to be going; it’s a marketing best practice bellwether.”
“If you would have asked me two years ago, I would’ve said the growth we’ve seen wouldn’t be possible… but it is, and it requires a really awesome partner. We’re grateful to the Northbeam team for that.”
Have you or a loved one recently been inundated with messages about incrementality? Can’t figure out if this is the latest fad or a legitimate evolution in analytics? In today’s fast-paced and data-driven marketing landscape, staying ahead of the curve is essential for success. In a sea of buzzwords and trends, incrementality has emerged as a contender with significant implications for marketing strategy and performance measurement. In this article, we’ll cover what exactly this purported “silver bullet” is, and what marketers need to know.
Section 1: What is incrementality?
Incrementality refers to the measure of the additional (or incremental) lift that a marketing initiative provides compared to baseline expectations. In other words, incrementality seeks to answer the question of “what additional value did my marketing activities contribute beyond what would have occurred naturally?”
Incrementality helps answer this question by comparing the performance of those who were exposed to marketing campaigns (treatment group) with a similar cohort that did not see those same campaigns (control group). By measuring the difference in outcomes between the two, we can determine the additional value generated by the campaign.
You’ll notice this is essentially describing the scientific method; like scientific experimentation, incrementality analysis begins with hypothesis testing, where marketers conduct randomized experiments to isolate the incremental impact of campaigns. This emphasis on empirical evidence is why incrementality is so revered: of all the popular analytical methodologies employed by marketers, incrementality is the only one that establishes a causal relationship as opposed to simply explaining the correlation between the independent and dependent variables.
These experiments can take many forms, but they all generally involve some way of randomly selecting users into a treatment or control group. A geographic experiment, for example, would segment the country into similar pockets and then randomly assign them to a treatment and control group. This is also called a holdout test since we’re withholding marketing exposure from a portion of the target audience.
Section 2: How is incrementality used?
Since we can rely on incrementality to establish a causal relationship, it’s often a great tool to validate results or justify marketing spend. Unlike other common marketing analytics tools that mostly conduct correlational analysis, this gives our data more credibility. The next time your finance department asks why they should keep spending dollars on a marketing initiative, you can simply refer to the results of your latest incrementality experiments with confidence. Without the marketing spend that you’re counting on, the company would be leaving money on the table. This really helps change the message from looking at marketing as an expense, and more as an investment.
Incrementality is commonly used for budget allocation purposes because we can run various types of randomized experiments on our top channels and campaigns to test if those marketing dollars are truly driving new revenue instead of cannibalizing existing sales. Not sure if Facebook Display is actually driving positive results for your business? Design an incrementality test and find out. Repeat the process for your top channels and let those results help you decide how to divvy up your marketing spend. Tactically, incrementality is also well-suited for channels that your team doesn’t have much experience with. Let’s say your team wants to spend more on a new channel, but you’re not sure if additional spend will generate the necessary ROI. If we run a pilot and conduct incrementality analysis on the results, we can extrapolate and forecast what a scale up would look like.
Section 3: What are some challenges of incrementality?
While incrementality testing is a powerful tool for measuring the true impact of marketing efforts, there are some challenges and limitations you should be aware of.
Since incrementality involves experiments, a big challenge is ensuring the sample size is sufficiently large enough so that our results are statistically significant. Small sample sizes can lead to low statistical power, making it difficult to draw meaningful conclusions from the results.
It’s impossible to remove all of the “noise” surrounding a marketing campaign so an omitted or hidden variable could dramatically skew your results. Did you hire more sales reps while the campaign was running? Did you increase more communication with the system so more sales occurred? When you randomly assigned treatment and control groups, were there hidden mechanisms that prevented true randomization such as inherent biases in the selection process? Remember that incrementality attempts to show causation, which means your chances of getting a bad read increase if there are important omitted variables. Incrementality also struggles to account for spillover effects (marketing efforts often impact individuals who were not directly exposed to the campaign, through word of mouth and other means) which are particularly important in digital marketing where these cross-channel interactions are more common.
Incrementality is not a low maintenance, set it and forget it, type of tool. To get the most out of this technique requires a skilled marketing team that has a larger strategy and framework (in the form of a learning agenda or testing plan) that will guide what to test, when to test it, and most importantly, how to interpret those results and make actionable decisions off those insights.
Note that incrementality experiments generally run for weeks not days, during which we cannot make changes to our campaigns because it will impact results. That means no swapping out of creative, A/B testing, or introducing any other variables while experiments are running. This is not ideal for larger channels because it requires us to disrupt our business as usual operations to accommodate a statistical technique.
Section 4: Incrementality vs. other popular analytics methods
Before the digital advertising schism moved us away from traditional advertising channels like linear TV, MMM was the primary form of media measurement because it doesn’t rely on granular data. MMM excels at capturing older channels and the longer-term impact on revenue by leveraging historical data (both media and macroeconomic data) to predict future sales based on past behavior. However, MMM is very expensive to spin up and often takes months to deploy with a reporting lag after each quarter to update the model, so other methodologies sprang up to make up for these deficiencies.
MTA came next with the rise of digital media, along with detailed user tracking which gave marketers more granularity in terms of insights. By leveraging this richer user data to track customer journeys, MTA was far better adapted for the complex and cross-channel landscape and is the most bottoms-up approach as it collects data at the individual level and then aggregates this to create a model. However, with the rise of consumer privacy regulations, MTA may become less effective in the future if user-level tracking goes completely away (still looks to be sometime in the future given Google recently delayed cookie deprecation again) so marketers began to turn to incrementality experimentation.
You’ll notice that none of these methodologies are silver bullets; each have their own distinct advantages and disadvantages. The truth is that the best marketing teams leverage some combination (or all) of these 3 to guide their decision making because this can make up for individual weaknesses. Incrementality is simply the latest in a long line of measurement methods that marketers are using to try and gain an edge. Although the promise of finding causal relationships is tantalizing, the challenge of setting up experiments correctly keeps many companies from effectively using incrementality. If your audiences somehow overlap, your segment sizes are off, or a number of other possibilities somehow skew your results, you’re using incorrect data to make very impactful decisions (potentially for the worse).
If you want to learn more about the MTA, MMM and how they compare to incrementality, we wrote this awesome deep dive that goes into much more depth on what MTA and MMM are, and also provides a detailed comparison of the three. Click here to check that out.
Think the holiday season and peak shopping time is over after Christmas? Think again. You know we're all about arbitrage here at Northbeam.
If you’re a savvy advertiser, you’re probably already familiar with Q5 (the “hidden” quarter) and have been actively squeezing more performance "juice" out of the proverbial holiday promotional season lemon. In fact, both Meta and TikTok are vocal about encouraging more brands to increase spend in Q5 to capitalize on opportunities.
The good news is, even if you’ve never heard of Q5, it’s not too late to take advantage of this unique selling time. In this guide, we’ll give you our take on Q5, why you should care about it, and how you can make the best use of Q5 with your campaigns.
What is Q5?
Ask a couple of marketers what Q5 is and you’ll likely get slightly different responses. At Northbeam, our definition of Q5 is the period from late December to early-to-mid January. The fifth quarter is that hazy period between Christmas and the proper start of the New Year where the days blend together as people relax and unwind with family and loved ones. Most brands are replenishing BFCM investments and reducing campaign budgets across the board with (at best) only top performers active.
This is when most marketers take their vacations after the absolute gauntlet of Black Friday, Cyber Monday, and Christmas sales. While the competition is taking a break before Q1, this relatively quiet time in the year can help propel you past the competition if you’re well prepared.
Why is Q5 important?
It can seem counterintuitive to keep spending during “down” time, but there are several good reasons why brands should consider Q5 as part of the holiday season:
Since most marketers will be slowing down after Christmas, there’s naturally going to be less competition in ad auctions.
This not only means fewer promotions and other advertisers to compete against, but also considerably lower ad costs vs. peak holiday CPMs. Adweek estimates that Meta CPMs were 28% lower during Q5 YoY, with ad fees in the last week of December at least 12% cheaper than previous weeks.
This is a great way to get the most bang for your buck, or get an even larger share of voice with the same amount of spend.
Engagement on social media and digital devices remains high as people relax and spend time at home during the holidays.
Not only are people using their devices to disconnect more, but new devices that were gifted are also coming online. Usage peaks around Christmas and stays elevated all the way through early January for these reasons.
According to Snapchat, although engagement is highest on Christmas day, a surge of app downloads follow. 81% of TikTok users expect to spend the same amount of time or more on the app in Q5, and during the week after Christmas, TikTok views actually increased +24% vs the Q4 average. This means that even though ad costs are lower, customer engagement is in the same ballpark as more expensive weeks.
Shopping intent also remains high after peak holiday sales are over.
Survey data shows that 92% of customers intend to keep shopping into Q5 as people exchange and return unwanted gifts for something they actually want. According to the National Retail Foundation, 70% of customers this year expect to shop in the week after Christmas, driven by continuing sales.
Gift cards are often used during this time to take advantage of ongoing sales, which explains why shopping behavior during Q5 shifts from gifting to more personal buying (which is better aligned with most brand messaging) as we approach New Years resolutions season.
In summary, not only is there less noise in the market from other ads and sales, but you’ll also enjoy high customer engagement and shopping intent at a healthy discount. Not a bad time to think about efficient customer acquisition.
Take advantage of Q5 with these strategies
Because of the dynamic of lower CPMs with high engagement, there’s an opportunity to get a discount on ads and unlock some incremental revenue at the same time. Let’s discuss a few scenarios that are perfect for Q5.
People begin shifting from buying gifts to purchasing for themselves after Christmas and as we approach New Years.
We start thinking about resolutions and unsurprisingly “New Year New Me” brands generally see an uptick in conversions and performance at this time. If your brand is in the health & wellness, fitness, financial management, or other similar spaces tied to New Years Resolutions, you’re missing out by not taking advantage of cheaper ad fees at a time when people are actively looking for your products.
We’re advising our "New Year New Me" clients to double down and aggressively pursue revenue goals this Q5; you should, too.
Seasonally relevant and consumable stock-up brands also do well during this time.
Winter clothing & accessories, ski & snowboard, beauty & skincare are just some examples of verticals that should seriously consider ramping up or at least extending spend through early-to-mid January. One of our best clients is a beauty brand powerhouse that has consistently made smart choices around their Q5 media mix and budget allocation.
In 2022, they actually spent 6% more in Q5 than the prior month, but efficiency metrics like ROAS (+2%) and CPA (-5%) were healthy due to high device usage and shopping intent. Best of all, revenue from Q5 was +8% higher vs. the traditional Q4 period which included Black Friday/Cyber Monday.
Q5 is a great time to run awareness plays and retargeting campaigns because A/B testing will cost even less than usual.
Get a leg up on Q1 by using this period to start on your learning agenda to test creative, audiences, channels, copy, and so on. Ideally you’d want to have a large diversity of creative running not only to narrow down on themes that work, but also to start building up your pipeline for the rest of the year.
Brands can use this time to begin filling the top of their funnels for 2024 while their competition is asleep at the wheel, especially smaller brands who have the rare opportunity to outbid larger advertisers (those brands will largely have exhausted spend by this point and are in planning mode for next year). Be bold when others are fearful.
Finally, Q5 is perfect for liquidating excess inventory or older SKUs.
We’d advise switching up the offer from your previous holiday promos and launching new creative to avoid customer fatigue. Make it clear this is a sale specifically for Q5 that will only run for a limited time to generate a sense of urgency.
How leading brands use Northbeam to tackle Q5
We surveyed our power customers on the ways they use Northbeam in Q5. Here are a few of our favorites:
A big advantage of having infinite lookback windows is that customers acquired in Q5 will stay in your funnel even if their purchase intent comes later.
Unlike most other attribution solutions that have a limited lookback window (usually 7 days), Northbeam allows you to target all of the people in your pipeline regardless of when conversion actually happens. For example, let’s say your client is a swimsuit and summer essentials brand who traditionally has ignored Q5 in their media plan.
Using Northbeam, cohorts recruited or acquired during Q5 at a lower cost thanks to decreased CPMs can be integrated into the funnel once the high-selling season starts in spring and summer. With a shorter attribution window, those Q5 customers would get lost in the shuffle even if they were likely to convert down the line. Make sure you don’t miss out on revenue with shaky attribution modeling.
Using the Customer LTV tab, you can track the average value of customers acquired during Q5 vs BFCM or any other time in the year. Let your own data tell you whether Q5 is a good time to increase spend or not.
Although Q5 offers lower CPMs, this can easily be offset with worse efficiency or conversion rates. To carefully monitor and manage your ROI, you need a single source of truth that you can rely on to make budget allocation decisions.
What’s the good of doubling down on a campaign if your data is faulty and you weren’t investing in a winner to begin with?
Top brands including The Ridge, Hexclad, and Jones Road Beauty trust Northbeam’s expertise in attribution and customer journey analysis to capitalize and get even more value out of Q5 because they trust the accuracy and integrity of the data and modeling.
Bonus: Northbeam is a retention tool and value add for marketing agencies
With platform-reported data and GA4, Digital Marketing Agency partners often view Q5 as the season of churn as clients invest a majority of their available budget into BFCM and the holiday season before going dark. Northbeam can help agencies with retention efforts by building on the momentum from Q5 and applying learnings into their larger 2024 strategy.
We asked a few of our best partners on how they’re advising clients on Q5:
Sol8: “Take advantage of the low CPMs on the Google TV network, optimizing for reach targeted to TB screens. At a time where there is a large amount of time off for families, you can leverage all the Google Ads audience targeting to multiply your viewership to multiple people in a household. The best part? It’s 81% cheaper than the CPMs on Hulu.”
Blaze Digital: “Q5 is the time to push as ad costs plummet after the Christmas holiday. Really effective promotions tend to be 'end of year clear out sale.' This is also a big time to push the use of gift cards.”
Embrace E-Commerce: "From an Amazon marketing perspective, leverage the Q5 period as a strategic opportunity that extends beyond low CPMs and CPCs. Its about comprehensive preparation and seizing a competitive advantage over other brands."
So should your brand or clients keep some cash on the sidelines for Q5 this year? If any of the above strategies or tactics resonate with you, reach out to us and we'll give you our take on whether or not it makes sense for your marketing team.
The marketing measurement playbook is being rewritten.
As third-party cookies vanish and privacy regulations tighten, the traditional deterministic tracking methods that once gave marketers clarity are becoming less reliable.
Identifiers are disappearing, cross-device journeys are harder to stitch together, and walled gardens are closing off visibility.
That doesn’t mean measurement is dead: it means marketers need new tools.
Enter probabilistic attribution vs deterministic attribution: two complementary approaches that, when used together, provide a clear path forward in the cookieless future of marketing measurement.
Deterministic models give precision where user data is available, while probabilistic models fill the inevitable gaps with statistically reliable insights.
Blending the two creates a hybrid attribution strategy that balances accuracy with coverage, helping brands continue to measure performance and allocate budgets confidently.
Defining Deterministic and Probabilistic Attribution
Both deterministic and probabilistic attribution models aim to answer the same question: which marketing touchpoints deserve credit for a conversion?
But they approach it in very different ways:
Deterministic Attribution
Deterministic attribution relies on exact matches between a user’s actions and their identity, often through login credentials, user IDs, or hashed email addresses.
It gives you pixel-level precision: if a logged-in customer clicks an ad and makes a purchase, you can confidently connect the dots.
Strengths:
Highly accurate, especially when backed by robust first-party data like CRM integrations or loyalty programs.
Limitations:
As third-party cookies vanish and privacy regulations tighten, deterministic tracking loses visibility outside of walled gardens and logged-in environments.
Data gaps widen when identifiers aren’t available.
Probabilistic Attribution
Probabilistic attribution takes a different route.
Instead of requiring a perfect identifier, it uses statistical modeling and machine learning to infer the most likely conversion path.
Signals such as device type, location, time of day, and referrer can be combined to estimate user journeys with a high degree of confidence.
Strengths:
Provides coverage when deterministic data isn’t available, bridges fragmented journeys across devices and channels.
Adapts well to privacy-first environments.
Limitations:
While sophisticated, it’s not a 100% exact match.
Results are modeled probabilities, not certainties.
In short, deterministic attribution models provide precision, while probabilistic attribution provides coverage.
Why Both Deterministic and Probabilistic Approaches Are Vital Today
As third-party cookies disappear and privacy regulations tighten, marketers can no longer rely on a single attribution method.
Deterministic tracking, once the gold standard, is now limited by shrinking visibility outside of logged-in environments and walled gardens like Facebook or TikTok.
Even with strong first-party data, deterministic models can only capture part of the story, leaving blind spots in the customer journey.
This is where probabilistic attribution becomes essential.
By modeling likely paths with aggregate-level signals, probabilistic methods fill in the missing context:
Estimating conversions where IDs aren’t available.
Reconstructing fragmented, cross-device journeys.
Extending visibility into channels where deterministic tracking breaks down.
Combine probabilistic and deterministic approaches to get the best of both worlds:
Deterministic provides precision where identifiers exist.
Probabilistic provides coverage where they don’t.
The result is a more complete, privacy-compliant measurement framework.
Instead of choosing one approach, forward-thinking marketers blend the two: using deterministic attribution models as the anchor for accuracy, and probabilistic attribution to ensure no piece of the funnel goes unseen.
When to Use Deterministic vs Probabilistic Approaches
Each model shines in different contexts, and together, they create a stronger foundation for marketing measurement.
When to Use a Deterministic Approach
Deterministic models are most effective when you have reliable identifiers and consented data.
Common scenarios include:
Logged-in users on apps or websites (e.g., loyalty members, subscribers).
CRM or CDP integrations that tie ad interactions directly to customer profiles.
Environments where cookies or hashed identifiers remain accessible.
In these cases, deterministic attribution models ensure high-confidence matches between ad exposure and conversion, making it ideal for measuring retention campaigns, subscription funnels, or personalized remarketing.
When to Use a Probabilistic Approach
Probabilistic models excel when deterministic tracking breaks down. They’re particularly valuable in situations such as:
Tracking disruptions: Cookie loss, ad blockers, or iOS privacy updates.
Cross-device journeys: Customers moving between mobile, desktop, and connected TV.
Regulatory environments: Where consent is limited or explicit IDs can’t be stored.
Here, probabilistic modeling allows marketers to maintain visibility into conversion paths without overstepping privacy boundaries.
Why Blending is the Smartest Approach
The strongest attribution strategies use deterministic data as the anchor for accuracy, then layer probabilistic models on top to fill in the gaps.
This hybrid mindset ensures both confidence and coverage, creating a resilient measurement system in a world of cookieless attribution.
Northbeam’s Hybrid Attribution Capabilities
While most attribution frameworks lean heavily on one method or the other, Northbeam is designed from the ground up to integrate both deterministic and probabilistic attribution.
This hybrid approach gives marketers precision where identifiers are available and reliable insights where data would otherwise disappear.
Clicks + Deterministic Views (C+DV)
One standout capability is Northbeam’s CDV model, which blends click data with view-through signals.
Instead of relying solely on last-click or impression-based rules, C+DV merges these inputs into a unified dataset that can confirm when an ad truly influenced a conversion.
For example, on platforms like TikTok and Snapchat, where many conversions happen after a view rather than a click, C+DV enables marketers to measure real view-through impact without inflating results.
This closes a major gap left by traditional deterministic tracking, which often undercounts conversions when users don’t interact directly with an ad.
Probabilistic Modeling for the Cookieless Attribution Era
Alongside deterministic anchors like CDV, Northbeam applies probabilistic attribution models to infer the likely paths of anonymous or cross-device users.
By using aggregate-level signals, these models expand coverage while maintaining compliance with privacy regulations.
The Hybrid Advantage
By combining deterministic insights with the advantages of probabilistic attribution, Northbeam solves the “real-funnel tracking” challenge:
Coverage: Probabilistic models ensure visibility across fragmented, cookieless attribution environments.
Confidence: The hybrid view enables marketers to make high-stakes budget decisions with both accuracy and breadth.
This dual-layered system means that even as cookies vanish, Northbeam customers retain a clear, data-backed picture of performance from impression to conversion.
Implementation Guide
Understanding the theory is one thing; implementing it in your day-to-day measurement strategy is another.
Here’s a step-by-step guide for marketers who want future-proof attribution strategies for privacy-first environments:
1. Audit Your Current Tracking Setup
Begin by identifying gaps in your existing measurement framework.
Where are cookies or platform-level limitations creating blind spots? Which channels are you over- or under-counting due to incomplete attribution?
2. Strengthen Your First-Party Data Capture
Prioritize collecting and organizing consented, first-party data wherever possible.
Tactics include encouraging account creation, building loyalty programs, and syncing CRM/CDP systems.
This data forms the backbone of deterministic attribution.
3. Anchor Reporting with Deterministic Models
Use deterministic attribution wherever you have reliable identifiers, such as logged-in users or CRM-connected campaigns.
This provides a high-confidence foundation for reporting.
4. Layer in Probabilistic Models to Fill the Gaps
For channels or journeys where identifiers aren’t available, supplement with probabilistic attribution models.
This ensures you don’t lose visibility into anonymous, cross-device, or walled garden conversions.
5. Continuously Validate and Optimize
Hybrid attribution isn’t a “set it and forget it” process.
Regularly compare deterministic and probabilistic outcomes, run lift tests, and refine models as your data environment evolves.
Following these steps helps you build a measurement system that is both resilient and adaptable, balancing accuracy with the flexibility to handle data disruptions.
The Cookieless Future of Marketing Measurement
As cookies disappear and privacy restrictions intensify, no single attribution method can provide the complete picture.
Deterministic attribution offers the precision marketers need when identifiers are available, while probabilistic attribution models ensure coverage where deterministic models fall short.
Relying on just one leaves gaps; blending the two creates a measurement strategy that is both accurate and resilient.
The result is clear visibility into performance, confident budget allocation, and the ability to keep growing with bullet-proof attribution strategies in privacy-first environments.
Marketers who embrace this hybrid approach today will be best positioned to adapt, compete, and win in tomorrow’s cookieless landscape.
I don’t know about you, but every year I resolve to cut sugar out of my diet only to find myself ordering gourmet cookies online by early February (at the latest). This year I’ll have no choice: Google recently confirmed their plan to completely deprecate third-party cookies by the second half of 2024 is on track.
Beginning in Q1, 1% of Chrome users will migrate to the new Privacy Sandbox ecosystem and have third-party cookies disabled. Google says this will give developers and advertisers enough time to learn the new environment and prepare for a cookieless future that will dramatically alter the internet landscape.
So fellow practitioners, what do we make of this? Is this the end of digital marketing as we know it, or the continuation of a trend in privacy concerns that Apple accelerated with the release of iOS14?
And more importantly: how is third-party cookie deprecation going to affect Northbeam data?Short answer: it won't.
What is "Cookie Deprecation?"
If this is the first time you’ve heard about Cookie Deprecation, let’s briefly discuss what cookies are and why they’re important for digital marketing.
Cookies 101: Cookies are small text files that help identify individual users on the web. There are cookies on every website you visit which are then stored on your computer’s web browser. They were first introduced in 1994 to allow people shopping online to store items in a virtual shopping cart; a functionality that we now take for granted as eCommerce marketers.
These are first-party cookies: cookies that belong to the owner of the website to help improve the user experience and collect anaytical information on customer behavior. Since they belong to the owner, these cookies are not at risk of being disabled by Google or any other browser provider.
Third-Party Cookies function the same as a FPC, but belong to someone else who mainly uses the cookie to track activity across the web for online advertising. These tracking (and problematic for privacy) cookies facilitate much of modern marketing by allowing external parties to create user profiles and target ads based on their individual behavior.
So why are they being banned? Shortly after their introduction, advertisers began using them to track user data including interests, demographics, location, often without the consent of everyday consumers. Shady practices such as “zombie” cookies that were difficult to delete eventually led to increased regulation such as the now ubiquitous consent pop-ups.
However, it was the ad tech giants who were the biggest culprits as evidenced by the Cambridge Analytica incident and subsequent repeated revelations of further violations.
In the mid- 2010s, data privacy gained more momentum as popular browsers including Apple’s Safari and Mozilla Firefox started taking these concerns more seriously and curbed the use of third-party cookies before eventually banning them altogether. Google’s move is notable however because 63% of global web traffic flows through Chrome.
Third-party cookies were already unreliable before, but the move to Privacy Sandbox will be their final death knell.
What are the implications?
Although we’ve been preparing for a cookieless future for a while, many marketers still rely on third-party cookies to understand consumer behavior such as purchases, interests, affinities, and browsing behavior. Insider reports that 78% of marketers still use cookies for ad buys, meaning most of us are in for a rude awakening very soon.
Let’s explore the effects of disabling third-party cookies on various platforms and strategies.
User tracking and targeting will be significantly hampered so any campaigns that rely on third-party cookies will see a drop in performance. Certain tactics such as behavioral targeting, retargeting (incl. cross-device tracking), and audience extension will all be dramatically less reliable once Chrome completely disables third-party cookies.
User profiles won’t be as detailed so targeting will lose some personalization, and the ability to build lookalike audiences with third-party data will no longer be possible. Frequency capping will also be affected due to the inability to accurately determine how many times an ad has been served to a specific profile. This only emphasizes the importance of leaning into a first-party data strategy as quickly as possible.
The big ad tech platforms rely on a mix of first and third-party cookies to target and serve ads. Post iOS14.5, marketers have already seen a drop in the efficacy of campaigns and precision in targeting, leading to fewer dollars being spent in social and digital. Facebook estimated a $10B loss in revenue in 2022 from Apple’s privacy changes; imagine how much more they stand to lose once Google phases out third-party cookies altogether.
Programmatic advertising may also see a drop in performance due to auctions relying heavily on targeting data. Expect these platforms to respond with more first-party products that rely on walled garden data (probably with a price premium) to keep your ad dollars in their ecosystem and circumvent some effects of cookie deprecation.
Measurement: Generally, multi-touch attribution is currently handled by a third-party cookie on a brand site associating a conversion event with an impression from third-party cookie on a publisher site. Without third-party cookies, marketers will need some sort of other identifier to attribute revenue to campaigns.
However there are already plenty of tools such as cohort analysis, segment analysis and media mix modeling (MMM) that can help you better understand performance without needing third-party cookies. In other environments such as CTV, cookies have never existed yet several analytical tools exist for attribution.
Probabilistic modeling and AI/ML are already speeding up this transition away from cookies; we think marketers will (as they always have) figure something out. Some even think attribution will significantly improve due to the high levels of bot activity in third-party data: a decline in fraudulent clicks and impressions should boost data integrity in general.
How will cookie deprecation affect Northbeam?
Short answer: it won't. We've run internal holdout tests where we've experimented with removing cookies from our modeling and 98% of our data was unaffected.
If you're using Northbeam for your ad attribution, cookie deprecation will not affect your reporting.
In our (humble) opinion, a cookieless future is another reason for marketers to move from targeting-led strategies to creative-led strategies. We’ve been preparing for the cancellation of third-party cookies since the beginning of Northbeam: many of our strategies post iOS14.5 were created to emphasize first-party data because we knew eventually that would be the only data we could rely on.
"Years ago, when we were starting Northbeam, we knew this was coming. Google's been telling us for years they were going to do away with cookies," said Austin Harrison, CEO and Co-founder of Northbeam. "So preparing for that future has been our mission. It's not just about cookies, it's about advertising more effectively in a more privacy-minded era. That's what we're about."
First and foremost, we don’t rely exclusively on third-party cookies in our attribution modeling.The core of Northbeam's MTA tracking solution uses a first-party cookie from your website, which is exempt from Google's third-party cookie deprecation plans.
We leverage only first-party data to resolve customer identities and expect to see minimal if any effect for our customers and clients because Chrome’s upcoming changes will not affect our pixel’s functionality. The first-party data we create has so many touchpoints that third-party cookies are just a tiny drop in the bucket of our overall attribution models.
Our collection and user stitching heuristics are designed for a cookieless future. It’s the reason why our customers (such as The Ridge) trusted us for their attribution needs: Northbeam is a future-proof solution in your marketing stack that will evolve to handle the changing environment.
That’s why we introduced MMM+ and are continuing to push out new features and updates. The Google announcement is mostly significant because of the scale of web traffic handled rather than the actual changes being implemented.
Although Northbeam brands won’t be affected by Google’s cookie ban, will yours? Reach out to us, we’re always happy to give you our take.
Meta’s sensitivity category restrictions can disrupt how marketers track and optimize their campaigns.
By assigning certain industries to “sensitive” categories, Meta now limits access to lower-funnel events and restricts how much conversion data can be shared. That means fewer optimization levers, less visibility into what’s driving sales, and more risk of wasted ad spend.
In this guide, we’ll break down what the Meta sensitivity category restrictions are, which industries are most affected, and why these changes matter for ecommerce.
More importantly, we’ll share practical strategies for adapting to these rules, and show how tools like Northbeam can help you maintain clarity and performance even when platform signals are cut off.
What Are the Meta Sensitivity Category Restrictions?
Meta has a sensitivity category system that classifies certain industries as “restricted.”
When a brand falls into one of these categories, Meta limits how much tracking and optimization data it can access through tools like the Pixel and Conversions API (CAPI).
Not all restrictions are the same.
Some categories face full restrictions, which remove the ability to optimize campaigns toward key lower-funnel events like purchases.
Others receive partial restrictions, where you can still run campaigns but may lose access to certain targeting options or event types (for example, optimizing for “Add to Cart” but not for “Purchase”).
In short, these rules change how advertisers in sensitive verticals can track, measure, and optimize performance, forcing them to adapt their strategy and measurement approach.
Which Industries Are Impacted?
Not every advertiser feels the effects of the Meta sensitivity category restrictions in the same way. The restrictions are focused on industries deemed more sensitive due to Meta’s privacy restrictions in ecommerce as well as ethical and regulatory concerns.
Meta’s sensitive categories include:
Health & Wellness: especially products tied to specific conditions or treatments, which raise privacy considerations.
Financial Services: from credit cards to investment platforms, where regulations around targeting are already strict.
Sexual Wellness: products in this space are often flagged as sensitive content.
Gambling and Alcohol: heavily regulated industries where ad targeting is tightly controlled.
In addition, lifestyle brands with adjacent offerings (like supplements, skincare, or certain subscription services) may find themselves flagged under Meta’s sensitive categories even if they don’t directly sell restricted products.
The challenge doesn’t stop there.
Misclassification risk is real: if Meta assigns your brand to a restricted category by mistake, you could lose access to critical optimization events, misreport campaign performance, or waste budget targeting the wrong audience.
This makes it essential for advertisers to regularly monitor their category status and act quickly if misclassified as one of Meta’s sensitive categories.
Why This Matters for Ecommerce
For ecommerce advertisers, the Meta sensitivity category restrictions aren’t just a policy change, they represent a fundamental disruption to performance visibility.
Signal Loss
When Meta restricts lower-funnel events like purchases, advertisers lose the ability to clearly attribute sales or feed back accurate data into Meta’s optimization engine.
That means the algorithm has less context to find high-value customers.
Budget Waste
With less precise targeting and optimization, brands risk spending more to reach less qualified audiences, driving up customer acquisition costs.
Inaccurate Reporting
If a campaign is misclassified, performance data may underrepresent conversions or misstate return on ad spend, making it harder to evaluate channel efficiency.
Bigger Than iOS 14?
Many advertisers compare these Meta ad restrictions in 2025 to Apple’s iOS 14 privacy updates, which cut off large portions of signal from Meta campaigns.
But sensitivity restrictions can hit even harder, removing optimization paths altogether rather than simply limiting attribution windows.
In short, the new rules could have direct bottom-line consequences if brands don’t proactively adjust how they measure and optimize campaigns.
The good news: there are clear steps brands can take to adapt, and even find new opportunities in the process.
How to Adapt Your Strategy
Meta’s sensitivity restrictions don’t have to mean a performance collapse. The key is adjusting your playbook to maintain visibility, efficiency, and control.
Here are five steps ecommerce brands can take right now:
1. Audit & Appeal Category Assignments
Start by checking your account’s classification in Meta Events Manager.
If you’ve been placed into a sensitive category incorrectly, you can submit an appeal with supporting documentation.
Staying on top of this ensures you don’t lose optimization capabilities due to an error in Meta’s system.
2. Adjust Optimization Goals
If full purchase optimization is no longer available, shift to higher-funnel events like ViewContent, Landing Page Views, or Add to Cart.
While these aren’t as close to revenue, they still provide the algorithm with valuable signals to identify interested users and keep campaigns moving forward.
3. Diversify Channels
Avoid over-reliance on Meta by building a more balanced channel mix.
Consider open-web campaigns, search ads, retail media networks, or influencer partnerships.
Spreading spend across multiple platforms not only reduces risk but also helps uncover new sources of incremental growth.
4. Strengthen First-Party Data
Invest in building durable, owned audiences through email, SMS, and CRM systems.
These first-party data assets are immune to platform restrictions and can be used to power both remarketing campaigns and lookalike audiences with greater control.
5. Use Third-Party Attribution & MMM
When platform signals are restricted, third-party solutions like Northbeam provide a unified view of performance.
By combining modeled conversions, cross-channel insights, and methodologies like incrementality testing and media mix modeling (MMM), you can maintain budget efficiency and accurately measure channel lift, even without full Meta data.
Northbeam’s Role in Navigating Restrictions
When platform signals break down, analytics clarity becomes more important than ever. That’s where Northbeam comes in.
Northbeam provides advanced attribution and measurement tools designed to connect fragmented platform data into a unified, accurate view of performance.
Even when Meta limits purchase optimization or pixel events, Northbeam’s modeling capabilities allow brands to track conversions with confidence.
Walled garden advertising has long dominated the digital advertising ecosystem, giving marketers access to vast audiences and powerful targeting capabilities.
But the cracks are starting to show.
As costs rise and transparency shrinks, brands are increasingly frustrated by the data silos in walled gardens that make it difficult to measure performance across platforms.
Limited visibility, higher CPMs, and attribution blind spots are leaving marketers questioning whether the trade-offs are worth it.
In this guide, we’ll cover the walled garden definition, explain why its dominance is shifting, and share strategies for thriving in a future that blends both closed platforms and the open web.
What Is a Walled Garden?
A walled garden in advertising refers to a closed ecosystem where a single platform controls nearly every aspect of the advertising experience: from data access and audience targeting to ad inventory and reporting.
Google, Meta, and Amazon are the most recognizable examples today, each offering vast user bases and tightly integrated ad tools.
The walled garden definition itself comes from the telecom industry of the 1990s, when companies like AOL or early mobile carriers created “closed” environments that kept users inside their own networks.
While the internet eventually opened up, the same model re-emerged in ad tech: large platforms built self-contained systems that give marketers access to powerful audiences but restrict what happens outside their walls.
Put simply, walled gardens in ad tech offer scale and precision, but the trade-off is limited transparency and minimal control for advertisers.
Why Walled Gardens Rose to Power
Walled gardens didn’t dominate digital advertising by accident. They offered a combination of scale, precision, and ease that marketers found hard to resist.
Advantages of a walled garden in advertising include:
Scale: Billions of active users across Google, Meta, and Amazon mean advertisers can reach massive audiences without leaving a single platform.
Advanced targeting: Rich first-party data enables hyper-specific targeting based on demographics, interests, and behaviors.
Integrated tools: From campaign management to creative optimization, walled gardens bundle everything in one place.
User-rich environments: Platforms keep users highly engaged, giving advertisers more opportunities to connect.
For advertisers, these features translated into simplicity and performance.
Instead of juggling multiple platforms or struggling with fragmented data, brands could log into one dashboard, run campaigns, and quickly see results.
The promise of reach plus measurable ROI helped cement walled gardens as the default advertising choice for much of the past decade.
The Challenges of Walled Gardens in Marketing
But for all their benefits, walled gardens come with significant drawbacks that are becoming harder for marketers to ignore.
Limited transparency: Platforms restrict access to granular reporting and limit data export, making it difficult to fully validate performance through walled garden data restrictions.
Attribution gaps: Because data doesn’t leave the ecosystem, advertisers can’t see the complete customer journey across multiple platforms.
Rising costs: As competition intensifies, CPMs climb, and brands have little leverage to negotiate better terms.
Competitive silos: Advertisers end up competing for the same audiences inside the same closed environments, driving up costs further.
The Pros and Cons of Walled Gardens
To better understand why walled gardens are both attractive and problematic, it helps to weigh their benefits against their limitations.
The same features that once made them indispensable — scale, targeting, and simplicity — now come with trade-offs in cost, transparency, and control.
Closed systems may still deliver strong short-term results, but these challenges leave many marketers searching for more open, transparent alternatives to walled gardens.
Moving Beyond Walled Gardens
The dominance of walled gardens is no longer absolute. Across the industry, there’s growing momentum toward a more open and interoperable advertising ecosystem.
Push for Interoperability
As data silos become more frustrating for marketers, the industry is pushing for interoperability: the ability to connect and analyze data across multiple platforms.
By stitching together fragmented insights, advertisers gain a more holistic view of performance and can make smarter budget decisions.
This shift is powered by new analytics tools and cross-platform integrations designed to break down barriers that walled gardens created.
Rise of Retail Media and Programmatic Exchanges
Retail media networks and programmatic open exchanges are emerging as powerful alternatives to walled gardens like Google and Meta.
Retailers like Walmart and Target are monetizing their shopper data, offering highly targeted ad opportunities with built-in purchase intent.
At the same time, programmatic exchanges provide transparency and flexibility, allowing brands to diversify spend beyond the big three platforms.
Regulatory Pressure
Governments and regulators are increasingly focused on consumer privacy, data transparency, and portability.
Frameworks like GDPR and CCPA, along with proposed legislation worldwide, push back against the opacity of walled gardens.
These pressures are forcing ad platforms to adapt, creating openings for more transparent and accountable advertising models.
Evolving Marketer Needs
Modern marketers can no longer rely on a single platform to measure success.
Instead, they need holistic measurement across channels that captures how every touchpoint — from search and social to email and retail media — contributes to the customer journey.
As expectations shift, advertisers are demanding tools that deliver cross-platform visibility and actionable insights.
Taken together, these shifts signal a clear reality: marketers can no longer rely on walled gardens alone.
To succeed in this new landscape, they need to start moving beyond walled gardens and developing strategies that blend the scale of closed platforms with the transparency and flexibility of the open web.
How Marketers Can Thrive Beyond Walled Gardens
The end of walled garden dominance doesn’t mean marketers are left without powerful tools. It simply requires a smarter, more connected approach.
By blending closed-platform advantages with open-web opportunities, brands can build resilience and long-term growth.
Embrace Cross-Platform Measurement
The first step to breaking free from walled garden limitations is to unify performance data across channels.
Cross-platform measurement allows marketers to see how campaigns on Google, Meta, Amazon, and the open web interact to drive conversions, rather than viewing each in isolation.
With a consolidated perspective, advertisers can optimize spend with confidence.
Collecting and organizing customer data directly — through email lists, loyalty programs, or on-site behavior — provides independence from platform restrictions.
A strong first-party foundation ensures that brands maintain control and can activate insights wherever they choose to advertise.
Leverage Incrementality Testing and MMM
Relying solely on platform-reported attribution risks overstating impact.
Incrementality testing and media mix modeling (MMM) offer alternative ways to measure true lift across campaigns.
By layering these approaches, marketers can validate whether spend is actually driving outcomes, enabling smarter budget reallocation across both closed and open ecosystems.
Adopt Integrated Tech Solutions
Finally, thriving in a fragmented landscape requires technology that connects the dots.
Tools like Northbeam integrate walled garden performance with open-web data, giving marketers a single source of truth.
This unified view not only improves transparency but also makes it easier to allocate budgets dynamically based on what’s really working.
In short, success beyond walled gardens in ad tech comes down to balance: using the scale of closed ecosystems while unlocking the transparency and flexibility of open web advertising.
Northbeam’s Role in the Open Measurement Future
Navigating the shift beyond walled gardens requires more than just strategy; it demands the right technology.
Northbeam delivers industry-leading analytics that connect performance data from both walled gardens and the open web.
Instead of relying on platform-reported metrics in isolation, brands can access a single, unified view that reveals the full customer journey across channels.
This makes it possible to see what’s truly driving revenue, and where budget can be better allocated.
Northbeam enables marketers to understand the complete impact of their campaigns by delivering industry-leading analytics and privacy-first design
Equally important, Northbeam’s platform is built with a privacy-first design. Marketers gain actionable, transparent insights without sacrificing compliance or user trust.
By balancing precision with accountability, Northbeam empowers brands to make decisions with confidence in a post-walled-garden world.
Conclusion
Walled gardens have shaped the past decade of digital advertising, offering scale, targeting, and efficiency that made them nearly irresistible. But as costs rise and transparency erodes, their limitations are becoming impossible to ignore. The future of advertising won’t be about choosing between closed platforms and the open web — it will be about finding the right balance between the two.
Marketers who embrace cross-platform measurement, invest in first-party data, and adopt advanced testing methodologies will be better positioned to thrive in this evolving landscape. Technology partners like Northbeam make this possible, providing a unified view of performance across both walled gardens and open ecosystems while upholding privacy and trust.
Ultimately, the winners in the post-walled-garden era will be the brands that prioritize transparency, flexibility, and accountability — leveraging the best of both worlds to drive sustainable growth.
Sky-high expectations combined with post-pandemic economic austerity measures and the ever-changing marketing landscape have led to the ousting of many a CMO.
The average tenure of a CMO in 2021 and 2022 was 40 months, the lowest it's been in a decade, and less than half the average tenure of a CEO, which is 85 months.
FastCompany summarizes the curse placed on the modern CMO:
“CMOs enter an organization that they perhaps don’t fully understand, and before they have the chance to find their footing, are tasked with meeting ambitious deadlines or targets. They are, in essence, set up to fail. And so, after a couple of years, they leave, only for the cycle to restart.”
You don't have to fall into this trap. Whether you're a CMO (or aspiring to be) there are ways to secure both your position and the success of your team.
At Northbeam, we work with hundreds of marketing leaders. We've seen what behaviors separate high-performing CMOs from those at-risk.
We spoke with Bryan Bumgardner, Director of Growth Marketing at Northbeam and Luca Taormina, Sr. Partner Manager at Northbeam to put together 5 tactical tips on how to do a radically excellent job as a CMO.
1. Understand the modern marketing landscape
The past twenty years have seen a veritable explosion of new marketing channels, strategies, and perspectives. An old playbook — even a two-year-old playbook — simply won’t work anymore. CMOs that bring the same strategy from one company to another are bound to come up against issues.
In November 2007, Facebook enabled brands to serve targeted ads to their potential customers, completely changing the marketing game and giving rise to true performance marketing.
In April 2021, Apple launched the iOS 14.5 update and drastically reduced the ability to track individual parameters, kneecapping an entire generation of growth marketers who were used to the "old way."
The landscape threatens to shift again as Google completely deprecates cookies this year. It’s difficult to keep up.
“We’re sitting in this really awkward time in which those media buyers-turned-CMOs that don’t have pre-2007 experience — suddenly the tool they were using is no longer as actionable as it once was,” said Luca.
Today’s CMO has to espouse a creative vision for the brand, while being a technologist skilled with data, while also having a strong sense of the business strategy. A unicorn, basically.
“We’re shifting towards this play with Apple and Google’s privacy updates where instead of being able to target people perfectly with technical implementations, we need to be both more creative-driven or algorithm-driven,” Bryan said.
"Plus you need to be more business-savvy than ever. Marketers are basically MBAs now."
“Talented CMOs truly understand this combination of how creative work can impact analytics and data and vice versa, and how to use both of them to your benefit,” Luca said.
“So it’s exciting; we’re filtering out the wannabes and the imitators.”
2. Embed yourself across teams
Why are COOs often called into take over for exiled CMOs? Because COOs have a bird’s-eye view of the entire business. They understand the product strategy. They understand the sales strategy. They understand the finances. And they know how marketing can fit into that equation in a complementary and holistic way.
Take note, CMOs: don’t stay in your lane. Get involved early and often in product and sales, customer success and finance, and any other teams that touch your company’s go-to-market flow.
A marketing department’s scope of control has never been broader. Consider this non-comprehensive list of marketing functions, and all the various departments at a company that could serve as potential stakeholders:
PR
Conferences
Webinars
Product marketing
Sales enablement
Partnerships
Website management
Newsletters and email campaigns
Experiential marketing
Community management
Analytics and reporting
Brand
Content
Communications
Research
SEO
Digital strategy
Social media
Analyst relations
Demand generation
CMOs are expected to either run these functions themselves, or manage people or agencies to do so. Community management requires a close understanding of the customer experience. Product marketing involves constant communication with engineering teams. Any paid channels necessitate an intimate relationship with finance’s priorities. The list goes on.
Only by having a clear picture of the entire company’s day-to-day and direction can you align your strategy fully with the overall vision — and get the information and resources you need to do your job!
Bonus: if you can manage some sway across departments, you can successfully avoid a situation where you are tasked with selling a sub-par product, or trying to grow the top of the funnel while the bottom is leaking severely. It’s a win-win, if you can swing it.
3. Become a master storyteller
Storytelling is what brings a brand to life and engenders life-long loyalty. But we’re not just talking about the stories we tell externally — CMOs have to become expert storytellers within their organization.
The truth is that most people who don’t do marketing have no idea what it is you do — not even (or especially not) your CEO or founder.
"There's a terrible refrain I've heard often after a senior marketing lead is ousted, " Bryan said. "People will say: 'What did that person even do here?' It's terrible to see a marketers true influence so misunderstood."
The vast majority (over 75%) of CEOs don’t have a background in marketing. Most come from operations, finance, or engineering. They don’t know the ins and outs of your role, how much budget you actually need, or what is a reasonable expectation or timeline for you to deliver on.
“Rarely do you see colleagues question any other discipline so openly,” said a CMO for Forbes. This results in a distinct and dangerous lack of trust.
Only 4% of CEOs say the CMO is the most trusted member of the leadership team — and only 32% trust them overall. That’s pretty dismal.
“I think this stems from a misunderstanding of marketing, a misunderstanding of what a CMO should be doing and the kind of results you can expect in 2024 and beyond,” Bryan said.
“In the event that a CMO is brought on when a founder doesn’t have a budget prepared, doesn’t have a staff ready, and they can’t speak to any of your questions particularly well, they’re not hiring a CMO: they’re hiring a co-founder.”
But often, they don’t recognize that they’re hiring a co-founder, an equal, and it’s your responsibility to make them understand that — your job may depend on it.
Storytelling can help build trust through affinity, persuasion, and pathos. Where data fails, storytelling wins, and vice versa. If you can’t make the case for your strategy through numbers, leverage your storytelling craft to educate the rest of your executive team about what you’re doing and why it matters.
"You aren't just storytelling for your customers, you're storytelling internally to your own team - make sure they understand what you're doing and why you're doing it," Bryan said.
4. Be ruthlessly realistic
“I think the absolute first conversation you need to have when you’re interviewing to be a DTC CMO is about budget,” Bryan said.
“Ask: how many people am I going to be permitted to hire? How many agencies can I hire? And do you agree that these people are required for us to succeed with the expectations that you’re setting for performance?”
“And if they don’t agree that you need an agency or two to handle your creative, your product marketing, whatever it may be, then you’re going to fail because they’re putting tire locks on your playbook before you’ve even started.”
CMOs are often met with a “just get it done” mentality from founders and CEOs. And while “just get it done” could be pulled off in other departments with sleepless nights and long-worked weekends, you can’t be expected to pull off miracles in the CMO role, where budget and resources are crucial — at least not with any regularity.
“The number one thing that is killing CMOs is the business shift into demanding ruthless profitability. Marketing is a huge line item on your P&L and one of the biggest variable costs. So when investors and boards are demanding profitability, they’re going to look at marketing first,” Bryan said.
Consider shifting your metrics towards harder numbers like cash, rather than engagement or marketing metrics that we know are relevant for growth but less understood by non-marketers.
“Profitability, cash in the bank, cash multipliers, and efficiency-focused metrics,” Bryan said. “Efficiency from day one is crucial. Don’t start with a massive budget; make your campaigns efficient from the start. Focus on capturing user data and building marketing around it.
"Look beyond traditional metrics and prioritize metrics that reflect your brand’s success and profitability. Brands are shifting their metrics constantly and what’s important today might not be tomorrow. Just try to focus on the things that matter and avoid falling into any unreasonable expectations.”
5. Innovate, innovate, innovate
Luca shared a quote from “The War of Art” by Steven Pressfield:
“The counterfeit innovator is wildly self-confident. The real one is scared to death.”
“I think that’s what a CMO should be: scared to death,” Luca said. “A CMO should not just be able to say ‘Hey, I hit a 5x return on ad spend this week’ and suddenly go around giving advice on how to succeed. A CMO really needs to understand that they’re dealing with a robust and changing audience.”
As a CMO, success has to be proven quarter after quarter, year after year. A successful campaign cannot be coasted on, but has to be replicated consistently, with continuous doses of ingenuity and innovation to keep it fresh.
“Your skills are your job security,” said Bryan. “Your playbook is your job security, it needs to constantly be evolving. You need to be able to put that into action and learn from it.”
Technology is your innovation superpower, too.
“Doing more with less is fundamentally a data problem. You need to have good data that explains the results of every little thing you do as a CMO. And it goes beyond having data to having accurate data that can tell the story you need to tell,” Bryan said.
“This is where Northbeam comes into play. You can draw a line from your activities to the revenue you’re generating, and take the guesswork out of attribution.”
If the first tip on this list is to have a thorough and up-to-date understanding of the ever-shifting marketing landscape, the last is to wield that knowledge as your superpower.
That knowledge will allow you to look ahead and innovate, to be creative and tactical and understand where the market, where your vertical, is going.
And here’s a bonus tip: enjoy the job. You can’t innovate without passion.
“You need to drink the tea,” said Luca. “You need to fall in love with the company and what it does. That company has to be the absolute savior.”
The implicit advice behind all of five of the tips above is to start with passion, and love for the art of marketing itself.
In the wide world of marketing analytics tools, Media Mix Modeling (MMM) stands out as particularly powerful. While multi-touch attribution (MTA) tools can go deep on the effect of individual touchpoints, MMM gives you thorough information about the performance of individual channels.
MMM goes beyond the day-to-day to give you a top-down look at performance so you can forecast, budget, and plan accordingly.
With more and more companies transitioning to MMM insights, the question arises: should you build a solution in house, or buy one off the shelf?
The Case for Building
Here are the three primary reasons you may want to build an MMM solution in-house:
Customization
Control
Cost
Customization
The main benefit of building an MMM solution yourself is that you can decide exactly what it does and how it does it. You have full control over the scope and specs of this tool, and you can change that scope whenever you need to as your needs change.
Of course, this requires resources, but some companies may find that those additional resources are worth it in order to prioritize their own product needs over the potential needs of other customers with an off-the-shelf solution.
Existing solutions may not always align perfectly with your needs. If that’s the case, building an MMM solution may be right for you.
Control
In a similar vein, building your own solution gives you complete control — not only over the scope of the product, but also over your company’s (or customers’) data.
If you’re in a heavily-regulated industry like healthcare or financial services, you may have more peace-of-mind knowing that you can control your security protocols from start to finish.
Building an in-house solution offers you complete control over proprietary or sensitive information, so you don’t have to worry about potential security issues down the line.
Cost
Building an MMM tool is more expensive up front (more on that below), but if you expect your data needs to stay the same over the years, you may very well unlock cost savings in the long-term.
Once your MMM solution is developed, the cost of maintaining and updating it is significantly lower, and it may prove to be more affordable than an enterprise-grade solution.
This is especially true for large enterprises that deal with massive amounts of data and have the deep in-house expertise to manage a tool like MMM themselves!
The Case for Buying
There are four main reasons that you may consider buying an MMM solution off the shelf:
Convenience
Expertise
Scalability
Resources
Convenience
Let’s be real, it’s way more convenient to buy a ready-made solution than to build one in-house. Instead of kicking off an expensive, long-term process that involves project management and maintenance, you can do your research, take a few demos, make a decision, and be up and running in a matter of weeks — not months or years.
Buying a solution allows you to skip the planning, development, and testing time and get straight to the actionable insights.
This convenience is hard to beat, especially for rapidly-growing teams that need to focus all their attention on the task at hand: building their business.
Expertise
If you don’t have the in-house expertise to build an MMM product and/or interpret its outputs, buying a solution may be the best option for you. Most MMM solutions come with not just the product itself but a dedicated team to help you get started and answer any questions you have along the way.
Consider how long it would take (and how much it would cost) to hire or train the expertise needed to build, maintain, and make the most of an MMM product.
With a team like Northbeam, you get tailored recommendations, strategic insights, industry best practices, and more included in the cost of doing business.
Scalability
As your business grows, so will your data and analytics needs. The MMM model that worked for you last year may not work for you this year, or next. There is serious maintenance to in-house MMM, and that maintenance will involve necessary updates and upgrades to keep up with your growing business needs — and the changing marketing landscape.
What if privacy measures restrict your existing sources of data? What if new platforms arise that you need to incorporate?
Off-the-shelf solutions are built to scale with you, and come with a dedicated team that can continue to tailor the MMM product as needed. If you foresee your business growing and changing in the coming years, a ready-made solution may make more sense for you.
Resources
We’ve touched on resources already throughout this guide, but let’s break it down further.
Here are some questions to ask yourself to assess whether your team is adequately staffed to build an MMM tool in-house:
When do you want to be up-and-running with an MMM solution?
How many people (engineers, project managers, marketing experts, data analysts, etc.) would you need to dedicate to this project in the building stage?
How many people (and how much of their time) would you need to dedicate to the maintenance of this product?
Consider doing some back-of-the-napkin math to put together a simple formula:
Cost of Building an MMM Tool In-House = Resources x Months x Salary Per Month
Here’s a simplified example:
Cost of Building an MMM Tool In-House = 1 Engineer and 1 Marketing Data Analyst x 6 Months x $12,500/month = $150,000
The above would be the initial cost to build an MMM tool — not the total cost. It doesn’t account for regular maintenance and updates. You may want to develop your own formula that takes into account the resources you would need to support this product.
Depending on your team and needs, it's likely that an off-the-shelf solution will prove more resource-effective.
Every Company is Different
Every company and use case is different. Depending on your particular situation, building an MMM tool may make the most sense. If that’s not the case, or if you want to talk it out with a professional, our team is here to help. Schedule a time to discover our MMM+ tool and how it can help scale your business to the next level.
The marketing world is full of metrics, and few cause more confusion than MER and ROAS.
On the surface, they seem interchangeable. In reality, they serve different purposes, and knowing which to prioritize can mean the difference between scaling profitably and burning through budget.
As marketers juggle cash flow, growth targets, and increasingly complex customer journeys, choosing the right metric at the right time can dramatically shift outcomes.
The wrong focus can create blind spots; the right one can sharpen your strategy and unlock stronger returns.
In this guide, we’ll break down what each metric measures, how they differ, and when to use each of them.
You’ll learn how to combine MER’s big-picture view with ROAS’s tactical precision to drive both short-term wins and long-term growth.
Let’s dive in.
What Is Marketing Efficiency Ratio (MER)?
Marketing Efficiency Ratio (MER), also called Media Efficiency Ratio, measures the relationship between the total revenue your business generates and your total marketing or advertising spend.
The Marketing Efficiency Ratio formula is simple: MER = Total Revenue ÷ Total Marketing/Ad Spend
Unlike campaign-level metrics, MER takes a top-down, holistic view of performance. It looks at all channels and campaigns together, showing you how efficiently your total marketing investment is converting into revenue.
This broad scope is why MER is sometimes referred to as blended ROAS or eROAS.
Instead of focusing on the return from a single campaign, it captures the cumulative effect of all your marketing activity, including brand-building efforts, halo effects from organic traffic, and cross-channel influence.
In some industries, you may also see MER framed as TACoS (Total Advertising Cost of Sale).
While the name is different, the concept is the same: understanding how much revenue you’re generating for every dollar spent across all marketing efforts, not just one slice of the pie.
💡To Recap: Marketing Efficiency Ratio (MER) is also sometimes referred to as Media Efficiency Ratio, blended ROAS, eROAS, or TACoS.
By focusing on the big picture, MER helps marketers cut through platform-by-platform noise and answer the most important question: Is my marketing spend making the business more profitable overall?
What Is ROAS?
Return on Ad Spend (ROAS) measures the revenue generated by a specific campaign or channel compared to the amount spent on it.
The formula is: Revenue from a Specific Campaign ÷ Campaign Ad Spend = ROAS
Because it isolates performance at the campaign level, ROAS is highly precise.
It’s ideal for identifying which ads, audiences, or platforms are delivering the best return — and where to optimize or reallocate budget for maximum impact.
MER vs. ROAS Comparison and Key Differences
Both metrics have value: MER gives you the big-picture efficiency view, while ROAS helps you fine-tune at the ground level.
Together, they create a complete performance snapshot.
Why MER Matters Today
In an era of fragmented customer journeys and stricter privacy rules, attribution data is often incomplete. This is where MER shines.
Platform-reported ROAS can be misleading if it only counts conversions that can be directly tracked back to clicks or views.
For example, a customer who sees an Instagram ad, reads a blog post, and then purchases via an organic search might be invisible to that campaign’s ROAS — but MER captures the revenue regardless of the path taken.
As another example, imagine a DTC brand running paid search, paid social, and influencer partnerships saw an average ROAS of 2.0 in Meta Ads Manager.
However, when they calculated MER across all channels, the ratio was 3.5, revealing that paid social was driving halo effects in organic and email sales that platform metrics couldn’t see.
In omnichannel environments, where customers interact with multiple touchpoints before converting, MER becomes the true efficiency metric.
It sidesteps attribution gaps by focusing on actual business outcomes (total revenue vs. total marketing investment) giving marketing leaders the clarity they need to make strategic decisions.
When to Use MER vs. ROAS
Both MER and ROAS have their place. The key is knowing when to lean on each.
Let’s do a quick MER vs ROAS comparison:
Use MER when:
Evaluating brand-wide health and overall profitability.
Planning long-term budgets or proving ROI to stakeholders (e.g., CMO or board-level reporting)
Measuring the combined impact of brand-building, organic growth, and paid media.
Use ROAS when:
Optimizing the performance of an individual campaign or channel.
Allocating budgets tactically across platforms based on near-term results.
Testing creative, targeting, or bidding strategies for quick performance insights.
By pairing these metrics, you can make high-level strategic decisions while still fine-tuning the details that drive day-to-day performance.
Benchmarking MER & Best Practices
A good MER benchmark often falls around 5.0 or higher, meaning your business is generating $5 in revenue for every $1 spent on marketing.
However, good MER benchmarks can vary widely by industry, business model, and growth stage.
A scaling startup might operate with a lower MER in the short term, while a mature brand might aim for a higher ratio to sustain profitability.
Here are three best practices for using and improving MER effectively:
Standardize Your Inputs
Clearly define which revenue streams (e.g., online sales, retail, wholesale) and marketing costs (e.g., ad spend, agency fees, creative production) are included in your MER calculation.
Inconsistent definitions can distort trends and make it difficult to compare performance over time.
By keeping the inputs uniform, you ensure your MER is a true reflection of changes in efficiency, not changes in how you’re counting.
Track Continuously
Treat MER as a rolling benchmark, reviewing it monthly or quarterly rather than just at year-end.
This allows you to see the impact of campaigns, seasonal shifts, or budget adjustments in near real time.
Consistent tracking makes it easier to spot early warning signs or capitalize quickly on positive trends.
Pair MER with ROAS
MER delivers the big-picture view of how all marketing is working together, while ROAS zooms in on individual campaigns.
Using both ensures you can make strategic, company-wide decisions without losing sight of tactical optimizations.
This dual approach helps you balance long-term growth goals with short-term performance wins.
When applied consistently, MER becomes more than just a number: it’s a dependable signal for whether your overall marketing strategy is driving sustainable business outcomes.
MER and ROAS: The Full Picture
MER and ROAS aren’t competing metrics, they’re complementary tools.
MER gives you macro marketing metrics for overall marketing efficiency, showing how every channel works together to drive revenue.
ROAS delivers the micro-level insights you need to fine-tune specific campaigns and tactics.
The question is: Are your metrics driving clarity or creating blind spots?
By tracking both MER and ROAS, you can make confident decisions that support short-term wins and long-term growth.
Artificial intelligence (AI) is a force multiplier, allowing organizations to do more with less. Northbeam was founded with AI baked into its DNA; its team of in-house academics and AI experts built Northbeam to wield the power of machine learning, attribution modeling, and statistical analysis to help marketers achieve their most ambitious goals.
By using Northbeam as your marketing team's source of truth, you're not only using AI by extension - you're making AI the backbone of your marketing processes.
So, how exactly does Northbeam use AI?
What is artificial intelligence?
Let’s differentiate between different types of AI.
AI is an umbrella term that refers to the ability of a computer to think, act, and/or learn like a human. AI has many different applications, like machine learning (ML), generative AI, natural language processing (NLP), and large language models (LLMs) to name a few.
ML is a form of AI that learns over time and is able to use algorithms trained on data to create new models — even new AIs! — to perform a variety of complex tasks. It is the powerhouse of AI applications. Using a strong ML model is like putting a supercharged engine in your car, whereas generative AI is more akin to giving your car a fancy paint job.
Northbeam uses ML to analyze trillions of data points and come to conclusions about your performance with a superhuman degree of speed and accuracy. Its ML models are built to deliver next generation ad attribution and forecasting so you can make the best decisions possible.
Ad Attribution
Marketing attribution is the process of measuring and quantifying the individual impact of all of your campaigns on a desired outcome. If your desired outcome is a completed checkout, attribution helps you understand the effect of every activity a customer did before they finished their purchase. If your desired outcome is lead generation, proper attribution can show you which campaigns contributed to a lead ultimately converting on your website.
If you can get attribution right — if you can understand the exact impact of each touch on the buyer’s journey — then you can get your budgeting and spend right. The simple truth is that if you don’t know how your campaigns are performing and contributing to your bottom line, you can’t truly optimize your spend.
Northbeam’s proprietary ML models do the dirty work of attribution for you. They analyze first party data across thousands of parameters and assign a percentage attribution to each campaign or touch along a buyer’s journey.
Because Northbeam feeds its models with direct first party data, they are not susceptible to reporting bugs or changes in privacy settings.
“Our technology is very resilient to the current privacy landscape and we’ve built out offerings like MMM+ that are future-proof,” said Josh Rad, Principal Technical Product Manager at Northbeam.
“We don’t rely on third party cookies or tracking, which makes me confident in the quality, accuracy, and compliance of the data that comes into our system,” said Dan Huang, Chief Technology Officer at Northbeam.
Northbeam doesn’t look at activity in a vacuum. Its models combine data across platforms and channels to present a unified picture of your ad attribution so you can make informed spending decisions with the help of powerful AI.
“If you look at click-through data on other platforms, you see lots of purchases and only one touchpoint — you know in most cases the person did not simply type in the name of the website to make a purchase," Huang said.
"We use probabilistic machine learning models to predict and infer where that purchase actually came from based on each brand’s own historical data and performance."
“It’s a very customized model based on the brand’s true customer data,” Josh said. “Our machine learning is reducing the amount of traffic that other platforms or tools will say is direct, but actually isn’t.”
You heard them: Northbeam's machine learning fills in the naturally-occurring gaps you see in other ad attribution datasets.
Forecasting
Super-accurate ad attribution is already a boon, but what if you could forecast how different channels would continue to perform in the future? ML is especially suited for this type of task: intaking trillions of data points and using them to model or predict future impact.
“We can forecast your attribution windows based on your historical information,” Huang said. “A click today might generate revenue in the next thirty days, right? And that is valuable information if you want to know how your campaigns are performing now or will continue to perform in the future without actually having to wait thirty days to get that data.”
Northbeam’s ML can go beyond real-time to deliver dynamic forecasting at your fingertips. You can run simulations on the Northbeam platform and see how channels would perform at different spending levels. This lets you predict when diminishing returns may occur and optimize your ad spend for maximum ROI.
The best part is that Northbeam’s ML gets better over time: a hallmark of strong AI. As it learns your unique data and channel performance, its predictions and attributions become more fine-tuned, delivering more and more value with each use.
“No one can give you 100% ground truth. If someone tells you they can, they’re being misleading. We intentionally don’t train our model on your entire historical dataset because we want to validate and see if we can match the non-trained historical data. This tests the performance of our methodology, and our accuracy against ground truth,” Huang said. “This is called ‘backtesting.’”
But perhaps the actual best part is that you don’t need to be an AI expert like Dan or Josh to use Northbeam. Its platform’s backend is built on a foundation of industry-leading AI and its frontend is built with you in mind, so anyone on your team can get instant value with ease.
A “cost per click” (or “CPC”) is a commonly-used metric in performance marketing. It stands for exactly that: the cost of a click on an ad campaign, ad set, or ad creative.
Intuitively, it feels like a good CPC means you’re running an efficient ad. Low cost per clicks means you’re getting people to click your ads at a relatively cheap price.
But is it that simple? Discussions about auction prices come up often on Linkedin and Twitter (X), usually without context.
Without context, any metric can be misconstrued. Whether or not CPCs matter depends entirely on the situation.
Let me show you a case study of a real situation of when high CPCs “mattered” and how we fixed it.
The Problem: $13 Cost Per New Visitor
Acceptable CPC ranges vary wildly depending on your product price, industry and business model. It’s almost impossible to say what a brand’s CPC should be without looking at the rest of their funnel first.
For example, you can look at “Cost Per New Visitor” (eCPNV), a derivative of CPC. This is the price you pay for a new visitor, obviously - but it’s an important measure for understanding how well your new customer acquisition is working.
If potential new customers aren’t clicking your ads, you aren’t going to get new customers. A high eCPNV means your ads aren’t bringing in new folks efficiently. That’s not what we want, right?
Let's look at an example. Below is a screenshot of real Northbeam data. For this brand in particular, a $13 eCPNV and $37 CPM are excessive.
This is a good indicator that this brand was spending too much on warm segments on Facebook Ads. These audiences already know a lot about this brand, which means we’re wasting spend pushing ads on them.
The Solution: Scaling Back & Diversifying Media Mix
This brand already had a large content library, so I moved some spend to YouTube. It made sense: I know video ad content is already working for this brand. Let’s take this content somewhere audiences are contextually primed for it.
Here, you can see in the first few days we are reaching much more people at a lower cost on YouTube compared to Facebook.
As you can see in the table, looking at Facebook and YouTube combined, our eCPNV is now 47% lower compared to the prior period.
Facebook eCPNV also dropped from scaling down spend. This means spreading our content across multiple channels resulted in more efficient results even in our primary selling channels like Facebook.
Despite the lower 1-day-click new customer acquisition costs (nCAC), we’re still hitting blended nCAC targets, so gaining the additional reach and cheaper email signup costs is a bonus win. That will have a downstream impact going into Q1 and beyond.
Plus these YouTube results are without any optimizations beyond an initial audience test, which suggests there’s an opportunity to unlock further performance.
Why reach and traffic volume matter
You can't build a brand without reaching new people.
But reach by itself doesn’t matter, or we’d all be manual bidding for clicks on the Google Display Network.
You need to make sure there’s an acceptable level of purchase intent, too.
— Zack Miller DTC/Ecomm Growth Marketing Partner (@growthzacks) December 7, 2023
If you’re thinking about CPCs, eCPNVs, nCACs, or any number of other metric acronyms, you should always consider the core metrics they derive from. For this brand’s eCPNV problem, it is important to look at reach and volume of traffic.
Reach by itself doesn’t matter, or we’d all be manual bidding for clicks on the Google Display Network. You need to make sure there’s an acceptable level of purchase intent, too.
I use Northbeam to help me understand that intent by looking at some of the following metrics:
% New Visits: ratio of new vs returning users based on data collected from Northbeam’s pixel. This is a great way of tracking site traffic in relation to your new customer acquisition strategies.
eCPNV: Cost per new visitor, which is a more effective measure of reach than just looking at CPC. It’s more effective because it adds value to your reach: you know what you’re paying to bring new eyeballs (and their potential LTV) to your store.
Cost per email signup: Existing customers and leads are already in your list. Email signups are a great measure of reach. It also marks them as at least somewhat interested - they wanted on your email list, after all. If you can acquire emails at a cheap price, those people are easily targeted using your email ad campaigns.
New customer ROAS (NC ROAS) and new customer acquisition cost (nCAC): If you’re struggling to reach new people, these metrics will be low compared to returning customer ROAS and CAC. Northbeam gives you new, returning and blended.
There’s no one answer for growth
When trying to solve something like high CPCs, this was the easiest solution. Start with the low hanging fruit while you’re planning to solve for more complicated challenges.
Thinking about new channels is a great way to expand your options for problems solving. In this scenario, we were able to have a pretty significant impact on reach just by shifting some money around while planning our next batch of content to test on Facebook.
Scaling on Facebook Ads alone is more challenging than ever, and brands need to be able to analyze data from multiple channels to grow profitably.
I use Northbeam to solve the high CPCs problem (and many other) because the metrics provided in the platform give me accurate, actually actionable insights I can use.
So long story short: no matter your CPCs, Northbeam is a great tool for helping you understand that data deeper.
Want to chat more? My name is Zack Miller. I help 8-figure ecommerce brands grow with strategy & execution on paid social, Google and YouTube for a fixed-cost monthly retainer.
Why would Google deprecate cookies in the first place?
Where did the pushback come from?
Why does this decision matter?
What does this mean for marketers?
Why would Google deprecate cookies in the first place?
Google announced its plans to deprecate cookies all the way back in 2020. The motivation for this plan involves a broader understanding of privacy concerns and regulatory pressures.
Privacy — especially digital privacy — has been a hot-button issue over the past five years. With a growing number of data breaches and increasing awareness of how personal information is used online, consumers have become increasingly concerned about their privacy. Governments around the world have responded by imposing more stringent data protection regulations that limit the way that corporations and other actors can collect data from users in digital spaces.
These regulations include the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, among others. These regulations place limits on how personal information can be collected, stored, and used, and place hefty fines on organizations that violate these rules.
Amid this environment, privacy-focused browsers like Mozilla Firefox have drawn consumers through measures to block third party cookies. Third party cookies allow companies to track what you do not just on their own website (first party cookies) but on other websites as well.
For example: you visit a sunglasses website and then you start to see targeted ads on Google for the same sunglasses store. To put it simply, companies use third party cookies to target consumers based on their interests.
In response to increasing regulation and consumer pressure, Google announced its Privacy Sandbox initiative in 2019. The initiative’s goal was to enhance user privacy while simultaneously enabling advertisers to reach consumers in new and different ways. As part of this initiative, cookies were initially going to be phased out by 2022, then later 2023, and now deprecation has been indefinitely delayed.
Where did the pushback come from?
Despite Google’s best intentions to make both consumers and advertisers happy, the road to deprecating third party cookies has been fraught with challenges.
Third party cookies are a cornerstone of digital advertising. Without them, advertisers can’t easily target the right audience at the right time to drive purchases. They won’t be able to deliver highly-targeted ads based on a user’s online behavior, or build out robust and detailed user profiles. Deprecating how advertisers get the data that informs their strategy makes the job of marketing that much more complex. It may reduce revenue and increase spend dramatically, at least in the short-term.
Ironically, some regulators also expressed concerns that Google’s plan would further entrench their dominance in the digital advertising space. By eliminating third party cookies, advertisers may be forced to rely even more on Google’s own advertising tools and data. This is a complex situation that highlights the opposing forces of privacy, competition, and market dynamics.
Finally, finding something to replace third party cookies while still retaining consumer privacy is just difficult. There have been some proposed solutions, like Federated Learning of Cohorts (FLoCs) and Topics, but they have each faced their own criticisms and potential risks. The current alternative is a user-choice prompt that allows users to select their preferred level of online tracking. This is similar to the changes and challenges that iOS 14 introduced to the marketing landscape in 2020. And like iOS 14, user-choice prompts may be disruptive to markers that rely on cookies. Marketing intelligence platforms like Northbeam minimize this disruption to nearly zero — more on that later.
Why does this decision matter?
Google’s decision to reverse its stance on third party cookies has significant implications for nearly all stakeholders in the digital ecosystem.
For advertisers, the continuation of third party cookies is a temporary relief. They can continue to use established methods for ad targeting and measurement for the time being. However, their search for effective alternatives should continue, and advertisers would be wise to stay engaged in ongoing discussion and developments.
For ad publishers like LinkedIn and Facebook, the decision is also a relief. They can maintain their revenue models without the immediate need to overhaul their advertising strategies. Because they rely primarily on ad revenue, this stability in the short-term is crucial. This is especially true for platforms that rely on ad revenue to provide free content to users.
For consumers, the impact is more nuanced. While the continuation of third party cookies means that cross-site tracking of personal information and activity will persist, consumers can also continue to benefit from the aforementioned free content powered by ad revenue. Ideally, having more time to develop adequate privacy solutions will allow this content to remain free while better protecting consumer data protection rights online.
For regulators, the job of scrutinizing the digital advertising landscape and the actions of dominant players like Google continues. The interplay between privacy, competition, and innovation is delicate and will require ongoing attention and potential regulatory adjustments and advancements.
What does this mean for data intelligence platforms?
Back in January, we wrote an explainer about what cookie deprecation would mean for data intelligence platforms like Northbeam.
Our short answer was: it won’t mean much. While some data intelligence and analytics platforms will be significantly hampered in their ability to deliver value without the use of third party cookies, Northbeam has a future-proof machine learning algorithm that mostly bypasses the need for third party cookies altogether.
We ran internal holdout tests to determine how the removal of cookies would impact our modeling outputs, and found that 98% of our data was unaffected.
"Years ago, when we were starting Northbeam, we knew this was coming,” said Austin Harrison, CEO and co-founder of Northbeam. “Google's been telling us for years they were going to do away with cookies, so preparing for that future has been our mission. It's not just about cookies, it's about advertising more effectively in a more privacy-minded era. That's what we're about."
Northbeam prioritizes first party data to resolve customer identities and solve for the deprecation of cookies — without impacting the level of intelligence that our users receive.
With Google’s decision not to deprecate third party cookies, marketers face both familiar and new challenges.
On one hand, they can continue to leverage these cookies for targeted advertising and measurement. This continuity creates stability and strategic benefits for campaign planning, performance tracking, and budgeting.
On the other hand, this is only a temporary reprieve. Changes are coming, and marketers must remain adaptable. The delay in cookie deprecation doesn’t mean the world isn’t shifting towards increasing user privacy online. Marketers will need to stay up-to-date on changes and act proactively to maintain their bottom line.
Marketers should use this time to explore and experiment with alternate targeting methods. Understanding these technologies will best position marketers to perform well when changes inevitably do arrive. Digital privacy innovation can also telegraph well to privacy-focused consumers who want their brands to act in alignment with their values. Ethical data handling will be critical to maintain brand reputation and customer loyalty in the future.
By embracing innovation, fostering transparency, and prioritizing consumer trust, marketers can set themselves up for success — and set themselves apart — in a future where privacy and marketing success are no longer at odds.
Not every underperforming campaign is a creative problem. More often than not, it’s a tracking problem.
You can have the most compelling ad copy, the perfect audience, and a flawless landing page, but if you can’t track where your traffic is coming from, it’s nearly impossible to measure what’s working (or fix what’s not).
That’s where UTM tracking comes in.
UTM parameters are simple, customizable codes you add to your URLs that give you crystal-clear visibility into how each campaign, channel, and creative is performing.
They’re one of the easiest yet most overlooked ways to improve ROI, optimize spend, and make smarter marketing decisions.
In this guide, we’ll cover:
What UTM parameters are and how they work
Step-by-step instructions for creating UTMs
Common mistakes that can ruin your tracking efforts
Best practices for clean, consistent data
And a few advanced tips for automating and scaling your UTM strategy.
Let’s dive in.
What Are UTM Parameters?
UTM parameters (short for Urchin Tracking Module codes) are snippets of text added to the end of a URL.
These tags help marketers track the performance of specific campaigns by passing detailed information to analytics tools like Google Analytics.
Think of UTMs as a way to answer critical questions like: Which campaign drove this click? From which platform? Through which channel?
Each UTM-tagged URL can include up to five key parameters:
Source (utm_source): Identifies where the traffic is coming from. This could be a platform, website, or newsletter. Example: utm_source=facebook
Medium (utm_medium): Describes the type of channel used to deliver the traffic. Example: utm_medium=cpc (for paid ads) or utm_medium=email
Campaign (utm_campaign): Specifies the name of the campaign or promotion. This helps differentiate between various campaigns running on the same platform. Example: utm_campaign=spring_sale
Term (utm_term): Used for tracking specific paid search keywords. It’s especially useful for PPC campaigns. Example: utm_term=running+shoes
Content (utm_content): Distinguishes between multiple links or creatives within the same campaign. Helpful for A/B testing ads or CTAs. Example: utm_content=cta_button vs. utm_content=text_link
When combined, these parameters give you a detailed view of where your traffic is coming from, how it’s interacting with your content, and which efforts are driving the best results.How Does UTM Tracking Work?
When someone clicks on a URL that includes UTM codes for marketing campaigns, those snippets of information are passed along to your analytics platform, like Google Analytics, Northbeam, or any CRM that tracks campaign attribution.
Still wondering how to use UTM tracking?
Here’s how it works in action:
A user clicks on a UTM-tagged URL (e.g., from a Facebook ad or email campaign).
The UTM parameters are sent along with the pageview when the landing page loads.
Your analytics platform captures these parameters and attributes the visit, conversion, or action to the correct source, medium, and campaign.
You can then analyze performance data by filtering traffic, leads, and conversions based on the UTM values.
This process allows you to:
See which campaigns are driving the most traffic (UTM campaign tracking).
Identify which platforms or channels are delivering high-quality leads.
Measure conversion rates by campaign, source, or even specific ad creatives.
Optimize marketing spend by doubling down on what’s working and cutting what’s not.
Without UTMs, you're often left guessing which campaign actually influenced a sale or lead.
With UTMs, you get clear UTM campaign tracking from the initial click to the final conversion.
Why Are UTMs Important?
In a world where marketers juggle multiple channels and campaigns, knowing what’s actually driving results is essential.
That’s where UTM tracking comes in. By tagging your URLs with UTMs, you gain the clarity and control needed to make smarter, data-driven decisions.
Here’s why UTMs are a must for any marketing strategy:
Accurate Attribution
UTMs provide a clear, detailed record of which campaign, channel, and creative drove each click, lead, or sale.
Instead of relying on vague platform reports or last-click attribution, UTMs help you trace performance back to its true source.
Budget Optimization
With accurate tracking, you can identify which campaigns are delivering the highest ROI — and which ones are wasting budget.
This allows you to shift spend toward top-performing channels and cut back on underperformers with confidence.
Better Reporting and Insights
UTMs unlock granular reporting capabilities: you can break down traffic, conversions, and customer behavior by source, medium, campaign, and even specific ad creatives.
This level of insight transforms vague analytics into actionable strategies.
In short: UTMs take the guesswork out of marketing performance and put you in control of your data.
How Do You Create UTM Parameters?
Creating UTM parameters is simple, but setting them up correctly ensures your data stays clean and meaningful.
Here’s a step-by-step guide for how to set up UTM parameters:
Step 1: Decide on a Consistent Tracking Structure
Before you start creating UTMs, establish standard naming conventions for your team.
Decide on lowercase vs. uppercase, set clear guidelines for campaign names, and document source and medium structures.
This consistency is key to clean reporting.
Step 2: Use a UTM Builder Tool
Google’s Campaign URL Builder is a free, easy way to generate UTM-tagged links.
Simply enter your destination URL and fill in the UTM fields: source, medium, campaign, and optionally, term and content.
Once built, your UTM parameters will be appended to the end of your URL.
You can use these tagged URLs in ads, email links, social posts, or any campaign asset that drives traffic.
Step 4: Test URLs Before Launch
Always test your UTM links to ensure they direct to the correct page and that the parameters are registering in your analytics platform.
Click through each link and check your real-time reports to verify tracking is working as expected.
Common Mistakes With UTM Tracking
UTMs are simple, but it’s surprisingly easy to make mistakes that muddy your data and lead to inaccurate reporting.
Here are some of the most common UTM pitfalls, and how to avoid them:
Inconsistent Naming Conventions
Using variations like “Facebook” vs. “facebook” in your utm_source will split your analytics data into separate line items.
Always use lowercase, standardized naming to ensure consistent reporting across campaigns.
Forgetting to Use UTMs on All Campaigns
It’s common to tag paid ads but forget UTMs on organic social posts, email links, or affiliate promotions.
This creates blind spots in attribution.
Every campaign link, paid or organic, should include UTMs for accurate tracking.
Overcomplicating URLs With Unnecessary Tags
UTMs should be clear and purposeful.
Avoid cluttering URLs with redundant or overly detailed parameters that don’t contribute to meaningful reporting.
Instead, stick to essential tags (source, medium, campaign) and only use term or content when they add value.
Losing UTMs Due to Redirects
Some redirects or shortened URLs can strip out UTM parameters, breaking the tracking chain.
Always test redirect flows and ensure your UTMs are passed through correctly. (If you’re using link shorteners, check that they preserve UTM tags.)
What Are UTM Best Practices?
Getting the most out of UTM tracking requires more than just adding parameters to links. It’s about building a disciplined, consistent approach across your team.
Here are key UTM best practices to keep your UTM data clean and actionable:
Standardize Your Naming Conventions
Create a documented set of rules for how your team will name sources, mediums, campaigns, and other UTM components.
Consistency is critical to avoid fragmented data in your analytics reports.
Keep UTMs Lowercase and Simple
UTMs are case-sensitive, so always use lowercase to maintain uniformity.
Stick to clear, readable names (e.g., utm_source=facebook, not utm_source=Fb-Social-Ads).
Use UTMs Across All Campaigns
Apply UTM tags to every traffic-driving link — not just paid ads.
That includes organic social posts, email newsletters, partnerships, and influencer campaigns. The more comprehensive your tracking, the clearer your attribution.
Centralize Tracking in a Shared Spreadsheet or Tool
Build a master UTM tracking guide or spreadsheet (or use a tool like Northbeam) where all campaign links and naming conventions are documented.
This keeps your team aligned and avoids duplicates, inconsistencies, and confusion.
Use a UTM Naming Template or Checklist
To streamline your process, use a UTM naming template that lists approved sources, mediums, and campaign structures.
Here’s an example:
Advanced UTM Tips
Once you’ve mastered the basics, you can level up your UTM tracking with advanced techniques that streamline workflows and enhance attribution accuracy.
Here are a few strategies for more sophisticated tracking:
Dynamic UTMs for Large or Programmatic Campaigns
Manually creating UTMs for hundreds of ads isn’t scalable.
Use dynamic URL parameters in ad platforms to automatically populate UTM fields with relevant values like campaign names, ad sets, or creatives.
This ensures consistency while saving time.
Connecting UTMs with Your CRM or Attribution Platform
To get a full view of how UTM-tagged campaigns drive revenue, integrate UTM data into your CRM or advanced attribution tools.
This helps connect the dots between marketing efforts and actual customer actions, beyond just clicks and sessions.
Automating Tagging with Marketing Platforms
Platforms like HubSpot, Google Analytics 4 (GA4), and Northbeam offer automation features for UTM tagging.
These tools can auto-append UTMs to outbound links, reducing human error and ensuring consistent tracking across all campaigns.
Make UTMs Work For You
UTM codes for marketing campaigns might seem simple, but they’re one of the most powerful tools in a marketer’s toolkit.
With just a few snippets of code, you can transform vague traffic reports into actionable insights, optimize your campaign spend, and prove what’s really driving results.
When used consistently and strategically, UTMs unlock a clearer, more accurate view of your marketing performance, helping you shift from guessing to knowing.
As the dust settles and the credit cards cool from this year’s Black Friday and Cyber Monday (BFCM) weekend, it's time to reflect on how our early 2024 bets stacked up against the actual trends shaping the season.
Earlier this year, Bryan Bumgardner, Northbeam’s Director of Growth Marketing, and Brayden Cruz, Senior Media Strategist, shared their 2024 BFCM predictions. In this blog post, we’ll break down what we got right, what we got wrong, and what we’re looking forward to next year.
Our 2024 BFCM predictions
Sky-high online sales
With ecommerce growing exponentially, 2024 was poised to outpace the $38 billion spent during Cyber Week in 2023. A greater focus on mobile optimization and seamless online experiences was expected to drive this growth, particularly with more consumers avoiding in-store crowds.
Buy Now, Pay Later (BNPL) gains momentum
Following a 17% year-over-year increase in BNPL adoption in 2023, we anticipated even higher reliance on installment payments in 2024. Younger demographics, especially Gen Z, have embraced BNPL as a way to manage holiday spending without straining budgets.
Personalization is the name of the game
This year, we predicted that brands would increasingly rely on AI-driven tools to deliver customized recommendations and targeted campaigns. This trend was predicted to be pivotal for converting the growing number of online browsers into buyers.
Supply chain stabilization
Unlike prior years marked by global disruptions set off by the political landscape, COVID-19, and wars, 2024 predicted a smoother supply chain, allowing retailers to manage inventory and deliver products more efficiently.
How the predictions played out
Sky-high online sales
This year’s Black Friday and Cyber Monday online spending confirmed our prediction of robust growth. On Black Friday, shoppers spent a record-breaking $10.8 billion online. This record was broken only a few days later on Cyber Monday with a whopping $13.3 billion in sales, making it the biggest online shopping day of all time.
Shoppers worldwide will spend an estimated $240 billion online during the holiday season, marking an 8.4% increase from 2023. In addition to Friday and Monday, Cyber Week as a whole accounts for over 25% of global holiday spending, emphasizing its position as the most significant contributor to annual retail performance.
About 75% of shoppers planned to shop online on BFCM, up 7% from last year. Notably, mobile shopping continued its dominance. Over 75% of shoppers have between 1-5 shopping apps on their mobile phones, and 50% of Gen Z and Millennials shop exclusively on their phones.
Buy Now, Pay Later (BNPL) gains momentum
BNPL adoption surged as anticipated, particularly among Gen Z and Millennial shoppers: 39% of Millennials planned to use BNPL during BFCM, followed by 38% of Gen Z shoppers.
By offering flexible installment options, retailers captured budget-conscious consumers navigating high inflation and financial pressures. This year, BNPL drove $18.5 billion in online spend during BFCM alone, an 11% increase from last year.
Personalization is the name of the game
Personalization proved to be a major differentiator during this year’s BFCM, particularly with the growing role of AI.
Generative AI tools were employed by 40% of shoppers to discover deals, specific items, or personalized recommendations, demonstrating how advanced technology is reshaping the shopping landscape.
Last year, AI influenced over 17% of all holiday orders in November and December — and there’s no reason to believe this trend hasn’t increased in 2024. Today, 70% of consumers use generative AI tools like ChatGPT to enhance their shopping experience. This includes finding the best deals, finding specific items online quickly, and getting brand recommendations.
Supply chain stabilization
Unlike previous years marred by supply chain disruptions, 2024 benefited from relative stability. Inventory shortages were minimal thanks to better forecasting, technologies like RFID, and smoother logistics, and retailers leveraged this stability to provide a more seamless shopping experience, ensuring consumers had access to popular products without significant delays.
But while political instability and an international pandemic can impact supply chains, there are other issues to consider, such as logistic worker strikes — like the U.S. port strike in September — or cyber attacks. Nearly 200,000 customers were affected by supply chain cyber attacks worldwide in 2024. As technologies improve on all sides, retailers and companies need to bring intense focus to cyber security to maintain supply chain stability over time.
Overperform with Northbeam
Our team took a look at the year-over-year data for brands that have been with us for multiple BFCM periods, and the results speak for themselves: Northbeam usage is correlated with significant performance growth over BFCM and the holiday period over time.
With Northbeam at their disposal, our clients know the impact of every dollar, and they’re more empowered to put money where it counts with full confidence.
Client A: Supplement company sees explosive ROAS
As an example, let’s look at “Client A,” a supplement company (anonymized to protect their strategy). In 2023, they spent $2.2 million on ads across the November shopping period, and brought in $5.1 million in revenue. That’s a 2.3x ROAS — not bad, but not stellar.
In 2024, they only spent $260,000 on ads — a 90% decrease — and made $8.2 million in revenue. That’s a 31.5x ROAS. How did they do it?
In 2023, Client A spent significant dollars across a wide portfolio of Facebook, Google, Snapchat, and Amazon ads. With Northbeam data in hand, they were able to hone in on the channels and campaigns that made the biggest impact and delivered the most bang for their buck. The result? Based on ad efficiency calculated by Northbeam data, Client A decided to shift a significant amount of advertising dollars into Amazon ads in 2024.
In 2023, Client A spent $38,000 on Amazon ads. In 2024, they upped that spend to $143,500 — nearly a 4x difference — and reduced spend across other platforms accordingly.
The result? Their Amazon ads had an outsized impact on revenue. Northbeam gave Client A the intelligence to approach the BFCM period with a data-backed strategy — and it paid off by the millions.
Client B: Beauty company sees big growth led by new channels
Let’s look at another example in the form of “Client B,” a beauty company. In 2023, they spent $213,000 across channels for the holiday shopping period and brought in $340,000 in revenue. In 2024, they spent $346,000 and made $1.15 million in revenue.
A spend increase of 60% resulted in nearly 4x the revenue!
How did they do it? With Northbeam’s data to enable their decision-making, Client B had the confidence to explore new channels and expand spend in strategic ways. While they maintained even spending on Meta and Google, they added in TikTok and boosted their Amazon spend, knowing that they’d be able to track the impact of each dollar.
Client B’s informed experimentation paid off with a record-breaking BFCM period!
What to watch for in 2025
As we look ahead, several strategies can help retailers succeed next year:
Early campaigns remain a cornerstone of success. Retailers should aim to launch their outreach at least three weeks before BFCM to capture attention before competitors flood the market.
With mobile devices accounting for most e-commerce traffic, retailers must invest in mobile-first designs, faster load times, and user-friendly checkout experiences.
Rising consumer skepticism about Black Friday deals underscores the need for transparency in promotions: over 57% of shoppers questioned the legitimacy of sales and deals.
Offering flexible payment methods, including BNPL, is not just a value-add but a necessity for capturing budget-conscious shoppers.
The 2024 BFCM season highlighted how innovation and preparation are key to thriving during the busiest shopping period of the year. While early predictions largely held true, the evolving consumer landscape emphasizes the need for agility, transparency, and strategic investments in personalization and technology.
Will these same predictions and outcomes hold true for the rest of the 2024 holiday shopping period? Let’s see what the end of 2024 has in store.
Northbeam: the premiere growth tool
Looking at the nearly $1 billion in revenue and $200 million in ad spend across Northbeam clients this year, here is how performance stacked up against data from last year’s BFCM:
The data above was derived from performance tracked across all Northbeam customers, across all industries, who had data inclusive of both 2023 and 2024 time ranges. Data represents the total across the five days of BFCM, comparing 2023 and 2024 year-over-year. All data is cash, on a 1-day click attribution window.
As we reflect on the 2024 BFCM season, it’s clear that data-backed decision-making is a cornerstone of success for retailers.
Northbeam’s MMM+ delivers unparalleled insights into how various marketing channels drive growth and impact revenue. Unlike traditional MMM, which often feels like a black box, Northbeam’s approach combines granular attribution data with intuitive, actionable reporting. This ensures that even complex multi-channel strategies can be demystified and fine-tuned with confidence.
The result? Brands can better allocate resources, maximize ROI, and make informed decisions across their entire marketing mix — even in the most competitive periods like BFCM.
The modern customer journey is non-linear and multidimensional.
They might discover a product on TikTok, research it through Google, get retargeted via Instagram, hear about it on a podcast, and finally convert through an email promo.
Yet, many brands still treat each channel separately, missing out on the full and holistic scope of the customer journey.
An omnichannel marketing strategy ensures every touchpoint works together, delivering a frictionless, personalized experience that increases engagement and revenue.
Read on to learn how to build a seamless omnichannel strategy that actually works.
Omnichannel vs. multichannel
Before we dive in, let’s clarify our definitions.
Many brands think they have an omnichannel strategy when they actually have a multichannel one. The difference?
Multichannel: Brands use multiple platforms (e.g., paid ads, social, email), but they operate in silos — each channel has its own messaging and metrics.
Omnichannel: Every channel works together as part of a unified journey, ensuring a consistent experience no matter where a customer engages.
Omnichannel isn’t just a marketing buzzword — it’s a fundamental shift in how brands engage with customers.
Consumers today expect interactions to be fluid and connected, whether they’re engaging through social, search, email, or in-store. Without a centralized measurement approach, brands risk misattributing conversions and undervaluing key channels that drive long-term growth.
The upside: omnichannel customers have a 30% higher lifetime value than those who shop using only one channel.
How to build a seamless omnichannel strategy
Unify data & measurement
A true omnichannel strategy starts with centralized data and measurement.
Tracking customer interactions across all touchpoints — social, search, email, and in-store — ensures brands understand how different platforms contribute to conversions.
Real-time attribution models, like Northbeam’s Multi-Touch Attribution (MTA) and Media Mix Modeling (MMM+), eliminate guesswork and provide clear insights into how each channel works together. By unifying data, brands gain a complete picture of customer behavior, leading to smarter budget allocation and more effective campaigns.
Create a consistent brand experience
While omnichannel marketing requires brands to be present across multiple platforms, it’s not about copy-pasting the same message everywhere.
Instead, brands should focus on delivering a cohesive experience, ensuring that the voice, visuals, and core messaging remain consistent.
At the same time, content should be tailored to fit each platform’s strengths: short-form storytelling for TikTok, in-depth messaging for email, and engaging visuals for Instagram.
A seamless customer journey means that if a user clicks on an Instagram ad, they should land on a personalized landing page that mirrors the messaging and design from the ad.
Leverage first-party data for personalization
As third-party cookies fade, first-party data has become the most valuable asset for marketers.
Brands should use purchase history, browsing behavior, and engagement patterns to create hyper-personalized experiences. For example, if a customer watches a TikTok ad but doesn’t immediately purchase, instead of retargeting them with another generic ad, brands can follow up with a personalized email offer based on the product they engaged with.
This data-driven personalization leads to higher conversions and keeps customers engaged throughout their journey.
Ensure frictionless cross-channel journeys
A true omnichannel approach means making it easy for customers to move between channels without interruption.
If someone adds an item to their cart on mobile, they should see the same cart when they log in from a desktop later. Brands can also use cross-channel retargeting to remind customers of the products they browsed, whether through social ads, search results, or email follow-ups.
Additionally, aligning creative strategy across channels ensures that promotions and messaging remain consistent, reinforcing the brand experience at every touchpoint.
Test, optimize, and iterate
Omnichannel strategies aren’t static — they should evolve based on real-time data.
Brands can continuously run A/B tests across platforms to understand what resonates with different audiences. By analyzing cross-channel performance, marketers can shift budget toward high-impact touchpoints and optimize campaigns accordingly.
Using Northbeam’s real-time insights, brands can make data-backed decisions about where to scale spend and where to refine strategies.
How to measure omnichannel success
Success in omnichannel marketing is measured beyond just ROAS.
Brands should track Customer Lifetime Value (LTV) to see how omnichannel customers spend over time, as well as Attribution Lift to determine whether awareness channels like TikTok are influencing conversions elsewhere.
Cross-channel ROAS helps brands understand how investing in one platform (like Meta) impacts another (like branded search), while engagement metrics reveal how customers interact with different touchpoints before making a purchase.
With Northbeam’s analytics, brands can monitor these key metrics and more in real time, ensuring their omnichannel strategy is optimized for maximum impact.
The future is omnichannel
Consumers don’t separate their experiences by channel — so neither should brands. A seamless omnichannel marketing strategy ensures that every touchpoint contributes to a unified, high-impact customer journey that drives revenue and retention.
The key to making omnichannel work is measurement. Without the right attribution tools, brands risk misallocating budgets, undervaluing critical channels, and missing revenue opportunities.
Get in touch to see how a data-driven omnichannel strategy can transform your brand.
For years, marketing has been dominated by one key goal: acquiring new customers.
Brands have poured money into digital ads, optimized landing pages, and tested every growth hack in the book — all in the name of customer acquisition. But as customer acquisition costs (CAC) rise and competition intensifies, this model is becoming less sustainable.
Brands can no longer afford to rely solely on new customers to drive growth. Instead, companies that prioritize customer retention — keeping existing customers engaged and returning — are seeing better margins, higher lifetime value (LTV), and more predictable revenue streams.
In today’s marketing landscape, acquisition gets customers in the door, but retention keeps businesses profitable.
In this guide, we’ll explore why marketers need to shift their focus, what a retention-first strategy looks like, and how data-driven insights can make retention the most powerful tool in your growth playbook.
The hidden costs of an acquisition-only focus
Acquiring new customers is undeniably important, but relying too heavily on acquisition creates significant financial and strategic challenges:
Rising Customer Acquisition Costs (CAC) – Digital ad platforms are more crowded than ever, leading to increasing ad costs and diminishing returns on acquisition campaigns.
Short-Term Gains, Long-Term Losses – Many brands acquire customers through discounts and promotions, only to see them churn when the incentives disappear.
Diminishing Returns on Ad Spend – As competition increases, brands must spend more to get the same results, making acquisition-based growth increasingly expensive.
To put it simply: winning new customers is getting harder and more expensive, while keeping existing customers is far more cost-effective.
Increasing retention by just 5% can boost profits by 25% to 95%.
The real question then isn’t (only) how to get more customers — it’s how to keep the ones you already have.
The retention advantage
Retention is more than just a cost-saving tactic — it’s a revenue-driving strategy that builds a stronger, more sustainable business.
Higher Lifetime Value (LTV) – Customers who return spend more over time, increasing their total contribution to your revenue.
Better Margins – Retained customers require no additional acquisition spend, leading to healthier profit margins.
More Predictable Revenue – Repeat customers provide financial stability, reducing reliance on unpredictable acquisition cycles.
Retention isn’t just a nice-to-have — it’s the foundation of long-term profitability. Brands that understand this are shifting resources toward keeping customers engaged, loyal, and consistently purchasing.
How to build a retention-first strategy
Here’s how to ensure that every customer who comes through the door has a reason to stay:
Leverage first-party data for personalization
The key to retention is understanding your customers’ behaviors, preferences, and needs. With the phaseout of third-party cookies, brands must rely on first-party data to deliver personalized experiences that keep customers engaged.
Here are a few tactics you could take:
Segment your audience based on purchasing behavior and engagement.
Use personalized messaging to target past customers with relevant offers.
Optimize cross-channel engagement, ensuring seamless interactions across email, SMS, and social media.
Northbeam’s advanced analytics can help brands identify who their most valuable customers are and how to retain them through personalized marketing.
Strengthen post-purchase engagement
Retention starts the moment a customer completes a purchase, and brands that create seamless, engaging post-purchase experiences are more likely to drive repeat sales.
Consider:
Onboarding sequences to help customers get the most value from their purchase.
Loyalty programs to reward repeat purchases with exclusive discounts or perks.
Proactive customer support to reach out before problems arise, building trust and goodwill.
Great post-purchase experiences don’t just encourage repeat purchases — they turn customers into brand advocates.
Build a community around your brand
Customers stay loyal to brands that make them feel like they belong. Investing in community-driven marketing creates an emotional connection that extends beyond just transactions.
Here are three possible approaches:
Create engaging content that speaks to your audience’s interests.
Foster user-generated content (UGC) by showcasing customers’ stories and testimonials.
Encourage social engagement, making customers feel like part of a larger movement.
Loyal customers aren’t just repeat buyers — they’re brand evangelists who drive organic growth through referrals, word-of-mouth, and social proof.
How to measure retention
If you’re moving towards a retention-first strategy, here are the four key metrics you should make sure to track:
Customer Lifetime Value (LTV) – How much revenue a customer generates over their lifetime.
Repeat Purchase Rate – The percentage of customers who make more than one purchase.
Churn Rate – The rate at which customers stop engaging or purchasing.
Engagement Metrics – I.e. Email open rates, SMS response rates, and loyalty program participation.
Northbeam’s advanced machine learning models provide deep insights into retention trends, helping brands make data-driven decisions to keep customers coming back.
The future of marketing is retention-first
In a world where ad costs are rising and customer expectations are higher than ever, brands that invest in retention will drive more sustainable, profitable growth.
Retention isn’t just about keeping customers — it’s about maximizing their value, turning them into brand advocates, and ensuring long-term business success.
Want to unlock the full power of retention marketing? Learn how Northbeam’s data-driven insights can transform your strategy.
In today’s hyper-competitive marketing landscape, we know that every dollar counts. Yet, a significant portion of ad budgets are consistently wasted due to inefficient strategies, limited data insights, and the ever-changing digital ecosystem and algorithm.
Research shows that over 40% of digital ad spend is wasted — that’s millions of dollars that could otherwise fuel growth and profitability.
If your business is feeling the pinch or struggling to measure campaign success accurately, it may be time to rethink your approach. With smarter tools and strategies, you can unlock the true potential of your ad budget and maximize your return on investment (ROI).
The high stakes of ad spend optimization
There’s a high cost to ad inefficiency. When your campaigns aren’t optimized, the effects can be felt across your organization in the form of higher customer acquisition costs (CAC), lower lifetime value (LTV), and missed opportunities to scale.
The stakes are even higher in today’s privacy-first digital world, where the upcoming death of third-party cookies may leave many businesses scrambling for reliable attribution.
The rise of multi-channel customer journeys adds another layer of complexity. A single customer might engage with your brand across five or more touchpoints before making a purchase, from an Instagram ad to an email campaign to a Google search.
Without a clear view of how these channels work together, businesses can over-allocate budgets to poorly performing channels while neglecting high-performing ones.
But with the right tools and strategies, you can turn your ad spend into a powerful driver of growth and properly account for every dollar.
Common pitfalls in ad spend management
Before diving into solutions, it’s essential to understand the most common mistakes businesses make with their advertising budgets.
Avoiding these pitfalls is the first step to unlocking greater ROI:
Over-reliance on platform data. Platforms like to take full credit for conversions that may have been influenced by other channels. This over-attribution can lead to skewed insights and inefficient spending.
Only focusing on first- and last-touch. Focusing on just one touchpoint gives you an incomplete picture of the customer journey. Without a full account of what ultimately led to a purchase, it's extremely difficult to know where to focus your funds.
One-size-fits-all strategies. Treating all campaigns, audiences, or creatives the same is a recipe for wasted spend. Without testing, personalization, or creative optimization, it’s impossible to fully know what works, and what doesn’t. And keep in mind that what works today might not necessarily be the best strategy tomorrow!
By addressing these challenges, businesses can lay the groundwork for smarter, more efficient ad spend.
How to get more ROI for every dollar
To optimize your ad spend and drive growth, it’s essential to implement data-driven strategies and leverage modern tools.
Here are four actionable steps to help you get started:
1. Invest in Multi-Touch Attribution (MTA)
Multi-touch attribution provides visibility into the entire customer journey, showing how different touchpoints contribute to conversions. Unlike traditional attribution models that rely on cookies, modern MTA solutions (like Northbeam’s) use first-party, cookie-proof attribution for greater accuracy.
2. Use Media Mix Modeling (MMM+)
Media mix modeling (MMM) helps businesses understand the impact of different marketing channels and allocate budgets effectively. Northbeam’s MMM+ goes a step further by incorporating advanced machine learning to forecast revenue and optimize spend in real-time.
Imagine reallocating 10% of your social media budget to search ads based on MMM+ insights — and seeing a 20% lift in conversions. This level of precision ensures that every dollar works harder for your business.
3. Focus on Creative Analytics
Not all ads are created equally, and the performance of your creative can make or break a campaign. Creative analytics tools help identify which ads resonate with your audience and which fall flat, enabling you to make data-driven decisions.
For instance, if your analytics show that video ads outperform static images by 40%, you can shift resources to produce more high-performing video content, boosting your ROI.
4. Rely on First-Party Data
In a world where privacy regulations and platform biases limit the value of third-party data, first-party data is your secret weapon. Northbeam empowers businesses to leverage granular, real-time first-party data to gain unbiased insights into customer behavior and campaign performance.
By building your strategy on a foundation of accurate, reliable data, you can reduce reliance on platform-reported metrics and make smarter decisions.
Ready to make every dollar count?
Ad spend optimization isn’t always about cutting costs — it’s about unlocking growth. There are many areas of potential inefficiency that could be getting in the way of your ROI potential.
By investing in advanced tools like multi-touch attribution, media mix modeling, and creative analytics, you can maximize the impact of every dollar and drive meaningful results for your business.
Economic uncertainty presents significant challenges for businesses, from tighter budgets to shifting consumer priorities. In such times, maximizing return on investment (ROI) becomes a crucial strategy for survival and growth. For marketers, this means making every dollar count by identifying what works and doubling down on it.
In this guide, we’ll explore how data-driven marketing can help you navigate economic turbulence, optimize your campaigns, and achieve sustainable growth — all while maintaining a sharp focus on ROI.
5 Strategies for Maximizing ROI
1. Prioritize Real-Time Data
In an uncertain economy, timely decisions can make or break a campaign. Relying on delayed or outdated data puts your marketing efforts at risk of being reactive instead of proactive. Real-time data allows you to identify trends, adapt strategies, and allocate budgets as opportunities arise.
Example: A fitness apparel brand notices a sudden spike in performance on TikTok due to a trending hashtag. Using real-time insights, they quickly launch a related ad campaign, increasing conversions by 30% while the trend is still active.
2. Prioritize High-Performing Channels
Not all marketing channels perform equally. Identifying and prioritizing your own unique high-performing channels is one of the quickest ways to maximize ROI. Metrics like Return on Ad Spend (ROAS) and Marketing Efficiency Ratio (MER) can help pinpoint where your efforts are paying off.
Example: A direct-to-consumer skincare brand discovers that TikTok ads generate a 25% higher ROAS compared to Meta Ads. By reallocating 30% of their ad budget to TikTok campaigns, they achieve a significant revenue boost while maintaining overall efficiency.
3. Focus on Retention Over Acquisition
Customer acquisition is expensive, particularly during economic downturns: acquiring a new customer can cost 5-25x more than retaining an existing customer. Retention strategies have the potential to deliver a higher ROI by leveraging existing relationships. Companies have a 60%+ chance of selling to an existing customer versus a <20% chance of selling to a new customer — you’ve already built that trust, so use it to your advantage!
Metrics like Customer Lifetime Value (CLV) and returning customer rate are critical for assessing the impact of retention efforts when budgets are tight.
Example: A home decor brand identifies a dip in returning customers through Northbeam’s analytics. By launching a loyalty program that offers exclusive discounts to repeat buyers, the brand increases its returning customer rate by 20% in just three months.
4. Optimize Your Ad Creative and Messaging
When budgets are tight, it’s essential to make your ad creative work harder for you. Tailoring messaging to resonate with your audience’s current needs and priorities can lead to significant improvements in performance.
When you need to optimize for every cent, take advantage of A/B testing to refine visuals, headlines, and calls-to-action to make sure your creative and messaging is going as far as it can.
Example: A SaaS company recognizes that its audience is focused on cost-saving solutions. By emphasizing "efficiency" and "affordability" in its ad copy over its existing “enterprise” messaging, the company improves its conversion rate by 15% and achieves a stronger ROI on its campaigns.
5. Improve Funnel Efficiency
A leaky funnel can erode ROI, no matter how effective your ads are. Optimizing every step of the customer journey — whether it’s simplifying checkout processes, improving website speed, or enhancing user experience — can make a significant difference.
Example: An e-commerce brand analyzes its funnel and discovers a high drop-off rate on the payment page. By streamlining the checkout process and offering more payment options, the brand reduces cart abandonment and sees a 10% increase in completed purchases.
Northbeam Empowers Data-Driven Decisions
Economic uncertainty doesn’t have to mean stagnation or decline. By embracing data-driven strategies, businesses can navigate challenges, optimize marketing efforts, and achieve sustainable growth.
Northbeam simplifies the process of tracking and optimizing marketing metrics, offering real-time insights and actionable recommendations. Its cross-channel attribution capabilities give businesses a comprehensive view of their campaigns, helping them identify high-performing channels and allocate budgets effectively.
During times of economic uncertainty, tools like Northbeam provide marketers with the clarity and precision needed to drive results and adapt to change with confidence.
In today’s crowded digital landscape, marketers are under constant pressure to prove which campaigns actually work.
With customers bouncing between ads, emails, social media, and search before making a purchase, it can feel almost impossible to know which touchpoints deserve credit.
Yet getting this right is critical: without this data, budgets get wasted and growth stalls.
That’s where marketing attribution comes in. Attribution is the framework for assigning value to your marketing efforts, helping you connect customer interactions to conversions.
Instead of guessing which channels drive results, attribution gives you a structured way to see what’s really working, and what’s not, so you can invest your time and budget more effectively.
What Is Marketing Attribution?
At its core, marketing attribution is the practice of figuring out which of your marketing efforts are actually driving results.
Every time a potential customer sees an ad, opens an email, or clicks through a social post, that interaction is a “touchpoint.”
Attribution is the framework that helps you connect those touchpoints to the final conversion, whether that’s a purchase, a sign-up, or another key action.
Think of it less as reporting clicks and more as understanding impact.
Instead of just knowing that a customer clicked an Instagram ad, attribution helps you answer: Did that ad meaningfully influence the sale, or was it your email follow-up that sealed the deal?
By assigning value to different touchpoints, attribution clarifies which channels contribute the most to your bottom line.
The result is better visibility into return on investment (ROI) and stronger alignment between marketing activity and business outcomes.
With attribution in place, teams can make decisions based on evidence, rather than guesswork.
Why Marketing Attribution Matters
Marketing attribution isn’t just a reporting tool: it’s a decision-making framework.
When you know which touchpoints drive results, you can stop guessing and start investing with confidence.
Here are the four primary benefits of marketing attribution:
Improved Budget Allocation
One of the top benefits of marketing attribution is the ability to make more informed budget allocation decisions.
Attribution shows you which channels actually move the needle, so you can shift spend away from underperforming campaigns and double down on what works.
Better Insight into Campaign Performance
Instead of relying on surface-level metrics like impressions or clicks, attribution highlights how each effort contributes to conversions and revenue.
Aligning Cross-Team Strategies
Marketing, sales, and finance often work with different numbers. Attribution creates a shared source of truth that helps everyone stay aligned on business outcomes.
Even a simple attribution strategy for beginners can go a long way in getting you and your team the data you need to collaborate effectively.
Higher Confidence in Scaling Channels
When you know where your returns are strongest, you can invest more aggressively in those areas without second-guessing.
In short, attribution gives marketers the clarity they need to prove impact and plan smarter growth strategies.
Common Attribution Approaches
There’s no single “right” way to measure attribution.
Different models assign credit to marketing touchpoints in different ways, each with its own strengths and limitations.
For beginners, it’s best to understand the basic types of attribution models before moving into more advanced options like multi-touch attribution.
Single-Touch Models
First-click attribution: All the credit goes to the very first touchpoint in the customer journey. If someone first discovered your brand through a Google ad and later converted, that ad gets 100% of the value.
Last-click attribution: All the credit goes to the final touchpoint before conversion. If a customer buys after clicking an email link, the email is considered solely responsible.
Why it matters: These models are simple and easy to implement, but they paint an incomplete picture since most journeys involve multiple influences.
Position-Based Models
These approaches spread credit across several touchpoints without requiring advanced algorithms.
A common example is the U-shaped model, which gives more weight to the first and last touchpoints, while still assigning some credit to the interactions in between.
Why it matters: This strikes a balance between acknowledging the importance of discovery and recognizing the role of closing touchpoints.
Custom or Blended Approaches
Many marketing teams design custom attribution models that mix elements of single-touch and position-based methods.
These custom setups might reflect unique buying cycles or industry needs. For example, a company could decide to give 40% of credit to the first touch, 40% to the last touch, and divide the remaining 20% among the middle interactions.
Why it matters: Custom models can feel more realistic, but they require thoughtful setup and ongoing validation.
How to Implement Marketing Attribution
Getting started with attribution doesn’t have to be overwhelming.
By taking a step-by-step approach, you can build a framework that grows with your business and learn how to implement marketing attribution with confidence.
1. Map Your Customer Journey
List out the main touchpoints where customers interact with your brand: ads, emails, social posts, organic search, referrals, and so on.
Understanding the typical path to conversion gives you a foundation for assigning credit.
2. Decide Which KPIs Matter Most
Are you trying to increase purchases, sign-ups, or repeat visits?
Choosing the right metrics keeps your attribution efforts focused on real business outcomes, not vanity numbers.
3. Choose a Starting Model
Pick an attribution model that matches both your goals and the data you have available.
For many beginners, a simple last-click or position-based model is a good entry point.
You can always refine later as your data maturity grows.
4. Implement With the Right Tools
Manual spreadsheets won’t cut it for long.
An attribution platform makes the process scalable by automatically connecting touchpoints, tracking conversions, and surfacing insights.
Tools like Northbeam are designed to help brands start simple and expand into more advanced models as they grow.
Challenges for Beginners
Attribution basics can feel deceptively simple at first, but there are common roadblocks that trip up many teams.
Being aware of these challenges early will help you avoid costly mistakes.
Data Silos and Tracking Gaps
When marketing, sales, and finance systems don’t talk to each other, it’s tough to follow the full customer journey.
Missing or inconsistent data makes attribution less reliable.
Overcomplicating the Model Too Soon
It’s tempting to jump straight into advanced setups, but starting with too much complexity can create confusion and mistrust in the numbers.
Misattribution from Incomplete Data
If some touchpoints aren’t tracked, like offline interactions or third-party platforms, you risk giving the wrong channels too much or too little credit.
Ignoring Attribution for Decision-Making
Attribution is only valuable if the insights inform real actions.
Some teams treat it as a reporting metric instead of a strategic tool, which means the data never drives smarter budgeting or campaign choices.
The good news? These issues are normal and manageable if you start with a simple model and build gradually.
Best Practices for Effective Attribution
Attribution works best when it’s treated as an ongoing process rather than a one-time setup.
These best practices will help you build a system that marketers, analysts, and executives can all trust.
Start Simple, Expand Gradually
Begin with a straightforward model like last-click or first-click.
Once your team feels comfortable, layer in more complexity to capture a fuller picture.
Validate Results Against Other Metrics
Attribution should never live in a vacuum.
Compare its insights against KPIs like customer acquisition cost (CAC) or return on ad spend (ROAS) to check for consistency.
Keep an Eye on Data Hygiene
Clean, accurate data is the foundation of any attribution system.
Regularly audit your tracking setup to make sure UTM parameters, pixels, and integrations are working correctly.
Use Attribution as a Decision-Making Tool
Numbers alone don’t create impact: action does.
The true value of attribution is in guiding smarter budget allocation, campaign strategy, and cross-team alignment.
By following these practices, you’ll build confidence in your attribution system and ensure it’s actually helping your business grow.
Get Back to the Marketing Attribution Basics
Marketing attribution is one of the most powerful tools for turning data into decisions.
By showing which touchpoints actually drive results, it empowers teams to allocate budget wisely, align strategies across departments, and scale growth with confidence.
If you’re looking for an attribution strategy for beginners, the key is to start simple: pick a basic model, test it against your KPIs, and refine as your data maturity grows.
Over time, you’ll build a more nuanced system that reflects the realities of your customer journey.
Platforms like Northbeam make it easier to evolve, giving you accurate and actionable attribution that grows with your brand.
Whether you’re running your first campaigns or managing a complex media mix, attribution ensures you’re always steering in the right direction.
Operating a Agency can be a Sisyphean task, to say the least.
From dealing with needy clients, validating ad performance, navigating privacy updates , and even just agreeing on attribution models— Agencies are punching far above their weight class when it comes to work load.
This reminds me of one of my favorite legends from Greek mythology. According to the Greek myth, Sisyphus is condemned to roll a rock up to the top of a mountain, only to have the rock roll back down to the bottom every time he reaches the top.
This is similar to the tasks agency operators face in 2024. Supporting a client for the long term can be an endless journey of blood, sweat, and tears.
We see Agencies make or break DTC brands all the time, the last thing they need to worry about is having accurate data. That’s where we come in.
Here's five reasons why Northbeam is the perfect tool to help your agency overcome the Sisyphean cycle.
Northbeam has infinite lookback windows and is unaffected by third-party cookie deprecation.
With the deprecation of third-party cookies in 2024 by Google and Yahoo, Northbeam becomes even more important to agencies and brands. We are 100% reliant on first-party data, use DNS-level tracking, and build our identity resolution in-house. This enables us to be unaffected by third-party cookie changes and maintain full visibility of your customer’s journeys.
When advertising on paid social channels like Meta and TikTok, many agencies face the challenge of getting budget approval for top of funnel spend. Most DTC brand operators are still stuck in their ways with last click attribution are laggards when it comes to top of funnel spend… Makes sense as they see hardly any conversions on a last click basis in Shopify or GA4.
Northbeam provides an infinite attribution window and models built to emphasize intent from top of funnel. These both fuel your signal to optimize for new customer acquisition and ultimately scale your clients business. We see brands with flat growth consistently pivot to top of funnel strategies when onboarding with one of our expert Northbeam agencies.
Flexible payback and conversion lookback timeframes.
Understanding CAC payback periods are crucial for scaling DTC brands. In a world bloated with metrics around CTRs, CPMs, and even ROAS — when you boil it down to necessity the most important metric is CAC/LTV.
Northbeam measures "conversion lag," defined as how long your ad clicks continue to drive revenue. We see for high AOV brands that often times 30-50% of customers were in the funnel for more than 90 days before ultimately converting.
We enable you to look forward and forecast campaign performance down to the ad level, or in hindsight to set benchmarks. Both of these are dynamically calculated throughout the day to ensure you can be as competitive in the auction to achieve key KPIs.
We have brands spending $120 million annually with a CAC payback period of 12 months. With Northbeam they learned that all they need on Meta to be profitable is a .51 ROAS on a 1DC to achieve their lofty business targets of profitable growth.
Creativity is the alpha and the omega. Are you prepared?
I'll say it again: CREATIVE IS THE NEW TARGETING.
The two most common reasons for flat growth are low new customer acquistion acquisition and creative fatigue.
Northbeam’s Creative Analytics allows our agency partners to rapidly scale creative testing with new hooks, angles, and ad types. We also partner with best in class Creative tools like Motion to power creative testing workflows with first-party data.
Ultimately, Creative let’s you reach new audiences. Northbeam enriches your creative with powerful metrics like New Visit %, or even new vs returning data to make sure you're prospecting.
Let’s face it. DTC operators are always going to be skeptical of the massive amount they are spending. Pair that with your agency retainer and you are bound to get a lot of heat from your clients on performance.
With Northbeam we enable you to turn your weekly/daily reporting into an integral part of your agency workflows. Save an unlimited amount of prepared views to make a CEO specific overview page, or paid social dashboard.
When a brand and agency are both optimizing on the same data, exciting growth happens. When both teams are using different data (cough cough… GA, platform data, AND MTA) then its bound to cause some problems in retaining clients
Customer journeys made easy and actionable.
Consumers have and always have bought products from where they spend their time. The problem is that today there is so many different places a consumer can buy from your brand.
From the numerous social platforms, TV, Google, Direct Mail, podcast, and good ol’ fashioned brick and mortar, consumers engage with brands in several different places before ultimately converting.
Northbeam highlights the customer journey at the order level, the customer level, across all customer paths, and even at the campaign level.
This means you can see every customer journey even for specific campaigns.
My favorite use case is understanding how Meta and Google interact with each other. Identifying where your brand search clicks are coming from is key to understand view through attribution, and continue to justify top of funnel spend.
Northbeam helps our agency partners retain their clients.
Our goal is to make sure that having accurate data is the last thing you have to worry about when operating your agency.
We take a hands on approach with our agency partners:
Agency wide trainings
Monthly Strategy calls
Dedicated Slack Channels
Industry insights + benchmarks
Our top goal is to make sure you are able to retain your clients by achieving KPIs with the most accurate attribution data.
Today’s top marketers aren’t just marketers: they’re data analysts, product insiders, customer champions, growth strategists, and more. Marketers have to hold an overview of the entire organization in their mind in order to best position their company’s products and services for success. They have to be savvy with numbers and comfortable getting down with accounting principles to make the best of their budget.
Understanding the different accounting modes — the different ways that ROI can be calculated and accounted for — makes a significant difference in how you assess the performance of your marketing campaigns.
In this guide, we’ll cover basic accounting modes and discuss the two most common ones on the Northbeam platform and beyond: Cash Snapshot and Accrual Performance.
What are accounting modes?
In this context, accounting modes refer to the different ways in which conversions and revenue can be credited within an analytics platform. Northbeam uses the Cash Snapshot and Accrual Performance modes to mirror traditional accounting methods used in corporate finance: cash basis and accrual accounting.
Cash Snapshot modecredits all revenue and conversions to the date when a given transaction actually takes place. This mode is particularly useful for measuring immediate cash flow. It helps you keep track of what is coming in on a daily basis. For example, if a customer makes a purchase on your website tomorrow, all the associated revenue would be attributed to that date.
Accrual Performance mode, on the other hand, credits revenue and conversions to the dates when relevant marketing touchpoints occur. This allows marketers to see a more accurate reflection of how different channels and campaigns contribute to conversions over time. If a customer interacted with your Meta ad yesterday, clicked on your email today, and made a purchase tomorrow, revenue would be distributed across all of these individual dates and touchpoints.
Cash Snapshot mode is more commonly used because it aligns with the way that businesses typically track their finances — based on when cash is received. For example, if you’re reporting on MER, Cash Snapshot mode can provide a clear view of the ratio between total revenue and total spend on a daily basis. This method is straightforward and easy to understand, making it a go-to for many marketers.
However, the simplicity of Cash Snapshot mode can sometimes lead to oversimplified or misleading interpretations of marketing performance. For example, if you launch an ad campaign that leads to significant engagement but no immediate purchases, Cash Snapshot mode might make the campaign seem like a failure, even if it leads to more purchases down the line.
Accrual Performance mode
Accrual Performance mode offers a more nuanced understanding of how your marketing efforts are contributing to conversions. By attributing revenue to the individual dates when marketing touchpoints occur, this mode gives you a clearer picture of which channels and campaigns are truly effective and generating ROI. This can be particularly valuable for marketers focused on scaling paid media and calculating ROAS. It can also be useful for products or services with long sales cycles where individual touchpoints need to be accounted for to get the full picture.
On the other hand, Accrual Performance mode could be over-complicated for straightforward sales cycles or simple marketing strategies. If you don’t have a lot of resources, or aren’t running a lot of campaigns, you may choose to keep things as simple as possible.
The benefits of choosing the right accounting mode
Take the time to think through your options and choose the right accounting mode so you can make the best decisions for your business. If you’re in doubt, try running both and looking at how the results compare. Northbeam lets users choose their preferred accounting mode, or toggle between the two for comparison.
Choosing the right accounting mode helps you:
Assess the true impact of your marketing efforts
Improve your budget allocation
Manage your daily finances
Align your marketing strategy with your business goals
And more.
When in doubt, chat with an expert. Northbeam’s dedicated advisors are happy to talk you through which accounting mode is best for your unique situation.
2025 will be here before you know it, so we’re covering the key marketing trends you need to know about to stay ahead of the competition. If you haven’t checked out part 1, we covered several seismic changes affecting the marketing landscape. From the rise of LinkedIn as the new hot platform to the potential banning of TikTok and the continued evolution of X (formerly known as Twitter), the next year is shaping up to be a very interesting one indeed. That’s not even mentioning how GenAI has completely upended the industry (and the larger world) and will continue to do so. In part 2, we’ll talk about the momentum of CTV in an increasingly cord-cutting world, new search methodologies, nano-influencers, and the growing awareness around data privacy.
Continuous Growth of CTV
In our modern digital world, Connected TV (ads shown on internet-connected TVs including smart TVs, external devices such as Amazon Fire sticks, gaming consoles, and even traditional TV offshoots like YouTube TV and Hulu with Live TV) has emerged as one of the most effective channels available to marketers. Even though the major social media platforms get most of the shine in digital marketing, CTV is one of the fastest-growing major ad channels in the US: projected to reach roughly $30 billion in 2024, a 22.4% growth from the previous year. That’s almost a third of all TV ad spending as cord-cutting becomes even more mainstream, especially among the younger Gen Z demographic. Linear TV viewership among Gen Z declines by a couple of million people a year, falling below 40 million viewers in 2024. On the other hand, there are 55 million Gen Z CTV viewers and counting. In fact, CTV ad spend is growing faster than the rate at which linear TV ad spend is declining. At some point towards the end of this decade, CTV will likely surpass linear TV altogether. Linear TV is still king for major live events such as the Super Bowl, Olympics, World Cup, etc., but even most of these are now available via CTV (albeit fractured across disparate streamers). This is driven by the fact that people in other generations are also choosing to spend more time watching CTV with their media consumption habits. In 2024, US adults spent 123 minutes per day watching CTV which lagged only mobile devices (235 minutes per day) in usage. CTV ad spend is forecasted to grow double digits through the end of 2027 and already accounts for 10% of ad dollars spent on digital formats.
Although CTV has “TV” in its name, CTV acts more like other digital channels such as social media rather than linear TV. CTV allows for precise segmentation and targeting based on interests, demographics, and viewing habits. Streamers have leveraged partnerships with retail media networks and other data brokers to get more accurate reporting and better attribution. CTV lets advertisers analyze trends in real time to aid in campaign optimization just like other performance channels. This is a distinct advantage over linear because we can track a consumer’s journey from first impression to purchase. Note that because streamers often exist as their own distinct walled gardens, advanced techniques such as multi-touch attribution are often needed to track a campaign’s performance across different networks. Last but not least, advertisers have a selection of innovative ad formats including shoppable ads that are more aimed at the bottom of the funnel rather than awareness. These also include formats not available in other channels such as overlays and less intrusive ads.
There are several players in CTV to keep an eye on, but Hulu is the biggest one in the US with $3.8 billion in projected ad revenues in 2024. The company was a first mover in building out an ad tier and live TV offering, so Hulu beat a lot of competitors to the punch when pitching for ad dollars. YouTube comes in second with $3.3 billion and has been aggressively building out its CTV infrastructure to grow that side of its ad business. Amazon has a few CTV products, including Fire Sticks, Fire TVs, Prime Video, and Freevee but is still very much a newer player that’s learning the ropes. Disney+ was able to leverage some of Hulu’s expertise as its corporate owner, but along with Netflix and Max, are still trying to find their footing in the ad-supported game. Notably, Apple TV+ doesn’t yet have a CTV offering, but this may change as the company invests more in digital advertising capabilities.
Newer Forms of Search
It’s no longer enough to have an SEO strategy in place for traditional search; in recent years voice and social search have emerged as popular alternatives to typing keywords into a search query to get answers. According to PwC, 65% of 25-49-year-olds speak to their voice-enabled devices at least once per day. Although voice-enabled search has been around for some time, recent breakthroughs and the wide adoption of smart speakers have accelerated this trend. In 2023, there were an estimated 200 million plus smart speakers in the U.S. Most smartphones, smart TVs, and an increasing number of connected devices feature voice search. In addition to the long runway of potential users, voice search is often faster and more convenient than typing into a search bar. Voice search is a critical opportunity to capture more organic search traffic, especially among this tech-savvy cohort of users who rely on smartphones and smart speakers to look for products and services. It’s important to note that while the foundational SEO strategies remain the same, voice search queries tend to be more conversational and closer to how we speak in real life. A text search might be phrased as “tacos near me” whereas a voice search might be something like “Where can I get the best tacos near me?” Voice search tends to shift focus toward the intent of the searcher as opposed to simply emphasizing keywords. Search engines also reward websites that optimize for voice searches, so investing in this channel can have positive effects on overall SEO and your site’s ranking.
Speaking of new forms of search, younger generations are turning to social media platforms for discovery as opposed to traditional search engines. Instagram is the top search engine for Gen Z with 67% of surveyed users claiming it as their first choice. TikTok was a close second with 62% and Google came in third with 61%. However, these numbers include YouTube, Google Maps, and Google Images which contribute a healthy amount of search traffic to Google, meaning Google Search is now a distant third for Gen Z. If we were to include Gen Alpha, these numbers would probably skew even heavier towards the social platforms. Although we don’t have official numbers, Google disclosed in 2022 that roughly 40% of youth use TikTok or Instagram to search for things like lunch spots rather than Google. Let’s be clear, we’re not saying this is the death of search engines like Google and Bing, but social media platforms are rising in the ranks as search tools across all demographic groups according to SOCI. This isn’t surprising since social platforms rely on algorithms to serve relevant content to users, so they’re naturally set up to be good search engines. Beyond personalization, social platforms tend to have more UGC and authentic content as opposed to Google results served to you by PPC ads. We expect to see this trend continue as younger generations (and even older ones) who grew up with social media increasingly rely on it for search.
Nano-Influencers: Small but Mighty
We’re not exactly sure when influencer marketing truly began, but the practice of companies using prominent figures and celebrities to endorse their products can be traced back to the late 19th century. Once social media gained traction in the early 2010s, the modern influencer was born on platforms such as Facebook, Instagram, YouTube, and now TikTok. Today, they’ve become an integral part of our digital marketing ecosystem because brands want to leverage the communities and audiences those influencers have cultivated. We can loosely define influencers into a few categories:
Mega-influencers: influencers with over 1M followers, usually made up of celebrities, athletes, actors, rising stars, and other prominent public figures.
Macro-influencers usually have between 100K and 1M followers
Micro-influencers then are between 10K and 100K followers
Nano-influencers have the smallest audience, with somewhere between 1K and 10K followers.
Nano-influencers are a somewhat new addition to the influencer marketing world as their audiences have traditionally been seen as too small to make a difference. Many of us operate under the false assumption that the biggest names will be the most effective at promoting brands, but if we think about our own day-to-day purchase decisions, we’re often influenced by much smaller niche voices who are either part of our community or an expert that we found through research. These figures, with their smaller followings, are often seen as more trustworthy and relatable than the larger influencers who aren’t as approachable to the average consumer. This is because nano-influencers tend to be ordinary digital creators who have a knack for social media and have amassed a small audience along the way. Their followers consider them almost as peers because nano-influencers are highly authentic, presenting honest views of their lives as opposed to the high production value and glossy content from bigger influencers. The biggest benefit of working with nano-influencers is that they have a much higher engagement level than the other categories of influencers. This is because they have more time to respond and interact with their audience and develop meaningful connections. Nano-influencers generally inhabit a specific niche and have a deep understanding of their followers’ interests and preferences. This means they’re very skilled at creating highly specialized content that resonates deeply with their followers. This combination of high engagement rates and loyal audiences who trust the nano-influencer's recommendations translates into higher conversion rates when they endorse a product or service. Last, but not least, nano-influencers tend to be at the very beginning of their journey so they will be much more cost-effective to work with than bigger creators. Thanks to this lower cost, it’s much easier to get positive ROI on your nano-influencer campaigns without even taking into account the higher conversion rates. Partnering with these creators while they’re still up-and-coming allows for the brand and influencer to grow together and organically, even if they may require more coaching and mentoring upfront to get quality content. According to an internal survey conducted by influencer marketing agency Aspire, 70% of brands are already working with smaller creators. We expect to see this trend continue into the holiday season and beyond.
Data Privacy
Five years ago, data privacy was generally considered an afterthought; something your legal team dealt with that wasn’t on the forefront of our minds. Today, that’s changed dramatically as data privacy has become a priority for regulators, consumers, and businesses. Privacy laws such as the EU’s GDPR and California’s CCPA regulations spurred a chain reaction of other countries and US states passing their own laws. Examples include Brazil’s General Law for Data Protection, Egypt’s Law No. 151, and Canada’s Digital Charter Implementation Act. As of May 2024, 17 US states have passed data privacy regulations, with several more expected to join them. It’s clear the legislative pace will only pick up, but we’ve also seen regulators take a stronger stance on enforcement actions. In 2023, companies were fined over 2 billion euros for violations of the GDPR (more than the past 3 years combined). Large tech companies including Meta, TikTok, and X (formerly Twitter) have been fined over $3 billion since the passing of GDPR. In the US, the FTC is currently investigating several companies over their use of sensitive data, including geo-location, health, and children’s privacy. One of the most notable cases involves Kochava, a geolocation data broker that the FTC alleges has been selling data that can be used to trace the movements of individuals to and from sensitive locations such as health clinics, shelters, addiction recovery centers, etc. Depending on the outcome of this case, the use of geolocation data may be severely restricted going forward for targeting purposes.
Another regulatory area to keep an eye on is the development of AI policy by governments worldwide. After ChatGPT exploded in popularity, governments became wary about the need for large amounts of training data to train AI models which could infringe on data privacy. Italy temporarily banned ChatGPT in March 2023 because OpenAI was using personal data to train the model without asking for consent (including children’s data because there weren’t age verification tools implemented at that point). OpenAI did create a form for European users to opt out of their personal data being used to train the model and created an age verification tool, but this did little to assuage the EU’s concerns about data privacy. The European Data Protection Board created a ChatGPT task force to develop suitable AI regulations, with several other countries including the US, UK, and China drafting their own guidelines.
In late 2023, Google claimed that its plan to completely deprecate third-party cookies was on track for the second half of 2024. However, in April 2024 the company announced that they would be further delaying the sunset of third-party cookies until some time in 2025 (although the exact timing is yet to be determined). Why? The company acknowledged that regulators (especially the UK’s Competition and Markets Authority) and advertisers still had key concerns about the implications of removing cookies. The CMA is concerned that transitioning to the Privacy Sandbox will only strengthen Google’s position at the expense of its competitors. At the same time, agencies and marketers can rest a little easier knowing they have more time to figure out a plan to replace third-party cookies in their strategies. We won’t be holding our breath on when Google will actually phase out cookies, but expect the company to figure out a way to launch Privacy Sandbox with the blessing of regulators. Google still expects to complete this in 2025, but would anyone be surprised at all if they kept delaying it for a few more years? Only time will tell!
Can you believe we’re already over halfway through the year? Before you know it, it’ll be Labor Day, then Halloween, Thanksgiving; I mean it’s basically almost Christmas if you think about it. As we get closer to 2025, the marketing landscape continues to undergo a transformative journey, driven by new technologies, evolving social media platforms and heightened awareness of data privacy, among several other factors. GenAI has emerged as a truly exciting innovation, enabling marketers to create personalized content at an unprecedented scale and speed. Imagine having a virtual assistant who not only understands your brand but also crafts compelling campaigns while you sit back and enjoy your coffee.
Meanwhile, social media platforms are starting to see glimmers of a world where Facebook no longer dominates the advertising landscape. Just like in Formula 1, platforms are constantly jockeying for pole position to fight for marketer’s advertising dollars. Platforms including LinkedIn and YouTube are making big pushes for relevance as user behavior shifts to adapt to new features and algorithm changes that challenge us to keep up and stay agile. In the past few years, we’ve seen some seismic changes in social media including the rebranding of Twitter to X (plus a variety of other changes instituted since the acquisition led by Elon Musk), a potential US ban of TikTok, and the introduction of Threads to rival X to name just a few. Beyond social, new search methodologies (voice, social) show much promise as opportunities to gain an edge over the competition for the savviest marketers.
Data privacy is also taking center stage, reshaping how marketers approach targeting and other strategies. Although Google has yet again postponed the phasing out of third-party cookies to early 2025, there is no doubt that the average consumer is increasingly wary of their personal information being used by marketers. In this two-part article, we’ll cover the top marketing trends to keep an eye on going into 2025.
The Rise of LinkedIn
Believe it or not, the platform best known for uploading your resume and job hunting has been one of the hottest movers and shakers since the pandemic upended our lives. In 2023, the platform reached 1 billion global users (including an impressive 211M+ in the United States) and 500 million newsletter subscribers. LinkedIn doesn’t share daily or monthly average users, but the company reported that in the spring of 2023, users shared 41% more content than they had in the same period in 2021. That kind of organic growth is hard to fathom for a 22-year-old platform, but LinkedIn has managed to position itself as one of the last places to have professional and increasingly personal conversations due to the changes at X (more on this later). Users who previously would have posted on Facebook, Instagram, or Twitter are gravitating towards LinkedIn. Why? Terrified of TikTok eating their lunch, Meta has prioritized their products to emphasize interests instead of friends, moving personal sharing to semiprivate realms such as Stories or direct messages. X on the other hand, has simply become too unstable for many advertisers. Algorithmic changes, the introduction of the paid blue badge, amongst several other unpopular changes have caused brands to flee, bringing their ad dollars with them (a year after the acquisition, X’s ad revenues were down 55% YOY). LinkedIn also saw a huge boost during the pandemic when our personal and work lives became increasingly blurred. Workers felt more comfortable posting vacation pictures and emotional stories, content that previously would’ve been less socially acceptable, often captioned with a hail-mary, vague mention of something work-related (in case the boss sees). The product team at LinkedIn has ridden this wave, introducing new tools for creators and pushing “knowledge-based” content to great success.
Advertisers have taken note. While B2B marketers have long utilized LinkedIn in their campaigns, B2C marketers are increasing their spending, driving up ad prices and revenues (in some cases by as much as 30%). Business Insider expects LinkedIn’s US ad revenues to increase +14.1% to almost $5B by the end of 2024. While that’s only a small fraction of Meta and Google’s market share, it’s more than double that of Snap and around 4 times that of X. Despite the sky-high prices of impressions on LinkedIn, marketers report very impressive ROI on their campaigns (as much as 20% ROI). Penry Price, LinkedIn’s VP of Marketing Solutions, said that brands are investing more ad spend into LinkedIn because the platform is filled with decision-makers and key stakeholders in buying processes. We expect this trend to continue through the rest of 2024 and into 2025; as Bloomberg so eloquently put it, LinkedIn is cool now.
Stay up to date with product announcements and exclusive insights by following Northbeam’s Linkedin page here.
The Race to Replace Twitter
In October 2022, Elon Musk officially acquired Twitter and immediately began dramatically remolding the company to fit his vision. He started by firing several senior executives at Twitter including CEO Parag Agrawal and CFO Ned Segal, eventually laying off 80% of Twitter’s 7,500+ employees. He famously declared that only employees who wanted to engage in “maniacal work” should stay, leading to the resignations of hundreds of staffers (including a large chunk of the trust and safety team, spurring the flight of several large advertisers from the platform). Musk also took Twitter private, officially delisting it from the New York Stock Exchange in November ‘22, giving him the freedom to make changes as he saw fit without having to report to a board or shareholders. He went on to overhaul the verified system by launching a paid $ 8-per-month subscription, allowing anyone to obtain a blue check mark. This led to a wave of impersonations of public figures and brands, leading to mass confusion and misinformation. Musk also controversially reinstituted accounts of several notorious accounts that had previously been banned, including former president Donald Trump. The biggest change came in the summer of 2023 when Musk rebranded Twitter to X seemingly overnight in an attempt to turn the platform into more of an “everything” app similar to WhatsApp. Although it remains to be seen if these changes will benefit X in the long run, there’s no doubt that advertisers have fled the platform in droves. Reports that brand ads were being shown next to controversial and offensive content made marketing teams uncomfortable with continued spending on X (perhaps because the brand safety team had been axed). A year after Musk bought Twitter, ad revenue was down 55% year over year. Musk later named Linda Yaccarino, a former executive at NBCUniversal, as the new CEO of X in a bid to reassure and woo advertisers back to the platform.
This fall from grace for X has prompted a race to provide an alternative space for users fleeing “Twitter 2.0” for greener pastures. Meta, always keen to take competitor market share, launched Threads in July of 2023 as their take on a text-based social media platform. Threads started hot with a reported 30 million sign-ups within 24 hours of release before hitting 100 million members within the 1-week mark. Members aren’t the same thing as active users, but the latest update from Meta shows Threads has 130 million monthly active users. X currently has 550 million monthly active users, so Threads still has a way to go before dethroning the king, but Meta has made remarkable progress in just one year. Leadership has stated many times that they believe Threads has the potential to become the next billion-user platform. Although there are several other players in the space such as Mastodon and Bluesky, Meta’s expertise in monetizing social media platforms and partnering with advertisers makes them the smart bet to eventually come out on top of this race.
During the pandemic, TikTok emerged as a powerhouse and social media darling as the platform raced to over a billion monthly active users worldwide by September 2021. TikTok is largely responsible for the popularity of short-form video content in the US, primarily based on its powerful recommendation algorithm that’s adept at showing users more of what they enjoy. Owned by Beijing-based ByteDance, TikTok has courted plenty of controversy in the past as politicians have accused of it being a tool for propaganda and a national security risk. Many American lawmakers are worried that the Chinese regime could “weaponize” TikTok and spy on US citizens including military and government personnel. Many universities and government agencies at the local, state, and federal levels began enacting bans on the app from official work devices. The Trump administration attempted to force a sale, but efforts were largely unsuccessful until April 2024 when President Biden signed the “Protecting Americans from Foreign Adversary Controlled Applications Act.” The bill stipulated that ByteDance has 9 months (until January 2025) to find a buyer for TikTok with the possibility of a 3-month extension if meaningful progress is made toward a sale. As of the writing of this article (summer 2024), TikTok has responded by suing the US government on First Amendment grounds. The case will almost certainly take more than a year to resolve, but as marketers, we can’t afford to be caught without a contingency plan in case TikTok does get banned by early January of next year. Creators are already beefing up their Instagram Reels presences in response, but YouTube Shorts has emerged as a promising contender to the big two. In early 2023 Shorts crossed the 50 billion daily views mark, which although still short of Meta’s combined 140 billion daily views across Facebook and Instagram, is starting to garner significant traction with advertisers. Interestingly enough, YouTube seems committed to longer-form content with the intent of using Shorts to drive engagement towards that content. The company is intent on chasing TV ad dollars instead of just digital ones given that YouTube owns more than 50% of ad-supported streaming watch time on TVs. This runs counter to the fact that Shorts isn’t designed for a TV experience (it’s optimized for mobile), but it’s useful for creators to have the option to create shorter content that can boost traffic to longer videos. While we’ll have to see who comes out on top of this short-form video war, this signals that YouTube believes its core value is in the longer video content that’s historically been its bread and butter.
The Elephant in the Room: GenAI
We would be remiss to talk about marketing trends without mentioning GenAI. Ever since ChatGPT was launched in late 2022, marketers have been salivating over the potential of using GenAI to create text, images, video, and audio. With GenAI, marketers can create personalized content at scale, producing tailored ads, social media posts, email campaigns, etc. that will resonate more deeply with target audiences. These AI-driven tools can analyze vast amounts of customer data to generate insights into behavior, preferences, and trends, allowing for more precise segmentation and messaging. Beyond that, GenAI can significantly streamline the content creation process, reducing the need to hire expensive agencies for good old-fashioned photo and video shoots. It can write decent copy, design graphics, and even generate videos, freeing up human resources to focus on strategy and creative oversight. GenAI has even empowered us to conduct A/B testing and optimize campaigns faster and more effectively than before. However, while content creation was the first widely adopted use case, another development could be more impactful to a wider range of users. In May 2024, Google rolled out AI Overviews: its AI-powered search engine (formerly known as Search Generative Experience). AI Overviews is designed to improve user interaction and engagement by offering conversational and contextually relevant search results in a UI similar to ChatGPT’s prompt system. When users search on Google using AI Overviews, they will no longer see highly-ranked links first. Instead, they will first get a summarized overview of the prompt before scrolling down to see which links contributed to that answer. As a result, AI Overviews tends to prioritize summarized content over publisher links which now get placements below the Overviews chatbox. Publishers have seen a substantial decline of up to 60% in organic search traffic, translating to an estimated $2 billion loss in ad revenue within Google’s pilot version of AI Overviews. It’s too soon to say for sure, but the release of Overviews may upend all of our traditional SEO strategies if we’re to stay relevant in this new era. Since GenAI can generate large amounts of low-quality content, we’ll likely need to focus on high-quality content that provides clear and valuable information to get our content featured in Contextual Summaries. It remains to be seen if SEO tactics will be as effective if getting highly-ranked pages is not nearly as impactful due to AI Overviews eating up most of the real estate on screen.
2024 has shaped up to be an exciting year already, and we can’t wait to see what 2025 brings us. Is your team keeping an eye on these trends? If so, watch out for Part 2 of our 2025 Marketing Trends deep dive, coming soon!
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