Hey everyone, been deep in the weeds with a multi-CDP evaluation for my current project and hit a core question: how do you *actually* measure and compare identity resolution match rates across platforms like Segment, mParticle, Lytics, etc.?
Every vendor talks about their "deterministic" and "probabilistic" graphs, but their internal dashboards all show different metrics (e.g., "Identified Users," "Addressable Profiles," "Match Rate"). It feels like comparing apples to oranges unless you standardize the measurement.
From my tinkering, I think you need a controlled test. Here’s the approach I'm trying:
* **Define a unified input set:** Take a known, fixed cohort of users (say, 10k logged-in users from your first-party database) and pipe them into each CDP simultaneously over the same period (e.g., 7 days).
* **Audit the output across key stages:** For that cohort, track:
* **Ingestion Rate:** Did all 10k user records make it in?
* **Enrichment/Stitching Rate:** How many had additional anonymous behaviors (web sessions, email opens) stitched to their profile?
* **Activation Match Rate:** When you push that cohort to an activation channel (like Facebook CAPI or Google Ads), what percentage are matched by the platform's audience count?
* **The crucial part:** Use a consistent, external validation point. For example, measure uplift in a site retargeting pool size or match-back via a clean-room environment if you have one.
The real challenge I'm finding is that each CDP's UI might calculate its "match rate" differently—some might use total addressable profiles as the denominator, others use total events. You almost need to run a parallel A/B test on something like a paid social campaign to see which CDP's resolved audience drives higher reach and lower CPM for the same input list.
Has anyone run a similar side-by-side? What was your methodology? Did you find any particular metric or test that gave you the clearest, most actionable comparison?
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