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cohort-analysis

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.

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cohort-analysis

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Use cohort-analysis to Analyze [cohort data file] to assess retention, engagement, and feature adoption, returning data-quality findings, visualizations, key patterns, and prioritized follow-up actions.

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  • [cohort data file]

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SKILL.md

name: cohort-analysisdescription: "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

Further Reading