# Churn Analysis

> Analyze customer and user churn to identify why people leave, who is at risk, and what interventions improve retention. Use when churn is high or rising, when quantifying revenue at risk, or when designing save and win-back motions.

- Skill: `itsual/churn-analysis` (Agent Skill)
- Install (CLI): `npx skillmds@latest add itsual/churn-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/itsual/churn-analysis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Finance & Business
- Author: itsual (https://skillmd.com/u/itsual)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/itsual/churn-analysis

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# Churn Analysis

## Overview

Churn is a lagging symptom of earlier failures in value delivery, expectations, or experience. Effective analysis connects quantitative patterns to qualitative causes and actionable interventions.

## When to Use

- Elevated or increasing churn rates
- Understanding revenue retention (GRR/NRR) drivers
- Designing intervention or save programs
- Prioritizing product investments that protect retention

## Core Practices

- Define churn precisely (logo, revenue, user, voluntary vs involuntary)
- Segment churn by cohort, plan, segment, tenure, and usage
- Identify leading indicators of churn risk
- Combine quantitative analysis with exit interviews / win-loss
- Quantify impact of potential interventions
- Close the loop into product, success, and pricing roadmaps

## Principles

- Separate “can’t use” (activation/setup) from “won’t stay” (value/fit)
- Involuntary churn (failed payments) needs different fixes than voluntary churn
- Early tenure churn often has different causes than late tenure churn
- Not all churn is equal — focus on valuable, savable customers first

## Verification

- [ ] Churn definition is consistent and trusted
- [ ] Top drivers are evidenced, not assumed
- [ ] Interventions are tied to specific driver hypotheses