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churn-analysis

Analyze customer churn by tenure, health score, and engagement metrics.

Updated Apr 1, 2026
Search Tags:#retention#analyze#segment#interventions#churn#health-score
One-click install
npx skills add https://github.com/hpsgd/claude-marketplace --skill churn-analysis-hpsgd
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: churn-analysis
Source: https://github.com/hpsgd/claude-marketplace/tree/main/plugins/product/customer-success/skills/churn-analysis
Command: npx skills add https://github.com/hpsgd/claude-marketplace --skill churn-analysis-hpsgd

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyzes customer churn data to identify why customers leave and which segments are at risk, enabling targeted retention actions.

Core Features & Use Cases

  • Segment churn patterns by tenure, health score, last engagement, and exit reason to prioritize interventions.
  • Distinguish voluntary churn from involuntary churn to tailor responses.
  • Recommend top interventions ranked by expected impact on retention.

Quick Start

Provide a time window and segment to analyze, and run the churn analysis to get prioritized actions.

Frequently Asked Questions about churn-analysis

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

FAQPage Schema
How do I identify churn drivers and segment at-risk customers?▼

To identify churn drivers, segment customer data by tenure, health score, last engagement, and exit reason. This process distinguishes voluntary from involuntary churn to prioritize targeted retention interventions.

What is the best way to analyze customer retention data across different time windows?▼

Analyzing customer retention data requires grouping churn patterns across specific time windows and segments. This method ranks interventions by expected impact on retention to guide targeted actions.

Can I distinguish voluntary churn from involuntary churn in my dataset?▼

You can distinguish voluntary churn from involuntary churn by grouping exit reasons within your dataset. This distinction allows you to tailor responses and apply appropriate retention interventions per segment.

How do I prioritize retention interventions based on expected impact?▼

Prioritize retention interventions by ranking recommended actions according to their expected impact on retention. This ranking is produced by analyzing churn drivers across tenure, health score, and engagement segments.

What data do I need to provide to run a churn analysis?▼

To run a churn analysis, provide a specific time window and customer segment for evaluation. The analysis processes engagement metrics, tenure, and health scores to output prioritized retention actions.

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