Analyze ingested GA4 data using platform query tools

Once GA4 event data has landed in the platform, the analytical value is realized by joining it with first-party profile data — loyalty membership, purchase history, identity graph — and then running funnel, flow, and attribution analyses that were impossible when the data lived solely in a Google Analytics report. This task covers the configuration and workspace-assembly steps required to turn the raw ingested events into business intelligence.

The join configuration is the highest-stakes decision: selecting loyaltyId as the Person ID links anonymous GA4 sessions to known customers only for sessions where the user was authenticated. Analysts must decide how to handle unauthenticated sessions — whether to exclude them, group them under a device ID, or apply probabilistic stitching. Once the connection is live, workspace analyses benefit from the enriched schema: marketing channel attribution can be evaluated across both web and in-store touchpoints, and loyalty tier can be used as a filter to compare conversion funnels for high-value versus standard members.

Parallel viability: High parallelism. The same join and analysis pattern is reproducible in Looker (with BigQuery as the backend), Tableau (with Snowflake or BigQuery as the source), or any BI tool that can join a GA4 export table to a CRM/loyalty table. The AEP-specific advantage is that the joined data also feeds into Real-time Customer Profile for activation use cases (audience building, journey triggering), creating a closed loop that a pure BI tool cannot replicate. Teams whose GA4 analysis is purely retrospective and does not feed real-time activation should evaluate whether a composable BI stack meets their needs at lower cost.