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cohort-analysis

Group users into cohorts by acquisition date and track retention over time.

145|28|Updated Jan 31, 2026
Search Tags:#data visualization#analytics#cohort analysis#user retention#churn analysis#customer behavior
One-click install
npx skills add https://github.com/guia-matthieu/clawfu-skills --skill cohort-analysis-guia-matthieu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cohort-analysis
Source: https://github.com/guia-matthieu/clawfu-skills/tree/main/skills/analytics/cohort-analysis
Command: npx skills add https://github.com/guia-matthieu/clawfu-skills --skill cohort-analysis-guia-matthieu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, plotly, click, and includes scripts (resource) components.

What problem does it solve?

This Skill helps you understand user retention and behavior patterns by analyzing how different groups of users (cohorts) perform over time, allowing you to identify trends and potential issues.

Core Features & Use Cases

  • Retention Tracking: Measure how long users remain active after their acquisition date.
  • Acquisition Analysis: Compare the long-term behavior of users acquired through different channels or at different times.
  • Product Changes Impact: Assess how new features or changes affect user stickiness.
  • Churn Prediction: Identify cohorts that show early signs of high churn.

Quick Start

Use the cohort-analysis skill to analyze retention from the file 'users.csv' using the 'signup_date' column for cohorts and 'last_active' for events.

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, group users into cohorts by acquisition date and track their activity over time. This Skill processes a CSV file using pandas to measure engagement and identify churn trends.

Can I use pandas and plotly for customer churn analysis and visualization?▼

Yes, customer churn analysis and visualization are handled using pandas for data manipulation and plotly for rendering charts. This combination tracks user behavior patterns and lifetime value across different cohorts.

What is the best way to measure the impact of product changes on user stickiness?▼

Measuring product change impact on user stickiness is done by comparing retention metrics across time-based cohorts. This approach isolates how new features affect long-term engagement and highlights early signs of churn.

How does cohort analysis track user engagement over subsequent periods?▼

Cohort analysis tracks user engagement by grouping users by signup date and monitoring their last active dates. This reveals retention trends and behavioral shifts across subsequent periods for marketing strategy optimization.

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