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Last Touch Attribution Model Comparison in Google Analytics 4 and Roivenue

The Last touch Attribution Model seems easy to understand so it is natural to expect that it will show the same results in Roivenue and in Google Analytics 4, especially when the same data is used to calculate the results.
Google Analytics 4
GA4 employs the Last Non-Direct Click rule as the basis for its Last Touch attribution model. It means that when a conversion happens after a direct visit GA4 attributes the entire conversion value to the last paid or unpaid channel the user interacted with before the direct visit. The only case when GA4 attributes the conversion to direct is when no other channel is present in the customer journey.
Roivenue adopts a different methodology which better reflects reality. It works with raw unprocessed data from GA4, bypassing GA4's post-processing, and does not exclude direct traffic from the conversion analysis. Consequently, in Roivenue's Last Touch model, direct traffic is given more credit.
Then in the Roivenue AI-driven model this credit can be reallocated to other channels depending on the real contribution to the conversion.
This is how Roivenue and Google Analytics will assign Last Touch credit in different situations:
  • Display > Social > Direct > Organic Search → both will assign 100% to Organic Search
  • Direct > Direct → both will assign 100% to Direct
  • Display > Social > Paid Search > Direct → In this case, Google Analytics will assign 100% to Paid Search, while Roivenue will assign 100% to Direct. In the Roivenue AI-driven model the credit will be distributed based on the true contribution to the conversion.
The fundamental disparity between GA4 and Roivenue lies in their treatment of direct traffic within the Last Touch attribution model. GA4 disregards direct traffic in all its models, whereas Roivenue shows a true Last Touch result, focusing on providing the best reflection of reality in its AI-driven model.
Understanding these differences is crucial for accurate analysis and interpretation of data, allowing businesses to make well-informed decisions based on their specific attribution needs.