Introducing Polaris, Bloom’s Proprietary Media Mix Modeling Tool

Digital Marketing / June 22, 2023

Marketers Lack Confidence in their Data

As companies rely more heavily on data-driven marketing strategies, it has become increasingly important for marketers to have confidence in the data they collect and analyze. However, recent studies have shown that many marketers lack this confidence. 

Fewer than 1 in 5 feel they can measure marketing ROI accurately or have the necessary technology tools to achieve their marketing goals. This lack of confidence can lead to ineffective marketing campaigns and missed opportunities for growth and revenue.

Working with multiple clients in multiple industries, we witness this problem firsthand. 

Bloom’s clients often ask: how can we identify successful strategies and channels when the tools we rely on report different numbers of conversions?

For example, Meta Ads,  Google Ads or even Display may report more conversions than Google Analytics, and these deltas can sometimes be way out of a normal standard deviation, confusing for marketers trying to optimize their advertising spend and allocate budgets accordingly.

This discrepancy is due to various reasons: 

1. Third-Party Cookie Deprecation: Google plans on phasing out third-party cookies on Chrome, the most widely used web browser. Other browsers, like Firefox and Safari, made the move years ago. This change impacts the ability of ad platforms to track users across websites, and as a result, some conversions may not be attributed correctly to the appropriate campaigns or channels.
2. Apple’s App Tracking Transparency: With the release of iOS 14.5, Apple introduced App Tracking Transparency, which requires apps to ask permission before tracking users. This change affects the ability of Meta and others to track iOS device users.
3. Different Attribution Models: Ad platforms and Google Analytics use different attribution models to measure conversions which can lead to discrepancies in reported metrics between the platforms.

Our solution: Polaris, Bloom’s Proprietary Media Mix Modeling Tool

Polaris is the name of Bloom’s media mix modeling tool, entirely created in-house by our data analytics team.

Media mix modeling is an analysis technique that allows marketers to measure the impact of their marketing and advertising campaigns to determine how various channels contribute to their objectives.

Bloom’s tool leverages econometrics to reconcile platform reporting and Google Analytics results. Its ultimate goal: help our teams craft more efficient and data-driven media plans. 

How does it work?

First, we connect different sources of data into our tool. We pull metrics such as, but not limited to: the number of conversions, sessions and revenue generated from various ad platforms like Google Ads, Meta Ads, Snapchat, Pinterest, and more. Then, we do the same thing from Google Analytics. 

Secondly, after rendering, the algorithm applies discount rates to the platforms’ reporting based on deltas between different data points. The average number of touchpoints to a conversion is also factored in.

Once that’s done, we are left with a modeled revenue for each platform. 

For example, on the one hand, Meta Ads is reporting $961 818 in revenue from its ads. On the other hand, Google Analytics was only attributing $18 543 in revenue from Meta Ads. After using Polaris, we can estimate that it’s more realistic to attribute $623 316 to Meta Ads.

Ultimately, the output is a more nuanced view of each channel’s performance in the media mix. It informs us on how to plan a more optimal budget allocation across different channels. 

Why Polaris?

Like the North Star, Polaris can serve as a reliable guide or a point of reference for marketers trying to navigate the complex world of digital marketing.

Polaris is also a symbol of dependability, reliability, and constancy. Just as sailors have relied on the North Star to guide them on their journeys, marketers can rely on an attribution tool like Polaris to provide accurate and consistent data on the effectiveness of their marketing campaigns.

Overall, the name Polaris embodies many of the key characteristics marketers seek in a tool that will help them navigate, measure, and optimize their marketing efforts.

What’s next for Polaris?

The fun thing about this project is that the possibilities for improvements are almost endless. In the near future, we’ll continue to refine our current model based on our clients and team’s suggestions. After that, we’ll be looking into incorporating traditional channels and offline metrics. We also look forward to experimenting with artificial intelligence and machine learning to grow the tools’ capabilities. 

If you are interested in learning more about our media solutions, please get in touch with us here.