Deep dive: unresolved discrepancies in cross-platform user engagement metrics during cohort analysis

i'm trying to refine our user acquisition funnels by applying a more granular generational psychology lens, particularly for our Gen Z and millennial segments. however, i'm hitting a wall with data integrity. we're observing significant, unexplained variances in conversion rates and LTV metrics when segmenting users by generation, even after normalizing for acquisition channel and initial product interaction. the primary issue seems to stem from cross-platform tracking discrepancies and how different analytics platforms (e.g., GA4 vs. our internal CRM) are attributing initial touchpoints and subsequent engagements to specific generational cohorts. this makes accurate cohort analysis extremely difficult, it's really frustrating.

we've implemented server-side tracking and robust UTM parameters, but when trying to reconcile these generational segments, we see a ~15-20% mismatch in active user counts and a 5-10% LTV delta between platforms for the same cohort. this is preventing us from confidently drawing conclusions about generational preferences or optimizing spend. i'm looking for advanced strategies for reconciling disparate generational data across multiple analytics systems, or methodologies to correct for these attribution inconsistencies without losing the behavioral nuances critical for generational psychology. thanks in advance!