Individual skill

cohort-analysis

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.

03-data-analysis-investigation/cohort-analysis/SKILL.md

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Repository last updated: Nov 7, 2026
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claude-code Repository topic
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SKILL.mdcore · 2,666 bytes
assets/cohort_report_template.mddoc · 2,527 bytes
assets/retention_matrix.htmlasset · 4,295 bytes
references/cohort_definition_patterns.mddoc · 4,721 bytes
references/retention_metrics_glossary.mddoc · 3,714 bytes
scripts/cohort_builder.pyruns · 3,572 bytes
scripts/cohort_query.sqlasset · 2,990 bytes
scripts/cohort_visualizer.pyruns · 3,410 bytes
scripts/retention_matrix.pyruns · 3,163 bytes

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# 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.