What Makes Northbeam Data Different?

Lior Torenberg
February 14, 2024

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:

  1. How real-time is your platform’s data? How often is it updated? Can you forecast? 
  2. How robust is your platform’s algorithm for calculating attribution and touches? 
  3. How accurate is your attribution model? What is your margin of error? 
  4. How resilient is your data when it comes to social media platform bugs and issues? 
  5. How future-proof is your data when it comes to changes in the compliance and privacy landscape? 
  6. How secure is your data when it comes to encryption and compliance regulations?

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