GA4 built-in metrics are typically session-scoped. This means they focus on quantifiable aspects of user behaviour within individual sessions on your website or app. 

While GA4 Session-scoped metrics don’t provide a single, unchanging value for a user, they can still be very insightful when combined with user-scoped dimensions.

But you should not be mixing dimensions and metrics of different scopes. So, you should not be combining session-scoped metrics with user-scoped dimensions. 

Combining Scopes (the correct approach):

Following are examples of how you can leverage session-centric metrics and user-scoped dimensions together in GA4 without directly mixing them within a single report row to gain valuable insights into user behaviour across multiple sessions:

Example-1: Analyzing Purchase Behavior by Country

User-Scoped Dimension: “country” (This captures the user’s country)

Session-Centric Metrics:

1. Average Order Value: This calculates the average amount spent per purchase order within each country segment.
2. Purchase Key Event Rate: This reflects the percentage of sessions that resulted in a purchase event (e.g., “purchase_complete”), analyzed within each country segment.

By segmenting user data by “country” and then analyzing “Average Order Value” and “Purchase Key Event Rate” within each segment, you can gain insights into how users from different geographical regions behave in terms of purchase behavior across multiple sessions.

This helps you understand if users from specific countries tend to spend more per purchase or have a higher rate of completing the purchase event.

Example-2: Analyzing App Feature Usage by Device Category

User-Scoped Dimension: “Device Category” (This captures the broad category of the user’s device – mobile, tablet, desktop)

Session-Centric Metrics:

1. Average Time Spent on Feature X: This measures the average time users spend on a specific app feature (e.g., a game level) within each device category segment.
2. Event Completions for Feature Y: This tracks the number of completions for a specific app feature event (e.g., completing a tutorial) within each device category segment.

By segmenting user data by “Device Category” and then analyzing “Average Time Spent on Feature X” and “Event Completions for Feature Y” within each segment, you can understand how users on different device categories (mobile, tablet, desktop) engage with specific app features across multiple sessions.

This helps you identify if certain features are more popular or easier to use on specific device categories and optimize the user experience accordingly.