cohort-analysis

Analyze user engagement data by cohort to identify retention curves and feature adoption trends.

Updated Mar 15, 2026

Search Tags:#data visualization #cohort analysis #user retention #feature adoption #churn analysis #engagement metrics

Ready to run

npx skills add https://github.com/bowiemax/pm-skills --skill cohort-analysis-bowiemax

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cohort-analysis
Source: https://github.com/bowiemax/pm-skills/tree/main/pm-data-analytics/skills/cohort-analysis
Command: npx skills add https://github.com/bowiemax/pm-skills --skill cohort-analysis-bowiemax

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps analyze user engagement and retention patterns by cohort, enabling identification of trends in user behavior, feature adoption, and long-term engagement.

Core Features & Use Cases

Quantitative Analysis: Calculate retention rates, engagement trends, and feature adoption across different user cohorts.
Visualization: Generate heatmaps, line charts, and comparison charts to visualize cohort performance.
Insight Generation: Identify significant patterns like early churn, late-stage engagement changes, and adoption clusters.
Follow-up Research: Suggest qualitative research methods such as user interviews and surveys.
Use Case: Analyze monthly user cohorts to understand why Q4 2025 cohorts underperform compared to Q3, identifying specific drop-off points and suggesting targeted interventions.

Quick Start

Upload cohort_engagement.csv and analyze retention patterns to identify why Q4 2025 cohorts underperform compared to Q3.

Frequently Asked Questions about cohort-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I analyze user retention patterns to identify why a specific cohort is underperforming?▼

User retention analysis processes cohort engagement data to calculate retention curves and identify significant drop-off points. By comparing cohorts, it pinpoints specific stages where users disengage and generates visualizations to highlight performance gaps.

What is cohort analysis and how does it help track feature adoption over time?▼

Cohort analysis segments users into groups based on sign-up timelines to track behavioral metrics. It reveals feature adoption trends and engagement clusters, showing exactly when and how different user groups interact with product functionalities over their lifecycle.

How do I start investigating churn patterns using my user engagement data?▼

Investigating churn patterns requires uploading a CSV file containing user engagement metrics. The analysis validates the data, identifies early churn trends, and generates visual heatmaps alongside follow-up qualitative research suggestions to explain the drop-off.

Can I use a CSV file to generate visualizations for cohort engagement metrics?▼

Yes, you can upload a cohort engagement CSV file to automatically generate visualizations. The analysis produces heatmaps, line charts, and comparison charts that map retention rates and engagement trends across different user segments.

What is the best way to visualize user retention curves across different cohorts?▼

Visualizing user retention curves is best handled by generating heatmaps and comparison charts from validated cohort data. This approach highlights segment-level insights, revealing late-stage engagement changes and adoption clusters that raw numbers often obscure.

Does this cohort analysis approach provide qualitative research recommendations?▼

Yes, the cohort analysis approach provides qualitative research recommendations. After identifying quantitative behavioral patterns like early churn or adoption clusters, it suggests targeted follow-up methods such as user interviews and surveys to investigate underlying causes.