Cohort Analysis
- 2k installs
- 24.9k repo stars
- Updated July 3, 2026
- phuryn/pm-skills
How do user retention, engagement, and feature adoption vary across cohorts over time, and what behavioral patterns drive these differences?
About
Cohort Analysis & Retention Explorer enables systematic examination of user engagement patterns grouped by signup period or feature launch date. The skill ingests CSV, Excel, or JSON data and performs multi-stage analysis: data validation, retention rate calculation, visualization generation, pattern identification, and research design. It produces retention heatmaps, adoption curves, anomaly detection, and actionable follow-up research recommendations including targeted user interviews and A/B test suggestions. Includes optional Python script generation for reproducible ongoing analysis using pandas and numpy.
- Multi-format data ingestion (CSV, Excel, JSON, SQL) with quality validation
- Retention heatmaps and cohort progression line charts with anomaly flagging
- Automated feature adoption curve comparison across cohort groups
- Research design recommendations for qualitative follow-ups and user interviews
- Reusable Python analysis scripts for reproducible cohort studies
Cohort Analysis by the numbers
- 1,983 all-time installs (skills.sh)
- +83 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #242 of 3,282 Productivity & Planning skills by installs in the Skillselion catalog
- Security screen: LOW risk (skills.sh audit)
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 2k |
|---|---|
| repo stars | ★ 24.9k |
| Security audit | 3 / 3 scanners passed |
| Last updated | July 3, 2026 |
| Repository | phuryn/pm-skills ↗ |
What it does
Analyze user retention patterns, feature adoption trends, and engagement metrics by cohort to identify churn drivers and behavioral insights.
Who is it for?
Product managers analyzing user lifecycle metrics, data analysts investigating retention patterns, growth teams optimizing engagement, teams with 3+ cohorts of multi-period data
Skip if: Real-time streaming analytics, individual user behavior tracking, ML model training, teams lacking structured cohort data or time-series engagement metrics
When should I use this skill?
Investigating user retention decline, comparing feature adoption across cohorts, identifying seasonal engagement trends, designing churn reduction experiments, validating product hypotheses
What you get
Quantified retention curves, identified adoption clusters, ranked churn patterns, and prioritized follow-up research—actionable insights for retention and feature strategy.
- Retention curve analysis
- Cohort segment report
By the numbers
- Accepts 3 input formats: CSV, Excel, and JSON
- Analysis workflow starts with a dedicated data read-and-validate step
Files
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
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Further Reading
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Use cohort-analysis for quantitative retention studies rather than discovery-interview-prep for qualitative customer interviews.
FAQ
What data format should I use for best results?
Provide CSV, Excel, or JSON with cohort identifier (signup month/date), time periods, user IDs, and engagement metrics (retention, feature usage, purchase frequency). Include at least 3-4 cohorts across multiple time periods for meaningful pattern detection.
Can this skill generate Python analysis code I can reuse?
Yes. Request Python scripts and the skill generates reproducible pandas/numpy code for retention calculations, cohort comparisons, and visualization generation, enabling ongoing analysis without manual recreation.
How does this help with churn reduction?
Cohort analysis identifies which user groups churn earliest, enabling targeted intervention. It reveals feature adoption clusters and suggests follow-up research (user interviews, session replays) to uncover root causes and test retention strategies.
Is Cohort Analysis safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.