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cohort-analysis

Analyze user retention and feature adoption by cohort with Python.

Updated Aug 27, 2026
Search Tags:#data visualization#cohort analysis#user retention#feature adoption#churn analysis#engagement metrics
One-click install
npx skills add https://github.com/KoryakinYurij/Self-Sustain-System --skill cohort-analysis-koryakinyurij
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cohort-analysis
Source: https://github.com/KoryakinYurij/Self-Sustain-System/tree/main/Skill%20Creation/research/pm-skills/pm-data-analytics/skills/cohort-analysis
Command: npx skills add https://github.com/KoryakinYurij/Self-Sustain-System --skill cohort-analysis-koryakinyurij

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

  • Data Validation: Accepts CSV, Excel, or JSON data and verifies structure and quality.
  • Quantitative Analysis: Calculates retention rates, engagement trends, and feature adoption.
  • Visualizations: Generates heatmaps, line charts, and comparison charts.
  • Insight Identification: Spots significant patterns like early churn or feature adoption clusters.
  • Research Suggestions: Recommends qualitative and quantitative follow-up studies.
  • Use Case: Analyze monthly user cohorts to understand why specific cohorts show lower long-term retention compared to others, and suggest targeted user interviews to uncover the reasons.

Quick Start

Upload cohort_engagement.csv and request an analysis of retention patterns for Q4 2025 cohorts.

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 by cohort from a CSV file?▼

To analyze user retention by cohort, upload a structured CSV, Excel, or JSON file containing user engagement data. The Skill validates data quality, calculates retention rates, and generates visual heatmaps and line charts to identify segment-level retention trends.

What is cohort analysis and how does it identify churn patterns?▼

Cohort analysis tracks user engagement over time by grouping users into cohorts based on their start date. It identifies churn patterns by comparing retention curves across different cohorts, spotting significant behavioral trends like early drop-offs or delayed feature adoption.

Can I visualize feature adoption trends and engagement metrics using Python?▼

You can visualize feature adoption trends using Python scripts powered by pandas and numpy. The analysis generates heatmaps and line charts that map how different user segments adopt features over time, providing clear engagement metrics.

Does this cohort analysis tool work with JSON and Excel data formats?▼

Yes, this cohort analysis tool accepts CSV, Excel, and JSON data formats. It validates the structure and quality of the uploaded data before calculating retention rates, engagement trends, and generating visualizations.

What follow-up research is recommended after identifying low long-term retention in a cohort?▼

After identifying low long-term retention in a cohort, the tool recommends targeted qualitative user interviews and quantitative follow-up studies. These research suggestions help uncover the underlying reasons behind early churn or poor feature adoption.

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