In several projects we tried building end-to-end analytics via user_id, but transmission failures lead to path mismatches and duplicates.
I’ve seen the same person appear as different profiles across devices and sources; the chain doesn't line up.
This creates headaches: metrics don’t line up, attribution breaks, and budgets get misplanned because we don’t see the true paths.
What problems does mis-transmission cause?
• Incorrect user_id and authorization errors break stitching: one person can be counted as different profiles on different devices and sources; attribution falls apart.
• In business terms: we undercount returning users, budgets are misallocated, and forecast accuracy suffers.
What can we extract?
The takeaway: without a single identity source stitching fails; data splits by devices and channels; we need to figure where identity is lost — on the client, on the server, or in the CRM.
I checked logs and ran quick tests; sometimes user_id drops on the client due to auth, sometimes in the CRM; duplicates and misaligned paths show up.
The bottom line: data from different sources don’t match; we need a single view.
What to do?
• Verify user_id format and transmission, don’t use user_id as GA4 dimension, set up proper stitch between client server and CRM, test in pilot with duplicates monitoring in BigQuery.
• Ensure consistent format and transmission across channels.
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