Northbeam&#x27;s Definitive Guide to Q5 2025

Leverage data to improve your marketing strategies

What Is Data-Driven Marketing? A Complete Guide to Transforming Your Marketing Strategy with Data

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.

Creative performance analysis → content optimization

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.

March 27, 2024

Transform Your Marketing Performance

Why You Should Care About MTA, MMM, and Incrementality

How they Differ, and How they Work Together

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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?

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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.

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August 8, 2025

Learn why ROAS matters and how to maximize your returns

What Is ROAS? A Beginner’s Guide to Return on Ad Spend

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.

ROAS in Action: MyHD

Learn how MyHD improved their ROAS by +84% with data-driven decision making, enabled by Northbeam.

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.

February 14, 2024

Not All Data is Created Equally

What Makes Northbeam Data Different?

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?

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March 18, 2024

Everything You Need to Know When You Try Out Northbeam

What to Expect When Switching to Northbeam

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.

Why do my metrics look different?

Northbeam’s data is different, and there are a lot of great reasons for that: we wrote a whole blog post about it.

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.

‍Get in touch with our team for more information on what you can expect when switching to Northbeam.

November 13, 2024

Unlock the Power of Pixel Tracking for Better Marketing Insights

What is Pixel Tracking?

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.

What’s the future of pixel tracking?

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.

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March 11, 2024

Uncovering Accurate Insights for Modern Marketers

What is Multi-Touch Attribution (MTA)?

Multi-Touch Attribution, or MTA, is a method of marketing attribution that accounts for all of the different digital touchpoints and activities in the customer’s journey. Different MTA models will assign a different weight or “credit” to different touches to give you an idea of the value that each is adding on the path towards a final conversion.

But let’s break it down even further. In this guide, we’ll cover the ins and outs of MTA and attribution in general so you can ace this particular marketing acronym.

What is marketing attribution?

Attribution is, at its core, the act of assigning an effect to a certain cause. If you’re satiated, you can attribute that to the meal you just ate. If you’re cold, you can attribute that to the wintery weather. Easy.

But if someone just made a purchase on your website, what can you attribute that to?

The answer isn’t as straightforward when it comes to marketing attribution, but it’s highly important: if you can’t do attribution properly, you can’t optimize your spend towards the channels and activities that work.

Many different attribution models exist to give you data on what’s leading to that final conversion. The Northbeam platform hosts six different attribution models (including two MTA models) so you can visualize what your data looks like from different angles.

What are the different attribution models?

There are straightforward attribution models, and then there are more complex ones like MTA in all its variations. Northbeam measures all of them, and our proprietary machine learning models are trained to cut through the complexity and deliver industry-leading MTA accuracy.

On the simpler side of things, you have the following attribution models:

First touch attribution models give all the credit to the first interaction a customer has before a conversion. This is useful for understanding how leads enter the top of your funnel.
Last touch attribution models give all the credit to the last interaction a customer has before a conversion. This is useful for understanding which bottom-of-the-funnel activities are driving success.
Last non-direct touch attribution models are a variation of last-touch models that exclude direct traffic. Direct traffic means that someone directly visited your website. Imagine that someone sees your Instagram ad today, and then in three days they type in your website to make a purchase. A last non-direct touch attribution model would still attribute that conversion to Instagram, while a typical last touch model would call it direct traffic.

Things get more complex — and more interesting — when we look at MTA models.

The classic MTA model is linear: it spreads credit and revenue evenly across all the touchpoints in a customer’s journey to a conversion.

But that doesn’t reflect reality. If you see an Instagram ad, a Google ad, and a marketing email in the week before making a purchase, it’s likely that each of these touches had a different and distinct effect on your final buying decision.

Northbeam has two proprietary models to help allocate credit and revenue where its due:

Our Clicks Only MTA model specifically excludes certain touchpoints like direct visits, organic search, paid branded search, and email/SMS if there are other touchpoints in the customer’s journey in order to put more weight on activities where a customer actively engages with your campaigns.
Our Clicks and Views MTA model builds on the Clicks Only model by also giving credit to campaign views at any point during the customer’s journey; it includes both active and passive customer engagement with campaigns.

In addition to the different types of attribution models, there are also attribution windows to take into account. Northbeam can offer you a variety of attribution windows, from 1-day to 90-day all the way to an indefinite attribution window so you can recognize campaign revenue at different cut-off points as well as total customer lifetime value (LTV).

Why MTA?

There are dozens and dozens of ways to reach a potential customer in today’s marketing environment, and you as a marketer have to be able to make informed decisions on where to spend your limited budget.

MTA lets you assess which channels are responsible for which revenue so you can stay on top of performance and make the best marketing decisions with the best data available.

But that doesn’t mean that other attribution models aren’t helpful. We recommend regularly referencing other attribution models as well as MTA so you can get a full picture of performance.

Having multiple ways to visualize your data lets you continue to optimize and test to find what works best for you and your organization. Data is power, after all.

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May 15, 2024

A comprehensive overview of MMM

What is Media Mix Modeling (MMM)?

What is Media Mix Modeling (MMM)?

A History & Modern Application with Northbeam

In the ever-evolving landscape of marketing, the ability to measure the impact of various marketing activities is crucial. One of the most sophisticated and iconic approaches to measuring this impact is through Marketing Mix Modeling (MMM); which is more commonly referred to today as Media Mix Modeling. This method has proven its worth over decades, adapting to the changes in media consumption and technological advancements. This blog post dives into the history of MMM, explores its fundamental principles, and examines how modern companies like Northbeam are utilizing MMM to optimize marketing strategies in today's digital age.

The Origins of Marketing Mix Modeling

MMM has been a part of the marketing arsenal for more than 70 years. However, MMM began to gain significant traction in the 1960s when companies like Kraft Foods pioneered the use of this analytical method to launch products such as Jell-O. During this era, marketers had limited but rapidly-growing channels like television and magazines, and MMM allowed them to analyze the effectiveness of varying advertising levels across different regions and times of the year.

Fun Fact: Neil Borden, a famed professor of advertising at the Harvard Graduate School of Business Administration from the 1920’s to the 1960’s, popularized the term "marketing mix" in 1949 and laid the foundational theory underlying MMM. He, along with colleague James Culliton, came upon this term through describing marketing executives as “mixers of ingredients”—adjusting components like product, price, place, and promotion [the 4Ps] to meet customer demands. This concept evolved into MMM, which systematically quantifies the impact of these elements on sales and revenue.

How Does Media Mix Modeling Work?

At its core, MMM is an analytical approach that uses statistical methods to estimate the effectiveness of different marketing activities. By examining historical data, MMM isolates the impact of various marketing efforts on overall business performance. This model operates on two primary components:

Inputs

MMM considers a broad set of inputs, including:

Marketing variables: Such as advertising spend, promotions, and product distributions.
Environmental factors: Like economic conditions, seasonality, and competitive actions.
Data quality: Ensuring the inputs are reliable and detailed enough to offer actionable insights.

Outputs

The outputs of an MMM are predictions and evaluations of key performance indicators (KPIs), typically focusing on metrics like sales volume or revenue. These outputs help marketers understand the return on investment (ROI) of different marketing strategies and guide future spending decisions.

Model Philosophy

MMM traditionally used linear regression models which assume that each variable has an independent and constant impact. However, modern MMM approaches often employ more sophisticated techniques, such as machine learning and AI, to capture the complex and dynamic interactions between variables.

Practical Applications of MMM

Media Mix Modeling can be applied to various strategic marketing activities:

1. Scorecards

These are retrospective analyses that help marketers understand what worked and what didn’t. They typically evaluate the ROI of past investments and are used to plan future marketing strategies.

2. Forecasting

MMM is used to simulate different marketing spend scenarios to predict their potential impacts on business outcomes. This helps in budget allocation and financial planning, answering questions like "What could be the impact if we increase digital spending?" This is where most marketers seeking “incrementality” would use MMM.

3. Optimization

Beyond forecasting, MMM helps in making real-time adjustments to marketing strategies, ensuring that budgets are allocated to the most effective channels. This involves understanding the law of diminishing returns and optimizing for long-term growth and profitability. This is where marketers will look at things like “cost curves”: visualizations of diminishing returns (and forecasted results) based on specific levels of spend.

MMM+ with Northbeam

Today, companies like Northbeam are revolutionizing MMM by integrating advanced technologies such as AI and machine learning. Northbeam’s MMM+ tool exemplifies modern MMM practices, offering features that are both innovative and practical:

Real-Time Data Analysis: Unlike traditional MMM that often relied on outdated data, Northbeam’s tool analyzes weekly data for more timely insights.
Demystifying Spend: Moving beyond the traditional understanding of where to place your dollars, the Northbeam MMM+ solution provides the ideal budget blend across all channels to maximize results for any and all KPIs you choose.
Customizable Outputs: Northbeam’s MMM+ allows companies to focus on specific KPIs that align with their business goals, providing tailored budget recommendations based on these targets.

The Complementary Role of MTA

In addition to MMM, Northbeam recognizes the importance of Multi-Touch Attribution (MTA) in understanding the digital customer journey. While MMM provides a macro-level view of marketing effectiveness, MTA offers granular insights into the performance of individual campaigns and channels. Together, they enable a holistic view of marketing performance, guiding strategic decisions that optimize both online and offline channels.

Conclusion

Media Mix Modeling has come a long way from its early days with Kraft Foods and magazine catalogs. Its evolution has mirrored the complexity of the marketing world, growing from simple analytical tools to sophisticated models that incorporate real-time data and advanced statistical techniques. As companies like Northbeam continue to push the boundaries of what's possible with MMM, marketers are equipped better than ever to make informed decisions that drive business success in the digital age. Whether you are assessing the impact of historical marketing efforts or planning future strategies, MMM remains an indispensable tool in the marketer's toolkit.

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June 21, 2024

Understand and enhance your marketing ROI

What is Marketing ROI and How Can You Improve It?

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.

Image of a man utilizing applovin on his phone and measuring the impact of his adds using Northbeam, the multi touch attribution solution for profitable growth

April 29, 2025

Why DTC brands are flocking to AppLovin, and how to make it work for you

What is AppLovin and How to Leverage it For Marketing Success

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.

June 27, 2024

Capture the right data for informed marketing decisions

What is Marketing Intelligence Software?

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.

January 14, 2025

Track these essential metrics to drive sustainable growth

Top Marketing Metrics to Track for Long-Term Success

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.

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August 22, 2025

The Critical Role of Attribution Models in Unlocking Growth

What Is Incrementality In Marketing? A Guide to Measuring True Campaign Impact

Brayden Cruz

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.

Leading marketing teams leverage a combination of incrementality, MMM, and MTA to build a well-rounded measurement strategy:

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?

Here’s a step-by-step guide to launching your first incrementality test.

Step 1: Identify High-Impact Channels to Test

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.

Northbeam presents a marketer’s guide to customer journey mapping, featuring a relaxed professional at a desk, symbolizing strategic planning and marketing success.

March 4, 2025

A five-step, strategic approach to customer journey mapping

The Marketer’s Guide to Customer Journey Mapping

In today’s competitive marketplace, understanding your customers isn’t just a luxury; it’s a necessity. Marketers who prioritize the customer journey can create meaningful, lasting connections that drive conversions and build loyalty.

In this guide, we’ll break down what customer journey mapping (CJM) is, why it matters, and how you can create your own journey map to enhance your marketing strategy.

What is Customer Journey Mapping?

Customer journey mapping (CJM) is the process of visualizing the path a customer takes when interacting with your brand, from initial awareness to post-purchase engagement.

At its core, a customer journey map identifies key touchpoints where customers interact with your business, such as social media, email campaigns, website visits, and customer support.

These maps provide a clear picture of the customer’s experience, highlighting pain points and opportunities for improvement.

For example, a simple journey map might look like this:

A customer sees your social media ad
They click through to a landing page
They subscribe to a newsletter
They make a purchase from the newsletter
They reach out to your brand for post-purchase support
They receive a discount code and buy an accessory for the product they purchased

By understanding these steps, marketers can refine their strategies to meet customer needs at every stage.

The Benefits of Customer Journey Mapping

Customer journey mapping delivers several key advantages, including:

Enhanced customer experience: By identifying and addressing pain points proactively, you can create a smoother and more enjoyable experience for your customers.
Increased ROI: Aligning your marketing efforts with customer needs ensures that your resources are allocated effectively, leading to better outcomes.
Cross-channel consistency: Journey maps help ensure a cohesive experience across platforms, from social media to in-store visits.
Data-driven decision-making: Leveraging insights from your map enables you to make informed decisions that optimize performance.

As an example, let’s consider a company that maps its customer journey, starting from awareness to consideration and finally to conversion. This company uses analytics tools and surveys to track customer behavior between journey points to uncover areas of drop-off or pain.

Let’s take a look at these benefits in action.

Enhanced customer experience: After identifying that most visitors leave within 30 seconds of landing on their site, the company creates mobile-friendly landing pages that better align with their ad messaging. These changes reduce bounce rates, leading to a smoother and more enjoyable experience for customers.
Increased ROI: The company discovers that detailed product information is missing on their site, causing hesitation among shoppers. By adding ingredient details, comparison charts, and customer reviews, they increase product page conversions by 15%, ensuring their resources drive better outcomes.
Cross-channel consistency: Aligning their social media ads with optimized landing pages ensures the company’s messaging is cohesive across platforms, building trust and encouraging visitors to progress through the customer journey.
Data-driven decision-making: By continuously analyzing customer behavior, the company pinpoints where drop-offs occur, such as during checkout. They simplify the checkout process and introduce new payment options, resulting in a 9% improvement in conversion rates.

5 Steps to Create an Effective Customer Journey Map

Creating a customer journey map may seem daunting, but breaking it into manageable steps can simplify the process:

1. Define Your Goals

What do you want to achieve with your journey map? Whether it’s improving conversion rates or enhancing post-purchase engagement, setting clear objectives will guide your efforts.

2. Identify Your Customer Personas

Understand who your customers are by developing detailed personas. Consider factors like demographics, behaviors, and pain points at a basic level. To level up your personas, you can incorporate more detailed information like income, personality traits, favorite channels, and more.

3. Outline Key Touchpoints

Map out the critical moments where customers interact with your brand, such as visiting your website, opening an email, or speaking with support.

4. Gather Data

Combine qualitative data (e.g., customer interviews) with quantitative data (e.g., website analytics) to create a comprehensive view of the journey, including time between steps and critical drop-off points.

5. Map the Journey

Plot the customer’s path across different stages: Awareness, Consideration, Purchase, Retention, and Advocacy. Use visuals to make the journey clear and actionable, and outline the distinct steps that go into each stage.

6. Analyze and Act

Identify gaps and opportunities for improvement. Then, prioritize and implement changes to optimize the journey at each stage.

Common Mistakes to Avoid

While customer journey mapping is a valuable tool, there are common pitfalls to watch out for:

Overcomplicating the map: Focus on clarity and actionable insights rather than including every minor detail.
Neglecting data insights: A journey map is only as good as the data behind it. Ensure your insights are grounded in accurate, real-time information.
Failing to account for cross-channel Interactions: Customers often switch between channels, and your map should reflect this complexity.
Treating the map as static: Customer behaviors and preferences change over time. Regularly update your journey map to stay relevant.

Map with Confidence

Customer journey mapping is foundational for marketers looking to create seamless, impactful experiences and invest in continuous marketing improvement.

By understanding the path your customers take, you can refine strategies, enhance customer satisfaction, and drive sustainable growth in a way that aligns with your target audience’s wants, needs, and unique pain points.

Use this guide to drive your customer journey mapping efforts, and get in touch with the Northbeam team for personalized support and guidance.

April 22, 2024

Navigating rising digital ad costs with Northbeam

The Future with YouTube Advertising

Craig Graham

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.

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You can't rely solely on KPIs inside Google Ads

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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.

Combine Google Ads reporting with Northbeam.

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.

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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):

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▪ 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.

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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)

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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.

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This Blog was written by contributing guest writer Craig Graham. Check him out below:
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Craig Graham

CEO/Founder at Grayvault Consulting

https://www.grayvaultconsulting.com/

www.linkedin.com/in/craig-graham-grayvault

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June 4, 2024

How modern marketers can best harness the power of AI

The Marketer’s Guide to AI

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.

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February 13, 2025

The Big Game, Big Spends, and Even Bigger Marketing Moves

Super Bowl 2025 Advertising Trends & Performance Analysis

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.

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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.

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December 3, 2023

A look into this year's performance compared to last year.

Performance Benchmarks: Black Friday Cyber Monday 2023

Bryan Bumgardner

Black Friday 2023 has come and gone. So how did it go, and how does your brand stack up?

Depends on who you ask.

Shopify self-reported that merchants drove a record high $9.3 billion in Black Friday Cyber Monday sales, a 24% increase year over year.

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

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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.

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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.

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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?

Call it precognition, foresight, straight-up magic: were our assessments in the Q4 White Paper about this holiday season accurate?

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.

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April 15, 2024

Leveraging Northbeam for scalable efficiency

Serious Data For Serious Growth: How Northbeam Enables HexClad To Stay Efficient with Scale

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.

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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.
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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.
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"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.”
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Maturity brings a much harder battle

After two years in a row 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.”
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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 100% in 2022 and another 100% 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.

@hexclad Making pasta from scratch? We're in! Saffron Brodetto with clams and mussels made by Chef Dafne Mejita. Get the deets on the dish below and check our pinned comment for some pasta making 101 🤌 #saffron #brodetto #clampasta #hexclad #recipes ♬ original sound - OMA

A top-of-funnel beast

“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.”

May 29, 2024

A guide to measuring marketing impact

The 2024 Guide to Incrementality

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

Incrementality is often grouped together with other popular measurement methodologies including multi-touch attribution (MTA) and marketing/ media mix modeling (MMM). While all 3 can help marketers understand the effectiveness and impact of their efforts, they evolved for different reasons and thus are best suited for different things.

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.

November 19, 2024

How probabilistic methods can help you navigate third party cookie deprecation

Probabilistic vs. Deterministic Approaches in a Cookieless Future

The potential deprecation of third-party cookies has sent shockwaves across the industry. Marketers are hustling to adapt as the tools and methodologies they’ve relied on for years are at risk of losing their potency. Whether that cookieless future is a year away or five years away, the best marketers are taking steps today to set themselves up for success in the long run.

This upcoming adaptation has also fed an ongoing debate about probabilistic vs. deterministic approaches in marketing strategy. In this guide, we’ll talk about both approaches, discuss why they matter, and cover how a probabilistic approach can help marketers prepare for a cookieless future.

The cookie crisis

Before we get into the nitty-gritty, let’s take a step back to discuss the potential deprecation of third party cookies. While Google Chrome won’t be deprecating cookies just yet, there are indications that we’re moving in that direction sooner rather than later. User preferences and governmental regulations are indicating that privacy is and will remain top-of-mind in the coming years.

This shift poses a threat to how digital advertising has traditionally been measured and managed. Deterministic measurement, which relies heavily on cookie data, is particularly vulnerable to this change.

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According to eMarketer, almost 90% of browsers could become cookieless long term. In this potential future, fewer than 20% of users will opt in to sharing cookies when browsing online. This drastic reduction in available data may lead to diminished performance for campaigns that rely solely on deterministic models.

For marketers, this could be the difference between thriving in a competitive environment and seeing their performance decline.

Probabilistic vs. deterministic measurement

Let’s dive into definitions.

Deterministic measurement relies on direct user actions and data points to track and measure behavior. It bases outputs on actual user behavior and builds its measurements off of cookie data, login information, and device IDs. While this method is highly accurate, it is also highly dependent on the availability of direct data, which will become increasingly scarce as security measures proliferate.

Probabilistic measurement, on the other hand, uses algorithms and statistical models to infer user behavior based on aggregated data points. Instead of relying on exact matches, it identifies patterns and correlations to estimate outcomes. While it might not offer the same level of precision as deterministic measurement, it is far more resilient in a world where direct data points are dwindling.

The growth of probabilistic measurement

As cookies disappear, so too does the feasibility of relying solely on deterministic measurement. Marketing intelligence platforms like Northbeam have taken this inevitable shift to heart and built their analytics platforms off of probabilistic models.

By leveraging advanced machine learning models, probabilistic measurement can continue to provide valuable insights even when direct data is limited. It’s adaptable, scalable, and, most importantly, future-proof.

The best thing about machine learning-based modeling is that it gets more accurate over time as it trains on massive amounts of available marketing touchpoints — we’re talking billions of data points a day about how users engage with your product and behave online. With a longer view, we can expect probabilistic measurement to grow in accuracy and gain even more value as a go-to tool.

The digital vs. traditional divide

Compounding the challenge of accurate measurement is the growing divide between digital and traditional media spending. According to recent projections by eMarketer, U.S. total media spending in 2024 is expected to reach $389.49 billion, with digital accounting for a staggering $302.77 billion of that total. In contrast, traditional media is expected to bring in $86.72 billion.

This shift from traditional to digital media underscores the importance of effective digital measurement. As more dollars flow into digital channels, the stakes for getting measurement right are higher than ever. Marketers can’t afford to rely solely on deterministic models as digital spend continues to rise — a marketing strategy built on unreliable or diminishing data is not scalable in the long- or even medium-term.

It’s time to adapt

What does all of this mean for you as a marketer? Now more than ever, you need tools and platforms that are built to not just survive but thrive in a cookieless world. Unlike traditional MTA solutions that rely heavily on deterministic data, Northbeam’s approach is and has always been rooted in probabilistic measurement. This ensures that your campaigns remain effective, even as the data landscape shifts beneath your feet.

By educating yourself on probabilistic measurement and anticipating the deprecation of cookies, you can stay ahead of the curve and remain competitive in tomorrow’s marketing ecosystem. This isn’t just about improving performance today, it's about setting yourself and your organization up for success in the long term. A future-proof strategy is the best strategy.

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January 22, 2024

Northbeam's resilience amid third-party cookie deprecation

How Will Cookie Deprecation Affect Northbeam? An Explainer

Jay Li

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.

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Northbeam showcases a futuristic city with holographic figures, drones, and digital interfaces, guiding e-commerce brands on Meta's new sensitivity category restrictions.

January 27, 2025

How to thrive ahead of new privacy restrictions

Meta’s New Sensitivity Category Restrictions: A Guide for E-Commerce Brands

Meta’s new sensitive category restrictions are set to reshape the advertising landscape for e-commerce brands in industries like health, wellness, finance, and politics.

These changes, designed to enhance user privacy and align with global regulations, could have an even greater impact on affected brands than iOS 14 did in 2020.

For advertisers, these restrictions mean losing access to important tools for bottom-funnel optimizations, such as tracking purchases and sign-ups, and limitations on precise audience targeting.

While the challenges of these new restrictions are significant, brands can adapt by revisiting their marketing strategies and leveraging platforms like Northbeam to maintain an edge.

We’ll break down what these changes mean, how to respond effectively, and how Northbeam can support your brand through this transition.

What are Meta’s new sensitivity category restrictions?

Meta’s policy update introduces new limitations for advertisers in “sensitive” industries like health, wellness, and finance in order to align with global privacy standards.

Companies in these categories will start to see these changes in 2025:

Limited access to lower-funnel events like Add to Cart (ATC), Purchase, and Sign-Up. Tools like the Meta Pixel and Conversions API will be significantly constrained for affected brands.
Loss of precise targeting options, making it harder to personalize ads and reach specific audiences.

With the loss of these tracking options, advertisers will have to get creative to maintain or improve ad performance.

There is also the risk of restrictions due to misclassification, which means your company should be careful about the content it posts if it doesn’t want to be classified under the categories expanded on in the section below.

Which industries are affected?

The restrictions target brands in:

Health and Wellness: Supplements, skincare for specific conditions, medical devices, and telehealth.
Finance: Loans, investments, and credit repair services.
Sexual Wellness: Products like condoms, personal lubricants, and sexual health education platforms.
Gambling and Alcohol: Online casinos and alcohol delivery services.
Additionally, brands on the periphery — such as general wellness or fitness — may also face new limitations and challenges.

How can I adapt to this new reality?

Brands can adapt to these changes by revising their strategies and focusing on areas they can control.

First, it is crucial to leverage first-party data. Building and nurturing owned audiences through email and SMS campaigns can create more direct and reliable customer connections. Additionally, using tools like Northbeam to analyze and activate first-party data will allow for personalized campaigns without over-reliance on Meta’s ecosystem.

Second, brands should adjust their creative approach. Avoiding sensitive language and focusing on aspirational benefits or general wellness themes can make campaigns more privacy-safe while still engaging audiences. Clear and creative messaging will play a significant role in maintaining audience interest.

Third, diversifying ad spend is essential. Brands should explore opportunities across channels so they can test, validate, and extend learnings to Meta where insights may be lacking. Testing new channels and diversifying your spending is a smart move in general — and when your platform analytics are limited, it can provide much-needed insights.

How can Northbeam help?

Northbeam provides powerful tools to help brands navigate Meta’s restrictions while maintaining data-driven marketing strategies.

Northbeam’s multi-touch attribution tool offers a complete view of your customer journey across channels, reducing reliance on Meta’s tracking and providing deeper insights into performance. By seamlessly integrating first-party data, Northbeam helps brands better understand their customers and create targeted campaigns that don’t rely solely on Meta’s ecosystem.

Additionally, Northbeam’s predictive analytics enable brands to model outcomes and optimize ad spend, even in a more restrictive environment. With omnichannel attribution capabilities, brands gain clarity on how each marketing channel contributes to revenue, allowing for more effective budget allocation and identification of growth opportunities.

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Ready to adapt? Get in touch with our team to learn more.

July 10, 2024

Embrace open web strategies in the evolving digital ad landscape

Moving Beyond Walled Gardens: The Shift in Digital Advertising

Moving Beyond Walled Gardens: The Shift in Digital Advertising

It’s no secret that a small handful of platforms have historically controlled the lion's share of information when it comes to digital advertising. It has been an incongruous game of chess between tech giants and brands trying to scale. But, the winds may finally be changing.

In the ever-evolving landscape of e-commerce, brands are increasingly taking control of their advertising decisions by leveraging their own data and first-party measurement solutions, like Northbeam. According to recent reporting by Adweek, this shift marks a significant departure from the traditional reliance on the walled gardens operated by tech giants such as Meta and Google.

The Decline of Walled Gardens

Adweek's conversations with four digital advertising firms reveal a growing trend: clients are moving away from the walled gardens of major platforms and turning to first-party services. This move raises the question: Are walled gardens withering away? While it's premature to declare their demise, the advertising industry is certainly experiencing a transformation.

Historically, walled gardens—digital ecosystems where the platform controls the user data and ad inventory—were seen as gold mines for platforms, media outlets, and hardware companies. These entities leveraged their substantial user data to attract advertisers and drive revenue. However, the landscape is shifting.

Financial Milestones and Market Share

Despite forecasts predicting that ‘US walled garden programmatic digital display ad spending’ will surpass $100 billion this year, there are signs of a change. For the first time since tracking began in 2017, walled gardens lost market share in programmatic ad spending last year, and this decline is expected to continue.

One significant factor contributing to this shift is the rise of retail media networks (RMNs). RMNs allow advertisers to spread their messages across various digital storefronts, reaching a broader audience. This flexibility and reach are increasingly appealing to brands, making the restricted nature of walled gardens less attractive.

Retail media's success underscores a growing distaste for the limitations imposed by walled gardens. Advertisers seek more open-ended approaches, driven by distrust of major ad platforms and a desire for greater control and transparency.

The Cost Factor

The financial aspect is also a crucial consideration. The cost of running ads on major platforms like Google and Meta has surged, prompting budget-conscious brands to reassess their strategies. These rising costs, coupled with various controversies that have plagued these platforms, are driving brands to explore alternatives.

Trust Issues

Over the past year, both Google and Meta have faced numerous challenges, including scandals that have cast doubt over their quality control, brand safety, and return on investment. These issues have further eroded trust in the ad duopoly, pushing brands to seek more reliable and transparent solutions.

For instance, Meta’s Advantage+, an AI-powered tracking solution designed to mitigate signal loss caused by Apple’s AppTrackingTransparency change, has faced criticism. Accusations of inflated metrics and budget-draining practices have not helped Meta's cause, contributing to the growing disillusionment with walled gardens.

As the advertising industry evolves, a more open-ended approach is gaining traction. Brands are increasingly relying on their own data and first-party measurement solutions, reducing their dependence on these major platforms. This shift signifies a move towards greater autonomy and control over advertising strategies.

Embracing First-Party Solutions & Diversification

While walled gardens are hardly disappearing entirely, one of the key trends in shifting away from them is the adoption of first-party measurement solutions. Northbeam, for example, is platform agnostic and provides brands with more accurate and transparent metrics, helping them make more informed, more nuanced decisions, daily. By relying on first-party data, brands can avoid the potential biases and limitations associated with walled gardens and create more personalized and targeted advertising campaigns, enhancing their effectiveness and ROI. Brands such as HexClad and Kizik have been able to leverage Northbeam in this precise way. Achieving significant growth that would not have been possible through the gated and biased channels of in platform metrics only.

Diversification is another strategy brands are adopting. Rather than relying solely on major platforms, brands are exploring a variety of advertising channels. This includes social media platforms, influencer marketing, programmatic advertising, and retail media networks. Through this method, a broader audience can be reached and any previous reliance on one single platform to drive conversions can be spread across multiple audience pools.

Emphasizing Transparency and Trust

Transparency and trust are becoming increasingly important in the advertising industry. Brands are seeking partners and platforms that prioritize these values, providing clear and honest metrics. This emphasis on transparency helps build stronger relationships between brands and their customers, fostering loyalty and trust.

Conclusion

The advertising industry, and e-commerce in general, are at a crossroads with a decision needing to be made. While walled gardens have long been the dominant force, a shift is clearly underway. With the advent of solutions like Northbeam’s MTA tool and MMM+ offering, brands are taking control of their advertising strategies, leveraging their own data and first-party solutions to make more informed decisions. The rise of retail media networks, increasing costs of major platforms, and distrust of gatekept data are all contributing to this transformation and transition.

As the industry continues to evolve, transparency, control, and cost-effectiveness will remain key priorities for brands. While walled gardens may not disappear entirely, their share of ad spending is likely to diminish. The future of advertising lies in a more open-ended approach, where brands have greater autonomy and flexibility to reach their target audiences effectively.

In this new landscape, the ability to adapt and innovate will be crucial. Brands that embrace these changes and prioritize transparency and trust will be well-positioned to succeed in the ever-evolving world of digital advertising.

Set up a demo with Northbeam today to see how you can evolve past in-platform metrics, and stop leaving money on the table.

How to Not Get Fired: A 5-Step Guide for CMOs

February 16, 2024

Secure your role amid a shifting marketing landscape

How To Not Get Fired: A 5-Step Guide for CMOs

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.

And those who care, use Northbeam.

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June 10, 2024

Evaluating the best approach to implementing MMM

Media Mix Modeling (MMM): Build or Buy?

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.

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May 6, 2024

Media Efficiency Ratio (MER) & Return on Ad Spend (ROAS): What are They & What’s the Difference?

Deciphering MER and ROAS

Media Efficiency Ratio (MER) & Return on Ad Spend (ROAS): What are They & What’s the Difference?

A Definitive Guide with Northbeam

In the ever-evolving landscape of digital marketing, metrics are the compasses that guide marketers toward success. Two of these crucial metrics are Media Efficiency Ratio (MER) and Return on Ad Spend (ROAS). While both metrics aim to assess the Return on Investment (ROI) of marketing efforts, they offer distinct insights and serve different purposes in a marketing strategy. This blog post will delve into the differences between MER and ROAS, how they are calculated, and when to use each to benchmark success.

What is Media Efficiency Ratio (MER)?

MER stands for Media Efficiency Ratio. It is a broad measure that looks at the total revenue generated versus the total advertising spend over a specific period. Simply put, MER is calculated as:

MER = Total Advertising ÷ Spend Total Revenue

MER provides a macro view of advertising performance, primarily focusing on cash flow—money in versus money out. It does not take into account the time it takes for a customer to convert after seeing an ad, known as conversion lag. Therefore, MER is often used in scenarios where understanding immediate cash flow impact is crucial, such as in cash accounting frameworks where revenues and expenses are recognized when they are received or paid. Brands like HexClad are using Northbeam to scale their ad spend by 100%, and improving their MER substantially in the process.

What is Return on Ad Spend (ROAS)?

ROAS stands for Return on Ad Spend. Unlike MER, ROAS provides a more granular look at the efficiency of specific marketing campaigns. It measures the amount of revenue each dollar of ad spend brings in, regardless of the time horizon. ROAS is often calculated at a cohort level, tracking the revenue attributed to a campaign over time. Here's a typical example of how ROAS might evolve:

Day 1 (Campaign launch): $100,000 spent, $0 revenue = 0 ROAS
Day 2: $75,000 revenue earned, ROAS = 0.75
One week later: $150,000 revenue, ROAS = 1.5

This metric is particularly useful in accrual performance accounting, where revenues and expenses are recognized when they are incurred, regardless of when the money is exchanged. ROAS includes considerations of conversion lag and is seen as a dynamic, ongoing measure of campaign performance.

Key Differences Between MER and ROAS

Scope of Measurement: MER measures the overall efficiency of all media spending against total revenue, providing a high-level view of financial health. ROAS, however, measures the efficiency of specific campaigns and is more precise in determining the effectiveness of individual marketing initiatives.
Accounting Frameworks: MER is more aligned with cash accounting principles, where the focus is on immediate financial impact. ROAS fits well within accrual accounting, allowing businesses to assess the long-term value of their advertising efforts.
Incorporation of Conversion Lag: MER does not consider conversion lag, making it a straightforward, albeit less nuanced, metric. ROAS accounts for conversion lag, offering a more detailed and accurate portrayal of how ad spend converts into revenue over time.

Practical Application of MER and ROAS

When it comes to practical applications, the choice between MER and ROAS often depends on the business context:

Short-Term Financial Planning: Businesses looking for immediate insights into cash flow may prefer MER. It helps in quick assessment and is beneficial for companies managing tight cash flows.
Long-Term Strategic Planning: Companies focused on long-term growth and the effectiveness of specific marketing strategies should lean towards ROAS. It helps in understanding which campaigns are truly profitable over time.

Where to Find MER and ROAS in the Northbeam Dashboard

For businesses using Northbeam for their marketing analytics, both MER and ROAS can be found on the Overview Page under different accounting modes:

MER: Located under the Cash Accounting section, reflecting advertising ROI in real-time cash flow terms.
ROAS: Found under Accrual Performance, showing advertising ROI over time with conversion lags considered.

So, which metric do I choose?

Choosing between MER and ROAS depends significantly on your business needs and the specific financial and strategic insights you require. While MER offers a quick snapshot of financial health, ROAS provides a deeper dive into the effectiveness of your marketing investments over time. By understanding and utilizing both metrics appropriately, marketers can optimize their campaigns to achieve the best possible ROI, aligning their strategies with both immediate financial realities and long-term business goals.

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Northbeam is powered by AI, and so will you.

February 8, 2024

Northbeam's advanced ML models enhance marketing performance

How does Northbeam use AI?

Get in touch to learn more about how ML can transform your marketing intelligence.

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.

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December 12, 2023

Evaluating the significance of CPC in performance marketing

Do Cost Per Clicks Matter?

Zack Miller, @growthzacks

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.

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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.

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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.

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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

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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.

Here’s how to reach me:

www.growthzacks.com

https://twitter.com/growthzacks

https://www.linkedin.com/in/zack-miller/

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August 13, 2024

Google's decision to retain third-party cookies, and what it means for you

Google Chrome Won’t be Deprecating Cookies — Yet

In Forrester’s 2024 Marketing Survey, over 60% of respondents said that they didn’t actually believe that Google would deprecate third-party cookies.

And it seems that these respondents were correct — for now.

After years of fighting with advertisers and regulators, Google has officially reversed its decision to deprecate cookies on Google Chrome.

In this guide, we’ll cover:

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.

What does this mean for marketers?

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.

Utm Tracking 101 How To Use Utm Parameters To Measure Campaigns

August 20, 2025

Learn how to use UTM parameters to track campaign performance, improve attribution, and optimize ROI with this step-by-step guide for marketers.

UTM Tracking 101: How to Use UTM Parameters to Measure and Improve Campaign Performance

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.

Many marketing intelligence platforms also offer automated UTM builders.

Step 3: Append UTMs to Your Destination URLs

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.

Northbeam presents Black Friday and Cyber Monday 2024 predictions and performance with a futuristic digital sphere, symbolizing data-driven insights and analysis.

December 13, 2024

Wins, surprises, and lessons for 2025

BFCM 2024 Predictions: How Did We Do?

Explore the possibilities here.

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:

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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.

Northbeam presents a seamless omnichannel marketing strategy with a historical meeting room and futuristic digital elements, symbolizing innovation and best practices.

March 11, 2025

Mastering Omnichannel Marketing by creating a Seamless, Data-Driven Customer Journey

Creating a Seamless Omnichannel Marketing Strategy: Tips & Best Practices

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.

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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.

Northbeam highlights customer retention with an elegant historical setting, symbolizing long-term relationships and why retention should be every marketer’s priority.

February 25, 2025

Shifting Focus: Why Retention Is the Key to Sustainable Growth and Profitability

Beyond Acquisition: Why Retention Should Be Every Marketer’s Priority

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.

Company Northbeam creates an ad spend optimization with a vintage airplane in a high-tech workshop, symbolizing innovation and maximizing Return on Investment from every marketing dollar.

February 11, 2025

Stop Wasting Ad Spend: How Smarter Strategies and Tools Can Maximize ROI

Ad Spend Optimization: How to Get More ROI from Every Marketing Dollar

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.

Schedule a demo with Northbeam today and discover how our platform can help you optimize your ad spend and grow your business.

February 4, 2025

Use data-driven techniques to thrive during challenging times

5 Strategies to Maximize ROI During Economic Uncertainty

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.

January 8, 2025

Learn what marketing attribution is and how to harness its power in an ever-changing digital landscape

A Beginner’s Guide to Marketing Attribution

How can you make effective, strategic marketing decisions if you don’t know the impacts of your efforts? This is where marketing attribution comes in.

In today’s hyper-competitive digital landscape, understanding the effectiveness of your marketing efforts is crucial for sustained and scalable growth. Marketing attribution is how you “attribute” sales or conversions to different touchpoints in a customer’s journey.

In this guide, we’ll break down what marketing attribution is, cover its various models, and help you understand how to implement it to improve your marketing strategy.

What is marketing attribution?

As stated above, marketing attribution is the process of evaluating the impact of different marketing efforts across a customer journey. When a customer interacts with your ads and content across multiple channels, attribution models help you determine which of these channels had an impact on an ultimate conversion or sale — and how much impact each one had.

Let’s say a customer discovers your brand through a Google ad. After clicking onto your site, they browse for a while before following your brand on Instagram. You send them a couple of emails that they open but don’t click on. Finally, after seeing a Youtube ad, they make a purchase on your website.

With proper attribution, you can identify which of these touchpoints contributed to the final sale. With this information in hand, you can optimize future campaigns and make sure you keep directing resources towards the efforts that are working.

Understanding which touchpoints are most effective enables you to not only improve your budget allocation and decision-making, but bolster your customer experience as well. Your various touchpoints represent your customer journey, and a strong knowledge of your customer journey helps you customize and personalize your content depending on where a prospect is in the funnel.

Without proper attribution, you might be left guessing when it comes to the most important questions about your marketing strategy: what works, and what doesn’t?

Types of marketing attribution models

At Northbeam, we offer six different attribution models for users to play around with. We divide those attribution models into two categories: Simple Attribution Models and Multi-Touch Attribution Models (MTA).

Simple attribution models

With Simple Attribution Models, all credit is given to a single touchpoint. Simple Attribution Models have historically been the main option for marketers, and you may be used to seeing these legacy models on different platforms.

These models include:

First Touch Attribution
Last Touch Attribution
Last Non-Direct Touch Attribution

First Touch gives credit to the very first interaction a customer has with your marketing or content, while Last Touch gives credit to the very last touch they made before a conversion or sale.

Last Non-Direct Touch also gives credit to the last touchpoint in the user journey — excluding direct search. It is the default model used by most in-platform reporting (Facebook, Google, TikTok, etc.) as well as by Google Analytics.

Multi-touch attribution models (MTA)

With Multi-Touch Attribution Models, credit for a sale or conversion is divided across multiple touchpoints.

On Northbeam, these models include:

Linear MTA
Clicks-Only MTA
Clicks & Views MTA

With Linear MTA, credit is divided equally across all touchpoints on the customer journey. Clicks-Only MTA also divides credit equally between all touchpoints, but it does not include lower-funnel touchpoints or view-through touchpoints, like scrolling past an Instagram ad.

Finally, Clicks & Views MTA combines the Clicks-Only model with Northbeam’s proprietary View-based machine learning model to accurately and effectively account for the impact of view-based touchpoints. With Clicks & Views, you can minimize unattributed traffic and touchpoints and get the most complete picture of what works and what doesn’t.

How to choose the right attribution model

Selecting the right attribution model for your business depends on a variety of factors, including:

Your sales cycle - if you have a long or complicated sales cycle with multiple touchpoints, an MTA model may be most appropriate. If you have a straightforward and short sales cycle or only one or two channels, consider a simple attribution model.
Your marketing goals - If you’ve got sales on the mind, an MTA model will give you the fullest scope of your performance. But if you’re not targeting conversions or sales but rather something like brand awareness, keeping an eye on first-click will give you great data on how customers first became aware of your company.

But it’s not enough to select your preferred models. You also need to ensure that the data you’re feeding these models is up to snuff. Perhaps the most common challenge with attribution is data — how do you make all of your different channels play nice with one another when each one wants to take credit for a sale?

Marketing intelligence platforms like Northbeam bring all of your various touchpoints together across platforms and channels to offer robust analytics, consistent data collection, and near-real-time attribution information, when you need it.

To recap:

Get familiar with marketing attribution and the different types of attribution models
Use a sophisticated, well-tested tool to bring all of your data points together and give you a full picture of attribution
Use your accurate and timely attribution data to continuously refine your approach and make the best of each marketing dollar

Now that you’ve gotten the basics down, you may want to deepen your understanding of attribution. Read more: Unlocking the Truth Behind Incremental Performance: Why & How Attribution Models Can Make Or Break Your Business

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Northbeam is the perfect tool for performance marketing agencies.

January 29, 2024

The Last Thing You Should Worry About Is Accurate Data

5 Reasons Why Northbeam is the Perfect Tool for Performance Marketing Agencies

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.

Alignment doesn't necessarily equal client retention.

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.

August 21, 2024

Understanding cash snapshot and accrual performance modes

Accounting Modes 101: What Marketers Need to Know

Learn more about accounting modes in our knowledge base.

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.

At a high level, accounting modes can shape how you interpret key metrics like revenue, return on ad spend (ROAS), and media efficiency ratio (MER).

Cash Snapshot mode credits 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.

When should you use each mode?

Cash Snapshot mode

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.

July 24, 2024

CTV growth, evolving search, nano-influencers, and data privacy

2025 Marketing Trends to Watch Out For: Part 2

The Northbeam Team

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.

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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!

July 17, 2024