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sp1esp1e/claude-skills/cohort-analysis/SKILL.md

cohort-analysis

Skill1 starsChanged 31 days agoMITView on GitHub

Time-based cohort analysis with retention and behaviour tracking. Activate when you need to measure how groups of users/customers behave over time — retention rates, revenue by cohort, or feature adoption curves.

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The file

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Files
SKILL.md

When to use

  • A stakeholder asks "are we retaining users better than last quarter?"
  • You need to measure N-day, weekly, or monthly retention for a product or feature
  • You want to compare how different acquisition cohorts (by channel, plan, or signup date) perform over their lifetime
  • You're investigating churn and need to identify at which period users typically leave

Process

  1. Define the cohort and activity — clarify: cohort grouping (signup month, first purchase date, etc.) and retention event (login, purchase, feature use). Document in the report header.
  2. Pull or build the data — if starting from a database, use scripts/cohort_query.sql as the starting point. Adapt the cohort_date and activity_date columns to your schema.
  3. Build the cohort table — run scripts/cohort_builder.py to produce a cohort × period membership table from event data. Output is a CSV with user_id, cohort_period, activity_period.
  4. Compute the retention matrix — run scripts/retention_matrix.py on the cohort table to generate the period-over-period retention rates. Output is an N×M matrix (cohort × period).
  5. Visualise — run scripts/cohort_visualizer.py to render a heatmap of the retention matrix and a time-series of retention curves per cohort.
  6. Interpret findings — consult references/retention_metrics_glossary.md for metric definitions and references/cohort_definition_patterns.md for pattern recognition.
  7. Write the report — fill assets/cohort_report_template.md. For a visual deliverable, fill in the assets/retention_matrix.html heatmap template.

Inputs the skill needs

  • Required: event data with user_id, cohort_date (e.g. signup_date), activity_date
  • Required: cohort grouping granularity (daily / weekly / monthly)
  • Required: retention event definition — what counts as "active" or "retained"?
  • Optional: minimum cohort size (recommend ≥ 100 users; smaller cohorts have noisy rates)
  • Optional: number of periods to track (e.g. 12 months)
  • Optional: cohort attributes to segment by (acquisition channel, plan tier, geography)

Output

  • assets/cohort_report_template.md (filled) — narrative interpretation and retention figures
  • assets/retention_matrix.html (filled) — colour-coded retention heatmap
  • scripts/retention_matrix.py output CSV — raw retention rates for downstream use

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