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retention-analysis

Identify retention patterns and churn risk from user data with survival curves, cohort matrices, and predictive models.

Updated Jan 15, 2026
Search Tags:#retention#cohort-analysis#machine-learning#predictive-models#churn#survival-analysis
One-click install
npx skills add https://github.com/KaiserWhoLearns/skillsbench --skill retention-analysis-kaiserwholearns
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: retention-analysis
Source: https://github.com/KaiserWhoLearns/skillsbench/tree/main/registry/terminal_bench_1.0/predict-customer-churn/environment/skills/retention-analysis
Command: npx skills add https://github.com/KaiserWhoLearns/skillsbench --skill retention-analysis-kaiserwholearns

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scikit-learn, lifelines, matplotlib, seaborn, plotly, and includes scripts (resource) components.

What problem does it solve?

The Retention Analysis Skill helps teams understand how and why users stay or leave, enabling data-driven actions to reduce churn and increase long-term value.

Core Features & Use Cases

  • Survival analysis to build and compare user lifetimes and churn timing.
  • Cohort analysis to track retention by acquisition period and behavior segments.
  • Churn risk modeling with ML that predicts at-risk users and prioritizes interventions.
  • Actionable retention insights and reports to inform onboarding, pricing, and engagement strategies.
  • Flexible data inputs and output formats (plots, tables, and exportable metrics) for SaaS, memberships, e-commerce, and gaming.

Quick Start

Analyze your dataset to produce survival analysis, cohort insights, and churn risk predictions for retention optimization.

Frequently Asked Questions about retention-analysis

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

FAQPage Schema
How do I use survival analysis and cohort analysis to understand user churn?▼

Survival analysis and cohort analysis identify retention patterns and churn timing by tracking user lifetimes and grouping retention metrics by acquisition periods to reveal when and why users leave.

Can I build predictive models to flag at-risk users before they churn?▼

Yes, predictive models use machine learning on user behavior data to calculate churn risk scores, helping prioritize interventions for at-risk users before they actually cancel.

Does this retention analysis approach work for SaaS, e-commerce, and gaming data?▼

Retention analysis supports SaaS, memberships, e-commerce, gaming, and service industries by processing flexible data inputs to optimize onboarding, lifecycle management, and revenue forecasting.

What is the best way to visualize churn risk and cohort retention matrices?▼

Visualizing churn risk and cohort retention matrices uses matplotlib, seaborn, and plotly to generate survival curves and cohort heatmaps, translating raw user data into actionable visual insights.

Do I need pandas and scikit-learn to run churn prediction workflows?▼

Yes, running churn prediction workflows requires pandas and numpy for data manipulation, scikit-learn for machine learning models, and lifelines for survival analysis computations.

What output formats can I export from a cohort analysis and survival curve workflow?▼

Cohort analysis and survival curve workflows export actionable retention insights as visual plots, structured tables, and exportable metrics for reports and engagement strategy planning.

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