With the legacy version of Google Analytics retiring soon, we’ve entered the era of Google Analytics 4 (GA4). This platform has undergone major changes, including the addition and refinement of machine-learning capabilities. GA4 has the ability to combine observed data and unobserved data, which is a necessity as changes in browser cookies and user identifiers increasingly limit the old way of tracking. This means that our tracking and analytics tools are losing data as we know it - and we must adapt. In this article, we discuss three out-of-the-box features in GA4, which will help us to compensate for this loss and remain data-informed.
Firstly, data-driven attribution (DDA) is an example of unobserved data. This attribution model uses a statistical model to show how significant a channel was in assisting a conversion. The DDA visualization is located in the Advertising > Conversion Paths report. Secondly, predictive metrics can be used to predict future behaviour. Predictive metrics are best used in the User Lifetime technique in the Explore reports, and can also be used to create audiences and segments to isolate likely/unlikely purchasers. Finally, behavior modeling involves integrating GA4 with a cookie consent management tool so that Google Analytics can collect data on users who don’t consent to be tracked. This data is anonymized and not related to a cookie or any user identifier. It is used to determine user-level activity.
By taking advantage of these features in GA4, questions about your users and data can transform from “How many views did page X receive?” to “Which users are most likely to make a large purchase within the next 7 days?”. Combining GA4’s machine-learning methods with remarketing and audience-sharing can launch your analytics from solely analysis to immediate use cases and even audience engagement and RoAS impact.
Originally reported by Martech: https://martech.org/3-google-analytics-4-features-to-make-up-for-lost-data/
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