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How Data-driven Attribution Works in Google Analytics 4

GA4

In the ever-evolving landscape of online marketing, understanding how user interactions impact conversion probabilities is a key to success. Google Analytics 4 (GA4) has introduced a new approach to attribution modeling, offering insights into the customer journey. In this article, we are going to explain the methodology behind this data-driven attribution model. While this is a brief summary of the functionality, you can also deep dive into this topic in our recent article, All you need to know about GA4.

How it Works

Google is analysing the impact of each customer interaction on conversion probability, as in – what is the likelihood of a user to convert at any given time in his journey? It uses factors such as:

  1. Time from conversion
  2. Device type
  3. Number of ad interactions
  4. The order of ad exposure
  5. The type of creative assets

The data-driven attribution model assigns credit based on how the addition of each ad interaction to the path changes the estimated conversion probability.

Example

In the following high-level illustration, the combination of Ad Exposure #1 (Paid search), Ad Exposure #2 (Social), Ad Exposure #3 (Affiliate), and Ad Exposure #4 (Search) leads to a 3% probability of conversion. When Ad Exposure #4 does not occur, the probability drops to 2%, so we know that Ad Exposure #4 drives +50% conversion probability. We repeat this for each ad interaction and use the learned contributions as attribution weights.


The combination of ad exposure and probability of conversion

It is important to note that Google does not incorporate parameters related to visit quality. Consequently, it may not accurately identify channels that commonly appear in conversion journeys but have minimal impact on the user’s decision-making process. It can lead to overvaluing some of the channels and undervaluing others as a result.

The drawback of the attribution modeling GA uses is its inherent limitation in accounting for the post-view impact of brand-awareness campaigns and other upper funnel activities. These types of campaigns often have low click-through rates but play a crucial role in influencing buyers over the medium to long term.

Conclusion

The introduction of a data driven model into Google Analytics 4 is definitely a good step as it is putting the last click model aside; however, it is also crucial to acknowledge the model’s limitations. The omission of visit quality parameters and the challenge in measuring the impact of upper-funnel activities highlight areas where GA4 falls short. As the digital landscape continues to evolve, it’s essential for marketers to critically evaluate these limitations, augmenting GA4’s insights with a holistic understanding of user behaviour.

Reference: Google Analytics 4 Official Documentation

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