pkking/cohort-analysis
dotfiles/dot_config/skillshare/skills/cohort-analysis/SKILL.md
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.
data-ai
Updated Apr 27, 2026
$ install --global
skillsauthnpx skillsauth add pkking/dotfiles cohort-analysisInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- cohort-analysis
- 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.
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
- Include time dimension: Provide data across multiple time periods
- Define cohort clearly: Make cohort grouping explicit (signup month, feature launch date, etc.)
- Provide context: Explain product changes, launches, or events during the period
- Multiple metrics: Include retention, engagement, feature usage, revenue, etc.
- Sufficient data: At least 3-4 cohorts for meaningful pattern identification
- 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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