mcpField
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retention-analysis

retention-analysis

github.com/liangdabiaoData & databases29836 installsUpdated 25w ago
Repository

About retention-analysis

A data analysis skill for measuring retention, estimating churn risk, and improving retention strategies across subscription, membership, and e-commerce businesses. It combines survival and cohort analysis with machine learning, producing risk scores and recommendations.

Key features

  1. 01Calculate retention and churn metrics
  2. 02Build Kaplan-Meier survival curves
  3. 03Analyze acquisition-time or behavior cohorts
  4. 04Use Cox regression to examine churn drivers
  5. 05Predict churn risk with machine learning
  6. 06Generate retention improvement recommendations

Use cases

  1. 01Analyze SaaS subscription renewals and cancellations
  2. 02Compare retention across membership tiers
  3. 03Identify e-commerce customers at risk of churn
  4. 04Assess factors affecting long-term retention

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/liangdabiao/claude-data-analysis-ultra-main --skill retention-analysis

After installing, type/retention-analysisin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

README.md
examples/
basic_retention.py
comprehensive_retention_report.html
survival_analysis_report.html
examples/sample_data/
README.md
scripts/
retention_analyzer.py
survival_analyzer.py
visualizer.py

Details

From the project README

Retention Analysis Skill

Analyze user retention patterns, predict customer churn, and optimize retention strategies using advanced statistical methods and machine learning techniques.

Quick Start

This skill helps you:

  1. Calculate retention rates and churn metrics
  2. Build survival curves using Kaplan-Meier analysis
  3. Perform cohort analysis to understand behavior patterns
  4. Predict churn risk with machine learning models
  5. Identify retention drivers using Cox regression
  6. Generate actionable insights for retention improvement

When to Use

  • SaaS Product Analysis: User subscription renewal and cancellation patterns
  • Membership Programs: Member engagement and loyalty analysis
  • E-commerce: Customer repeat purchase behavior and subscription boxes
  • Gaming Apps: Player retention and engagement metrics
  • Service Industries: Customer satisfaction and long-term relationships
  • Subscription Businesses: Monthly/yearly subscription analysis

Key Requirements

Install required packages:

pip install pandas numpy matplotlib seaborn scikit-learn lifelines

Core Workflow

1. Data Preparation

Your data should include:

  • User identifiers: Unique user/customer IDs
  • Time variables: Registration date, activity dates, subscription period
  • Event indicators: Churn status (1=churned, 0=active)
  • User attributes: Demographics, behavior, subscription details
  • Optional: Usage metrics, payment history, engagement data
2. Analysis Process
  1. Data preprocessing: Clean and prepare retention data
  2. Survival analysis: Build Kaplan-Meier curves
  3. Cohort analysis: Group users by acquisition time
  4. Risk modeling: Identify churn drivers with Cox regression
  5. Churn prediction: Build machine learning prediction models
  6. Insight generation: Create actionable recommendations
3. Output Deliverables
  • Retention rate tables and charts
  • Survival curves with confidence intervals
  • Cohort heatmaps and behavior patterns
  • Churn risk scores and feature importance
  • Retention optimization strategies

Example Usage Scenarios

SaaS Subscription Analysis
# Analyze monthly subscription renewal patterns
# Predict which users are likely to churn
# Identify features that drive long-term retention
Membership Program Analysis
# Track member engagement over time
# Compare retention across membership tiers
# Analyze payment method impact on retention
E-commerce Customer Retention
# Analyze repeat purchase patterns
# Calculate customer lifetime value
# Identify high-value customer segments

Key Analysis Methods

Survival Analysis
  • Kaplan-Meier Estimator: Non-parametric survival curve
  • Log-rank Test: Compare survival between groups
  • Cox Proportional Hazards: Multi-variable risk modeling
  • Median Survival Time: Time when 50% of users have churned
Cohort Analysis
  • Time-based Cohorts: Group by acquisition month/quarter
  • Behavior-based Cohorts: Group by usage patterns
  • Retention Matrix: Visualize retention over time periods
  • Cohort Comparison: Compare different cohort behaviors
Machine Learning Prediction
  • Logistic Regression: Binary churn classification
  • Random Forest: Non-linear pattern detection
  • Gradient Boosting: High accuracy prediction
  • Feature Importance: Identify key churn drivers

Common Business Questions Answered

  1. What is our overall retention rate?
  2. How does retention vary by user segment?
  3. What factors most influence customer churn?
  4. Which users are at highest risk of leaving?
  5. How can we improve long-term retention?
  6. What is the typical customer lifetime?

Integration Examples

See examples/ directory for:

  • basic_retention.py - Survival analysis basics
  • cohort_analysis.py - Cohort-based retention analysis
  • churn_prediction.py - ML-based churn prediction
  • Sample datasets for testing

Best Practices

  1. Data Quality: Ensure accurate churn definitions and time measurements
  2. Event Definition: Clearly define what constitutes "churn"
  3. Time Windows: Choose appropriate analysis periods
  4. Segmentation: Analyze different user groups separately
  5. Validation: Always validate models with test data
  6. Business Context: Consider operational constraints and costs

Advanced Features

  • Competing Risks Analysis: Different types of churn
  • Time-varying Covariates: Dynamic feature analysis
  • Customer Lifetime Value: Integrate retention with revenue
  • Retention Forecasting: Predict future retention trends
  • A/B Testing: Measure retention improvement impact

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