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abhishekchoudhari
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cohort-analysis

Analyze user engagement data to track retention patterns by cohort.

1|Updated Mar 9, 2026
Search Tags:#benchmarking#cohort analysis#user retention#engagement analysis#retention curve
One-click install
npx skills add https://github.com/abhishekchoudhari/pm-superic-skills --skill cohort-analysis-abhishekchoudhari
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cohort-analysis
Source: https://github.com/abhishekchoudhari/pm-superic-skills/tree/main/pm-data-analytics/skills/cohort-analysis
Command: npx skills add https://github.com/abhishekchoudhari/pm-superic-skills --skill cohort-analysis-abhishekchoudhari

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, seaborn, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you analyze user retention and engagement patterns by cohort, diagnosing root causes and recommending actions.

Core Features & Use Cases

  • Cohort Analysis: Track user retention over time by cohort.
  • Retention Curves: Build and interpret retention curves to identify trends.
  • Benchmarking: Compare retention metrics against industry standards.
  • Segmentation: Analyze retention across different acquisition channels, device types, feature adoption, and more.
  • Behavioral vs. Time Cohorts: Understand the impact of user behavior over time versus simple cohort age.
  • Resurrection Analysis: Identify reasons users return after churn.
  • Contribution to MAU: Determine which cohorts contribute to the current Monthly Active Users (MAU).

Quick Start

Run the cohort-analysis skill on your user engagement data to identify key insights into retention.

Frequently Asked Questions about cohort-analysis

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

FAQPage Schema
How do I perform cohort analysis on user engagement data using Python?▼

Cohort analysis groups users by acquisition date or behavior to measure retention patterns. It reveals whether specific cohorts drop off faster, helping diagnose root causes of churn and identify which features drive long-term engagement.

What is the difference between behavioral cohorts and time cohorts for retention analysis?▼

Behavioral cohorts group users by specific actions or feature adoption, while time cohorts group by acquisition date. Comparing them reveals the impact of user behavior on retention over time versus simple cohort age.

How do I build and visualize a retention curve to identify user engagement trends?▼

Build retention curves by calculating the percentage of active users per cohort over time. Visualize them using matplotlib and seaborn to identify engagement trends, benchmark against standards, and spot drop-off points.

Can I analyze user churn and resurrection reasons using cohort analysis?▼

Yes, cohort analysis tracks user retention to identify churn points and performs resurrection analysis to identify reasons users return after churning. This helps determine which cohorts contribute to current Monthly Active Users.

Do I need pandas and numpy to segment retention metrics by acquisition channel?▼

Yes, pandas and numpy are required dependencies to segment retention metrics across acquisition channels and device types. They process the engagement data needed to analyze patterns and benchmark against standards.

What is the best way to benchmark user retention metrics against industry standards?▼

The best way to benchmark user retention is to calculate your cohort retention curves and compare drop-off rates against industry standards. This identifies whether your engagement patterns fall below or exceed typical benchmarks.

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