Application analytics

Retention Cohort Diagnostics

Separate genuine retention shifts from tracking artefacts and identify which early behaviours actually predict returning users.

₩3,600,000 · 3-week study

Printed charts and data tables on a desk

Retention charts move for many reasons that have nothing to do with product quality: SDK upgrades, timezone mishandling, silent user-id changes, or a new onboarding that simply renames day-one events.

This study rebuilds cohort definitions from raw event history, checks identity continuity, and tests early-session behaviours against return rates. We are explicit about sample limits and what the data cannot prove.

You leave with a shortlist of behaviours worth protecting in the product, a list of measurement artefacts to stop treating as insights, and a reusable cohort notebook outline your analysts can maintain.

Typically included

  • Cohort definition rebuild
  • Identity continuity checks
  • Behaviour predictor shortlist
  • Analyst handoff notes

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