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Choi-Keith
Choi-KeithCOMMUNITY
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cohort-analysis

Analyze user retention and feature adoption trends across cohorts from CSV or Excel data.

Updated Aug 10, 2026
Search Tags:#visualization#pandas#retention#cohort analysis#churn#product analytics#feature adoption
One-click install
npx skills add https://github.com/Choi-Keith/skill-arsenal-ultra --skill cohort-analysis-choi-keith
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cohort-analysis
Source: https://github.com/Choi-Keith/skill-arsenal-ultra/tree/main/plugins/pm-skills/pm-data-analytics/skills/cohort-analysis
Command: npx skills add https://github.com/Choi-Keith/skill-arsenal-ultra --skill cohort-analysis-choi-keith

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Product teams often have raw user engagement data but lack a structured way to understand how different user cohorts retain, churn, and adopt features over time. This Skill turns raw cohort data into retention curves, visualizations, and actionable research recommendations. ## Core Features & Use Cases - Retention Analysis: Computes cohort retention rates, identifies churn points, and generates retention heatmaps and trend line charts. - Feature Adoption Comparison: Compares feature adoption curves across cohorts to find which groups adopt fastest and why. - Research Recommendations: Suggests follow-up qualitative studies such as churned-user interviews, session replays, and A/B tests based on detected patterns. - Use Case: Upload a CSV with cohort_month, weeks_active, user_id, and engagement_score columns, then ask why Q4 2025 cohorts retain worse than Q3 cohorts to receive quantitative findings, visualizations, and next-step research plans. ## Quick Start Upload your cohort engagement CSV file and ask the assistant to analyze retention patterns and explain why one cohort underperforms another.

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?▼

Provide a data file with cohort identifiers, time periods, and engagement metrics such as user_id, cohort_month, and weeks_active. The analysis computes retention rates per cohort, generates retention heatmaps, and highlights churn points and anomalies.

What data format is needed for cohort analysis?▼

CSV, Excel, or JSON files work, containing a cohort identifier (such as signup month), a time dimension (weeks or months active), a user identifier, and one or more engagement metrics like feature usage or engagement score.

How many cohorts do I need for meaningful retention analysis?▼

At least 3-4 cohorts are recommended for meaningful pattern recognition. With fewer cohorts, it is difficult to distinguish genuine trends from noise or one-off events affecting a single group.

Can cohort analysis compare feature adoption across user groups?▼

Yes. Provide feature usage data per cohort and the analysis computes adoption rates per cohort, builds comparison charts of adoption curves, and identifies which cohorts adopt fastest along with possible explanations.

What are the limitations of cohort retention analysis?▼

Cohort analysis reveals correlations and patterns but cannot prove causation. Findings should be validated with follow-up qualitative research such as user interviews, session replays, or controlled A/B experiments.

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