Mmcp.market

cohort-analysis skill

by phuryn·phuryn/pm-skills·27k stars·MIT

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.

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Install the cohort-analysis skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/phuryn/pm-skills.git /tmp/pm-skills
mkdir -p ~/.claude/skills
cp -r /tmp/pm-skills/pm-data-analytics/skills/cohort-analysis ~/.claude/skills/cohort-analysis
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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

Example 1: Upload CSV Data

Upload cohort_engagement.csv with columns: cohort_month, weeks_active,
user_id, feature_x_usage, engagement_score

Request: "Analyze retention patterns and identify why Q4 2025 cohorts
underperform compared to Q3"

Example 2: Describe Data Format

"I have monthly user cohorts from Jan-Dec 2025. Each row shows:
cohort date, user ID, purchase frequency, and support tickets.
Analyze which cohorts show best long-term retention."

Example 3: Feature Adoption Analysis

Upload feature_usage.xlsx with cohort adoption data.

Request: "Compare adoption curves for our new feature across cohorts.
Which cohorts adopted fastest? Any patterns?"

Key Capabilities

  • Data Reading: Import CSV, Excel, JSON, SQL query results
  • Retention Analysis: Calculate and visualize retention rates over time
  • Cohort Comparison: Compare metrics across cohort groups
  • Anomaly Detection: Flag unusual patterns or drop-offs
  • Python Scripts: Generate reusable analysis code for ongoing analysis
  • Visualizations: Create heatmaps, charts, and interactive dashboards
  • Research Design: Suggest targeted follow-up studies and interview approaches
  • Statistical Summary: Provide quantitative metrics and correlation analysis

Tips for Best Results

  1. Include time dimension: Provide data across multiple time periods
  2. Define cohort clearly: Make cohort grouping explicit (signup month, feature launch date, etc.)
  3. Provide context: Explain product changes, launches, or events during the period
  4. Multiple metrics: Include retention, engagement, feature usage, revenue, etc.
  5. Sufficient data: At least 3-4 cohorts for meaningful pattern identification
  6. Request specific output: Ask for visualizations, Python scripts, or research recommendations

Output Format

You'll receive:

  • Data Summary: Cohort overview and data quality assessment
  • Quantitative Findings: Key metrics, retention rates, and trend analysis
  • Visualizations: Charts showing retention curves, adoption patterns
  • Pattern Identification: 2-3 significant insights from the data
  • Research Recommendations: Specific qualitative and quantitative follow-ups
  • Analysis Scripts (if requested): Python code for reproducible analysis
  • Next Steps: Prioritized actions based on findings

Further Reading

  • Cohort Analysis 101: How to Reduce Churn and Make Better Product Decisions
  • The Product Analytics Playbook: AARRR, HEART, Cohorts & Funnels for PMs
  • Are You Tracking the Right Metrics?

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