User cohort analysis

Packaging adaptation by AutoGPT, 2026-09-25. Original authorship and licence are retained. Changes are limited to the recorded name, metadata and file references; see ATTRIBUTION.md in this skill package.

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
• The Product Analytics Playbook: AARRR, HEART, Cohorts & Funnels for PMs
• Are You Tracking the Right Metrics?