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AutomationApplied work

From social signals to a qualified dataset

Reconciling captured activity, repairing delivery, and separating potential buyers from research-only records.

ClayIntent signalsData QA
unique profiles reconciled
1,000+

Reconciled professional identities prepared for further qualification.

AutomationSELECT A STEP

The problem

Captured events, delivered rows, and unique people represented different units. Technical content alone did not prove a suitable buyer identity.

My contribution

  • Reconciled source captures with the destination and repaired scoped delivery failures.
  • Implemented exact company, role, geography, and duplicate rules.
  • Separated buyer-review records from research and partner cases, retaining uncertain affiliations.

Evidence & scope

Checked event and person counts, unique profile keys, qualification formulas, and shared name-normalization outputs. Sample precision was not generalized to the full population.

What came out of it

Repaired the delivery workflow and created a reconciled professional dataset with clear qualification and exception handling.

Created a reconciled, more useful review dataset with an explicit exception queue. Remaining contact and channel checks stayed separate.

LET’S WORK ON YOUR GTM

Improving your GTM workflow?

Tell me about your target accounts, current stack, and the step between research and outreach that needs attention.