Google Analytics (GA4) distinguishes between various attribution dimensions based on different scopes. These distinctions can cause data discrepancies when combining certain dimensions with different metrics, potentially leading to confusion.
A clear example of this occurs when we combine the Default Channel Group or Source/Medium dimensions with the Sessions metric:
We can immediately notice that the summary row does not match the total number of sessions. This discrepancy occurs because the Default Channel Group and Source/Medium dimensions are designated for conversion events. This means GA4 only pulls sessions where a conversion event has taken place.
To verify this behavior, we can apply a filter to our scorecard to include only sessions with conversion events:
Understanding Attribution Dimensions
This distinction is critical for accurate attribution reporting and applies to many dimensions. Below are definitions for some key attribution dimensions:
Default Channel Group Dimensions
- First User Default Channel Group – The channel group associated with the user’s first visit to the site.
- Session Default Channel Group – The channel group assigned to the session when it starts.
- Default Channel Group – The channel group attributed to the conversion event.
Source/Medium Dimensions
- First User Source/Medium – The source/medium from the user’s first visit to your site.
- Session Source/Medium – The source/medium for the specific session.
- Source/Medium – The source/medium attributed to the conversion event.
Campaign Dimensions
- First User Campaign – The campaign associated with the user’s first visit to the site.
- Session Campaign – The campaign assigned to the session when it starts.
- Campaign – The campaign attributed to the conversion event.
Conclusion
Understanding how GA4 attributes different dimensions across user, session, and conversion scopes is essential for accurate reporting. When analyzing reports, it’s crucial to use dimensions and metrics that align with the correct scope to avoid misinterpretations and ensure meaningful insights. By recognizing these nuances, you can refine your attribution analysis and make better data-driven decisions.
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