v1.0
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Pawel Huryn
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name: cohort-analysis
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description: "Perform cohort analysis on user engagement data. Identifies retention patterns, feature usage trends, and suggests qualitative follow-up research. Use when analyzing user retention by cohort, studying feature adoption over time, or investigating engagement patterns. Triggers: cohort analysis, retention analysis, user cohorts, engagement trends, cohort data."
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---
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# Cohort Analysis & Retention Explorer
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## Purpose
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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.
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## How It Works
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### Step 1: Read and Validate Your Data
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- Accept CSV, Excel, or JSON data files with user cohort information
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- Verify data structure: cohort identifier, time periods, engagement metrics
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- Check for missing values and data quality issues
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- Summarize key statistics (cohort sizes, date ranges, metrics available)
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### Step 2: Generate Quantitative Analysis
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- Calculate cohort retention rates and engagement trends
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- Identify retention curves, drop-off patterns, and anomalies
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- Compute feature adoption rates across cohorts
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- Calculate month-over-month or period-over-period changes
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- Generate Python analysis scripts using pandas and numpy if requested
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### Step 3: Create Visualizations
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- Generate retention heatmaps (cohorts vs. time periods)
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- Create line charts showing cohort progression
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- Build comparison charts for feature adoption
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- Visualize drop-off points and engagement trends
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- Output as interactive charts or static images
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### Step 4: Identify Insights & Patterns
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- Spot one or more significant patterns:
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- Early churn in specific cohorts
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- Late-stage engagement changes
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- Feature adoption clusters
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- Seasonal or temporal trends
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- Highlight surprising findings and deviations
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- Compare cohort performance to establish baselines
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### Step 5: Suggest Follow-Up Research
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- Recommend qualitative research methods:
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- Targeted user interviews with churning users
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- Feature usage surveys with engaged cohorts
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- Session replays of key interaction patterns
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- Win/loss analysis for high vs. low retention cohorts
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- Design follow-up quantitative studies
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- Suggest A/B tests or feature experiments
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## Usage Examples
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**Example 1: Upload CSV Data**
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```
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Upload cohort_engagement.csv with columns: cohort_month, weeks_active,
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user_id, feature_x_usage, engagement_score
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Request: "Analyze retention patterns and identify why Q4 2025 cohorts
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underperform compared to Q3"
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```
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**Example 2: Describe Data Format**
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```
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"I have monthly user cohorts from Jan-Dec 2025. Each row shows:
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cohort date, user ID, purchase frequency, and support tickets.
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Analyze which cohorts show best long-term retention."
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```
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**Example 3: Feature Adoption Analysis**
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```
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Upload feature_usage.xlsx with cohort adoption data.
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Request: "Compare adoption curves for our new feature across cohorts.
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Which cohorts adopted fastest? Any patterns?"
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```
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## Key Capabilities
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- **Data Reading**: Import CSV, Excel, JSON, SQL query results
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- **Retention Analysis**: Calculate and visualize retention rates over time
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- **Cohort Comparison**: Compare metrics across cohort groups
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- **Anomaly Detection**: Flag unusual patterns or drop-offs
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- **Python Scripts**: Generate reusable analysis code for ongoing analysis
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- **Visualizations**: Create heatmaps, charts, and interactive dashboards
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- **Research Design**: Suggest targeted follow-up studies and interview approaches
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- **Statistical Summary**: Provide quantitative metrics and correlation analysis
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## Tips for Best Results
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1. **Include time dimension**: Provide data across multiple time periods
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2. **Define cohort clearly**: Make cohort grouping explicit (signup month, feature launch date, etc.)
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3. **Provide context**: Explain product changes, launches, or events during the period
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4. **Multiple metrics**: Include retention, engagement, feature usage, revenue, etc.
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5. **Sufficient data**: At least 3-4 cohorts for meaningful pattern identification
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6. **Request specific output**: Ask for visualizations, Python scripts, or research recommendations
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## Output Format
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You'll receive:
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- **Data Summary**: Cohort overview and data quality assessment
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- **Quantitative Findings**: Key metrics, retention rates, and trend analysis
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- **Visualizations**: Charts showing retention curves, adoption patterns
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- **Pattern Identification**: 2-3 significant insights from the data
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- **Research Recommendations**: Specific qualitative and quantitative follow-ups
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- **Analysis Scripts** (if requested): Python code for reproducible analysis
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- **Next Steps**: Prioritized actions based on findings
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---
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### Further Reading
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- [Cohort Analysis 101: How to Reduce Churn and Make Better Product Decisions](https://www.productcompass.pm/p/cohort-analysis)
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- [The Product Analytics Playbook: AARRR, HEART, Cohorts & Funnels for PMs](https://www.productcompass.pm/p/the-product-analytics-playbook-aarrr)
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- [Are You Tracking the Right Metrics?](https://www.productcompass.pm/p/are-you-tracking-the-right-metrics)
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