# Cohort Analysis

Time-based cohort analysis with retention and behaviour tracking.

- Repository: https://github.com/nimrodfisher/data-analytics-skills (512 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-09-25
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add nimrodfisher/data-analytics-skills --skill cohort-analysis -g -a claude-code -y`
Codex: `npx skills add nimrodfisher/data-analytics-skills --skill cohort-analysis -g -a codex -y`
Cursor: `npx skills add nimrodfisher/data-analytics-skills --skill cohort-analysis -g -a cursor -y`
Gemini CLI: `npx skills add nimrodfisher/data-analytics-skills --skill cohort-analysis -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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.

The rest: https://github.com/nimrodfisher/data-analytics-skills/blob/HEAD/03-data-analysis-investigation/cohort-analysis/SKILL.md