Consolidating customer data into useful cohorts while preserving identity, provenance, and business rules.
ClayCSVData reconciliation
records consolidated
25,000+
Completed consolidation with source fields and identity keys preserved.
GTM systemsSELECT A STEP
Collect overlapping records and identify the fields that must stay consistent.
Resolve identities and apply explicit segment and exclusion rules.
Create clean destinations with repeatable updates and preserved source fields.
The problem
Multiple tables and overlapping segments made it difficult to identify the right users and keep source data consistent. Paying users, ownership cases, and suppressions needed distinct treatment.
My contribution
Defined conversion tiers and lifecycle segments with documented eligibility rules.
Consolidated source tables into clean destinations with stable segment identifiers.
Preserved original fields and refreshed suppression checks before downstream preparation.
Evidence & scope
Checked fresh exports, unique user IDs, payment flags, segment counts, source-field preservation, and boundary cases.
What came out of it
Created consistent working segments and repeatable updates without losing source information.
Created coherent working datasets and repeatable segmentation logic, with held records separated from eligible review cohorts.
More to explore
GTM systemsSELECT A STEP
Identify missing profiles, inconsistent identities, and work-contact gaps.
Recover relevant data and cross-check identity and company affiliation.
Return richer professional records with unresolved cases separated for review.