# Cohort Analysis

Perform cohort analysis on user engagement data, retention curves, feature adoption...

- Repository: https://github.com/phuryn/pm-skills (27597 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-10-29
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add phuryn/pm-skills --skill cohort-analysis -g -a claude-code -y`
Codex: `npx skills add phuryn/pm-skills --skill cohort-analysis -g -a codex -y`
Cursor: `npx skills add phuryn/pm-skills --skill cohort-analysis -g -a cursor -y`
Gemini CLI: `npx skills add phuryn/pm-skills --skill cohort-analysis -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add phuryn/pm-skills`

## Description

Perform cohort analysis on user engagement data, retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.

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

# Cohort Analysis & Retention Explorer

## Purpose
Analyze user engagement and retention patterns by cohort to identify trends in user behavior, feature adoption, and long-term engagement. Combine quantitative insights with qualitative research recommendations.

## How It Works

### Step 1: Read and Validate Your Data
- Accept CSV, Excel, or JSON data files with user cohort information
- Verify data structure: cohort identifier, time periods, engagement metrics
- Check for missing values and data quality issues
- Summarize key statistics (cohort sizes, date ranges, metrics available)

### Step 2: Generate Quantitative Analysis
- Calculate cohort retention rates and engagement trends
- Identify retention curves, drop-off patterns, and anomalies
- Compute feature adoption rates across cohorts
- Calculate month-over-month or period-over-period changes
- Generate Python analysis scripts using pandas and numpy if requested

### Step 3: Create Visualizations
- Generate retention heatmaps (cohorts vs. time periods)
- Create line charts showing cohort progression
- Build comparison charts for feature adoption
- Visualize drop-off points and engagement trends
- Output as interactive charts or static images

### Step 4: Identify Insights & Patterns
- Spot one or more significant patterns:
  - Early churn in specific cohorts
  - Late-stage engagement changes
  - Feature adoption clusters
  - Seasonal or temporal trends
- Highlight surprising findings and deviations
- Compare cohort performance to establish baselines

### Step 5: Suggest Follow-Up Research
- Recommend qualitative research methods:
  - Targeted user interviews with churning users
  - Feature usage surveys with engaged cohorts
  - Session replays of key interaction patterns
  - Win/loss analysis for high vs. low retention cohorts
- Design follow-up quantitative studies
- Suggest A/B tests or feature experiments

## Usage Examples

The rest: https://github.com/phuryn/pm-skills/blob/HEAD/pm-data-analytics/skills/cohort-analysis/SKILL.md