Hi everyone! I'm still fairly new here, but I've been diving deep into CDPs after recently moving our sales ops from Salesforce to HubSpot. That migration taught me to never, ever trust the "out-of-the-box" match rates without checking them myself. 😅
Now I'm evaluating a few CDPs, and the biggest question mark for me is identity resolution. The vendors all show impressive percentages, but how do you actually *validate* those outputs are accurate? I don't want to build activation segments on faulty foundations.
I'm thinking of using a couple of external tools to sample and check. For example, taking a known set of customer records we've manually verified and running them through the CDP's identity graph to see if the merged profiles make sense. Has anyone tried something similar? What tools or methods did you use? I've heard of some teams using a simple Python script to compare outputs, but I'm curious about more accessible approaches for non-engineers.
Specifically, I'm worried about over-merging (linking two different people) and under-merging (missing connections). Any war stories or step-by-step checks you'd recommend would be so helpful.
Always backup first
You've hit on the absolute core of it - validation is everything. Your idea of using a known, manually-verified set is the gold standard. I still maintain a "golden record" table for exactly this.
My methodical approach is to run that known set through, but then analyze the discrepancies in a staging environment. I'll often export the CDP's proposed merged profiles and the source raw records into BigQuery (Snowflake works too) and run a series of manual checks.
For example, a simple query to catch over-merging: look for merged profiles where your known records have conflicting immutable attributes, like two different account IDs from the same source system. Under-merging is trickier; I create a query that looks for records sharing high-fidelity signals (like a consistent device ID paired with the same email hash) that the CDP kept separate.
The Python script route is common, but for a non-engineering approach, you can do a lot with careful sampling and Excel. Export a random sample of merged profiles (say, 500). Then manually audit the constituent records for, say, 50 of them. Look for nonsense - merged profiles containing both '@gmail.com' and '@company.com' emails for a single person, which is a huge red flag. It's tedious, but it surfaces logic flaws the vendor's dashboard will never show you.
MigrateMentor
I like your BigQuery/Snowflake approach for catching over-merging. That's smart.
One caveat with the manual audit of a sample in Excel: you need to be careful about which 50 merged profiles you pick. A truly random sample might miss the edge cases that are most instructive. I've found it's better to stratify the sample - intentionally pick some profiles from high-value segments, some with many source records, and some that are flagged with low confidence scores by the CDP itself. That gives you a better look at where the logic might be breaking down in ways that matter for activation.
Have you tried comparing outputs between staging and production identity resolution runs? I've seen drift occur after a model retrains.