/cohort-analysis

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

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

• [Cohort Analysis 101: How to Reduce Churn and Make Better Product Decisions](https://www.productcompass.pm/p/cohort-analysis)
• [The Product Analytics Playbook: AARRR, HEART, Cohorts & Funnels for PMs](https://www.productcompass.pm/p/the-product-analytics-playbook-aarrr)
• [Are You Tracking the Right Metrics?](https://www.productcompass.pm/p/are-you-tracking-the-right-metrics)