Agent skill · data analytics · aj-geddes
Cohort Analysis
Track and analyze user cohorts over time, calculate retention rates, and identify behavioral patterns for customer lifecycle and retention analysis
Why this skill is useful
Provides specific Python scripts for cohort analysis that include retention calculations and visualizations not commonly found in generic resources.
What it needs
Requires matplotlib, numpy, pandas, seaborn installed locally. About 4k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Cohort Analysis Overview Cohort analysis tracks groups of users with shared characteristics over time, revealing patterns in retention, engagement, and lifetime value. When to Use Measuring user retention rates and identifying when users churn Analyzing customer lifetime value (LTV) and payback periods Comparing performance across different user acquisition channels or campaigns Understanding how product changes affect different user groups over time Tracking engagement patterns and identifying early warning signs of churn Evaluating the long-term impact of onboarding improvements or feature releases Core Concepts Cohort: Group of users sharing a characteristic (signup date, region, etc.) Cohort Size: Initial group size Retention Rate: Percentage remaining active Churn Rate: Percentage who left Retention Curve: How cohort degrades over time Cohort Types Acquisition Date: Users grouped by signup period Behavioral: Users grouped by actions taken Revenue: Users grouped by purchase value Geographic: Users grouped by location Demographic: Users grouped by characteristics Implementation with Python Key Metrics Retention Rate: % of cohort active Churn Rate: % of cohort lost Day/Month 1 Retention: Early engagement Lifetime Value: Total revenue per user Payback Period: Time to recover CAC Insights to Look For Early retention predictors Differences between cohorts Seasonal patterns Engagement degradation Revenue trends Deliverables Cohort retention matrix Retention curve visualization Churn rate analysis Lifetime value calculations Revenue per cohort Executive summary with insights Actionable recommendations
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/cohort-analysis