In a groundbreaking move, GA4 introduces a revolutionary user-based export feature to complement its event-based data export. Unveiling user activity data export to BigQuery, this enhancement empowers you to delve deeper into user attributes with unprecedented ease. If you’ve already established a connection to export your GA4 event data, get ready to take your analysis to the next level.
What’s Inside the User Data Export?
Upon activating the user data export, your analytics_*** dataset will welcome two new tables:
- pseudonymous_users_*: This table compiles data related to the default user_pseudo_id identifier.
- Each row corresponds to a user_pseudo_id.
- Rows are updated on field changes.
- Note: Unconsented user data is excluded from this table.
- The user_id field is not available here.
- Every row includes a timestamp indicating the latest activity of a pseudonymous user.
- users_*: This table contains data associated with the optional user_id identifier.
- Each row represents a user_id.
- Rows are updated on field changes.
- Data for unconsented users can be included if a user_id is present.
- The user_pseudo_id field is absent in this table.
- Every row includes a timestamp marking the most recent user activity.
Why Does This Matter?
While much of the user data export can be obtained from the existing event data export in BigQuery, this new feature offers several key advantages:
- Smooth Learning Curve: Accessing user (attribute) data becomes more seamless.
- Enhanced Insights: Uncover user properties, geographical info, and device data more conveniently.
- Unveil User Lifetime Stats: Gain indirect insights into user lifetime value (LTV) statistics.
- Unlock Audience and Prediction Data: Explore new dimensions of audience and prediction data not previously accessible.
Diving into User Scoped Audience Data
The schema below reveals exciting attributes accessible through the user scoped audience data:
- audiences (RECORD): Audience information
- audiences.id (INTEGER): Audience ID
- audiences.name (STRING): Audience Name
- audiences.membership_start_timestamp_micros (INTEGER): Timestamp (microseconds) of initial audience inclusion
- audiences.membership_expiry_timestamp_micros (INTEGER): Timestamp (microseconds) of audience membership expiration (reset upon qualifying activity)
- audience.npa (BOOLEAN): Reflects NPA settings for events and user-scoped custom dimensions in the audience definition.
Empowering Insights with User Scoped Prediction Data
The user export doesn’t stop at audiences; it also introduces essential prediction data for each user:
- predictions (RECORD): Prediction information
- predictions.in_app_purchase_score_7d (DOUBLE): Probability of a user with recent activity making an in-app purchase within 7 days.
- predictions.purchase_score_7d (DOUBLE): Probability of a user with recent activity making a purchase event within 7 days.
- predictions.churn_score_7d (DOUBLE): Probability of a user active in the last 7 days becoming inactive in the next 7 days.
- predictions.revenue_28d_in_usd (FLOAT): Expected revenue (in USD) from all purchase events within 28 days for a user active in the last 28 days.
A Glimpse at Limitations
Like any feature, there are constraints to be aware of:
- No historical backfill of user data; event data schema still required for accurate user counts.
- Expect minor differences in user data between the user export and event export due to Google’s processing.
Activating the Export: A Quick Guide
Unlocking this game-changing feature is remarkably simple:
- Navigate to the GA4 admin section.
- Click on BigQuery links and select your existing BigQuery link configuration.
- Look for the new checkbox at the bottom of the page.
- Choose the “Daily” option and hit Save.
Congratulations! You’re on your way to accessing enhanced user insights. Keep in mind that user data exports to BigQuery occur daily, so prepare for a wealth of actionable information.
Unleash the Power of GA4 User Activity Data Export Today!