Cross-Channel Profile Reconciliation (QA)

Cross-Channel Profile Reconciliation (QA)

1. 01

   Inventory every source system feeding the identity graph

   List CRM, product analytics, marketing automation, and community platforms. Confirm which fields each system exposes and how records are keyed, since a mismatch here propagates through every downstream match.

2. 02

   Run a deterministic match on known identifiers first

   Match on email, phone, and account ID before any probabilistic logic. Deterministic matches are the ground truth; they anchor the profile and expose where fuzzy matching is being relied on unnecessarily.

3. 03

   Apply probabilistic scoring only to unresolved records

   For records without a hard identifier, use behavioral and firmographic signals to suggest matches. Set a confidence threshold, for example 85%, and route anything below it to a manual review queue instead of auto-merging.

4. 04

   Compare profile completeness across matched records

   Check whether the unified profile has more populated fields than the best single source. If enrichment is not adding value, the matching logic or the enrichment provider may be underperforming.

5. 05

   Verify that anonymous web activity stitches to the right known contact

   Spot-check a sample of profiles to confirm that page views and product usage events are attributed to the correct person. Misattribution here silently corrupts attribution and personalization downstream.

6. 06

   Audit for duplicate profiles that survived the merge

   Run a deduplication query across the unified store and count residual duplicates. A duplicate rate above 2% indicates the match rules need tightening or the source data has quality issues.

7. 07

   Document match rates and confidence distributions for the client

   Produce a short report showing how many records matched deterministically, how many probabilistically, and how many remain unresolved. This transparency builds trust and sets expectations for campaign targeting accuracy.