Can this skill create visual charts and heatmaps?

Yes, it generates retention heatmaps, cohort progression line charts, and feature adoption visualizations to help spot trends at a glance.

How does it assist with qualitative user research?

Based on the quantitative findings, the skill suggests targeted follow-up actions such as specific interview questions for churning users or survey designs for high-engagement cohorts.

Does it provide code for external data analysis?

Yes, it can generate production-ready Python scripts using libraries like pandas and numpy so you can reproduce the analysis in your own environment.

What data formats does the Cohort Analysis skill support?

The skill can process CSV, Excel, and JSON files, as well as raw SQL query results containing user identifiers and time-based engagement metrics.

What is the minimum amount of data needed for useful results?

For meaningful pattern identification and trend analysis, it is recommended to provide data for at least 3-4 distinct cohorts over several time periods.

Cohort Analysis & Retention Explorer

Name: Cohort Analysis & Retention Explorer
Author: KoryakinYurij

byKoryakinYurij

0•

データサイエンスとML

Analyzes user retention and engagement patterns across cohorts to identify churn drivers and feature adoption trends.

This skill empowers product managers and data analysts to transform raw engagement data into actionable cohort insights within the Claude environment. It automates the calculation of retention curves, generates visual heatmaps, and identifies anomalies in feature adoption across different user segments. By combining automated quantitative metrics with qualitative research recommendations, it helps teams understand the underlying drivers of user behavior, facilitating data-driven decisions for product growth, churn reduction, and long-term engagement strategy.

主な機能

01Qualitative research design suggestions for deeper user behavior insights

02Python script generation for reproducible pandas and numpy workflows

03Automated retention rate calculation and heatmap visualization

040 GitHub stars

05Comparative analysis of feature adoption across various user segments

06Identification of early churn patterns and seasonal engagement trends

ユースケース

01Generating reusable Python analysis scripts for recurring monthly product performance reporting

02Analyzing quarterly user retention to identify why specific signup cohorts are underperforming

03Tracking the adoption velocity of a new product feature across legacy versus new users

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add koryakinyurij/self-sustain-system cohort-analysis

For use in Claude.ai and ChatGPT

主な機能

01Qualitative research design suggestions for deeper user behavior insights

02Python script generation for reproducible pandas and numpy workflows

03Automated retention rate calculation and heatmap visualization

040 GitHub stars

05Comparative analysis of feature adoption across various user segments

06Identification of early churn patterns and seasonal engagement trends

ユースケース

01Generating reusable Python analysis scripts for recurring monthly product performance reporting

02Analyzing quarterly user retention to identify why specific signup cohorts are underperforming

03Tracking the adoption velocity of a new product feature across legacy versus new users

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add koryakinyurij/self-sustain-system cohort-analysis

For use in Claude.ai and ChatGPT