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cohort-analysis

Analyze user retention and engagement by cohort from CSV, Excel, or JSON data.

Updated Aug 27, 2026
Search Tags:#python#visualization#data-analysis#retention#cohort-analysis#cohort#feature-adoption
One-click install
npx skills add https://github.com/jupitermoney/pm-superic-skills --skill cohort-analysis-jupitermoney
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cohort-analysis
Source: https://github.com/jupitermoney/pm-superic-skills/tree/main/pm-data-analytics/skills/cohort-analysis
Command: npx skills add https://github.com/jupitermoney/pm-superic-skills --skill cohort-analysis-jupitermoney

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyze user engagement and retention patterns by cohort to identify trends in user behavior, feature adoption, and long-term engagement.

Core Features & Use Cases

  • Data reading: Import CSV, Excel, or JSON cohort data.
  • Retention analysis: Calculate cohort retention rates and visualize trends.
  • Cohort comparison & insights: Compare cohorts on key metrics like feature adoption and engagement.
  • Visualizations & reporting: Generate heatmaps, line charts, and dashboards.
  • Research design: Suggest targeted follow-up studies and qualitative insights.
  • Python scripts: Generate reusable analysis code for ongoing cohort analysis (if requested).
  • Use Case: For example, identify cohorts with early churn and propose improvements.

Quick Start

Upload a cohort dataset (cohort_month, weeks_active, user_id, feature_x_usage, engagement_score) and request retention insights.

Frequently Asked Questions about cohort-analysis

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

FAQPage Schema
How do I calculate cohort retention rates from a CSV file?▼

To calculate cohort retention rates from a CSV file, import your dataset containing user IDs and activity periods. The analysis processes CSV, Excel, or JSON inputs to compute retention metrics and generate visual trend reports.

What is the best way to visualize user retention and feature adoption trends?▼

Visualizing user retention and feature adoption trends is best achieved through heatmaps and line charts. The analysis generates these visualizations to uncover behavioral patterns and compare engagement across multiple time periods.

Can I use Python scripts for ongoing product analytics and retention forecasting?▼

Yes, you can use Python scripts for ongoing product analytics and retention forecasting. The Skill generates reusable Python code alongside visual dashboards to support continuous tracking of feature adoption and anomaly detection.

Does cohort analysis work with Excel and JSON data formats?▼

Cohort analysis works seamlessly with Excel and JSON data formats. The tool reads these inputs natively to perform comparative analyses, calculate retention rates, and identify early churn patterns across different user groups.

How do I identify cohorts with early churn and propose product improvements?▼

Identifying cohorts with early churn involves analyzing engagement scores and feature usage over time. The Skill detects anomalies in retention data, compares cohort metrics, and suggests targeted follow-up studies for product improvements.

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