# Churn Analysis

> Diagnose churn and identify retention levers. Use when customers are leaving and you need to understand why and how to keep them.

- **Type:** Skill
- **Install:** `agentstack add skill-uthumany-uthy-legacy-os-churn-analysis`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [uthumany](https://agentstack.voostack.com/s/uthumany)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [uthumany](https://github.com/uthumany)
- **Source:** https://github.com/uthumany/uthy-legacy-os/tree/main/skills/improve/churn-analysis
- **Website:** https://uthumany.github.io/uthy-legacy-os/

## Install

```sh
agentstack add skill-uthumany-uthy-legacy-os-churn-analysis
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Churn Analysis

## Overview

Churn is the silent killer of product growth. Losing 5% of users per month means losing 46% per year — you'd need 50%+ annual growth just to stay flat. This skill helps you diagnose why users leave, identify leading indicators of churn, and design retention interventions.

## When to Use

- Monthly or quarterly churn is above target
- You need to understand WHY users are leaving
- You're designing retention campaigns or reactivation flows
- Building a churn prediction model
- Don't use for: one-off cancellations (talk to the user directly), products with < 100 active users (too few data points)

## Instructions

### 1. Measure Churn Correctly

**Definition**: Users who stop using your product within a given period.
- **User churn**: % of users who stop using
- **Revenue churn**: % of recurring revenue lost (often different from user churn)
- **Gross churn**: Total lost (include downgrades and cancellations)
- **Net churn**: Lost - expansions (can be negative = negative churn = growth)

Segment churn by:
- **Plan**: Free vs. paid vs. enterprise
- **Cohort**: Users who joined in the same month
- **Behavior**: Heavy vs. light users
- **Source**: How were they acquired?

### 2. Find Leading Indicators

Churn doesn't happen overnight. Look for early warning signs:
- **Engagement drop**: Frequency of use decreases over 2+ weeks
- **Feature decay**: Stop using key features
- **Support contact**: Filing complaints or asking about cancellation
- **Billable action stops**: For revenue-churn, look for stopped billing-related actions
- **Account changes**: Team members leaving, admin changing

### 3. Analyze Churn Reasons

**Quantitative**:
- Cancel survey results (what did they select?)
- Last actions before churn (what did they do last?)
- Usage patterns of churned vs. retained users

**Qualitative**:
- Exit interviews: "What made you decide to leave?"
- Win-back conversations: "What would it take to come back?"
- Lost deal analysis: "Why did you choose competitor X?"

### 4. Identify Retention Levers

For each churn driver, design an intervention:

| Churn Driver | Intervention | Metric to Track |
|-------------|-------------|-----------------|
| Never activated | Improve onboarding, add personal setup | Activation rate |
| Lost interest | Re-engagement emails, new features | Return rate |
| Poor support experience | Reduce response time, add self-serve | CSAT, support volume |
| Competitor switch | Competitive analysis, feature gap fill | Win-back rate |
| Budget cuts | Downgrade path, usage-based pricing | Downgrade rate |

### 5. Build a Retention Dashboard

Monitor weekly:
- Churn rate by segment
- Leading indicators (engagement drop, support tickets)
- Intervention effectiveness (re-engagement campaign conversion)
- Reactivation rate (how many came back?)

## Sample Output

**Churn analysis for Q2** (5,000 users, 8% monthly churn):

**Top churn drivers**:
1. (40%) Never activated — signed up, never invited team. Left in 2 weeks.
2. (25%) Lost interest — used for 1-2 months, then stopped.
3. (15%) Switched to competitor — mentioned better mobile app.
4. (10%) Too expensive — small teams on free plan hit limits.
5. (10%) Other — unknown.

**Key insight**: 40% of churn happens before activation. Invest in onboarding.
**Intervention**: New guided setup flow with team invite on first login.
**Target**: Reduce pre-activation churn from 40% to 20% → overall churn from 8% to 6%.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [uthumany](https://github.com/uthumany)
- **Source:** [uthumany/uthy-legacy-os](https://github.com/uthumany/uthy-legacy-os)
- **License:** MIT
- **Homepage:** https://uthumany.github.io/uthy-legacy-os/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-uthumany-uthy-legacy-os-churn-analysis
- Seller: https://agentstack.voostack.com/s/uthumany
- Browse the marketplace: https://agentstack.voostack.com/browse

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