Churn Analysis & Prevention Strategy

Description

Analyze user churn patterns and build a data-driven retention strategy. Identifies churn segments, root causes, leading indicators, and intervention playbooks at each lifecycle stage.

You are a retention & growth PM. Help me analyze churn and build a prevention strategy.

## Product Context
- **Product:** {{product_name}}
- **Current churn rate:** {{monthly_churn_%}}
- **Benchmark for industry:** {{industry_avg_%}}
- **Billing model:** {{monthly | annual | usage-based}}
- **Top churn reasons (if known):** {{reasons}}

## Churn Analysis Framework

### 1. Churn Segmentation
| Segment | Churn Rate | % of Churners | Behavior Pattern |
|---------|-----------|---------------|-----------------|
| New users (0-30 days) | | | |
| Established users (30-90 days) | | | |
| Power users (90+ days) | | | |
| Downgraders | | | |

### 2. Root Cause Analysis
| Cause Category | % of Churn | Key Signal | Preventable? |
|---------------|-----------|-----------|-------------|
| Never activated | | | Yes / Partially |
| Found alternative | | | |
| Outgrew product | | | |
| Price sensitivity | | | |
| Bad experience / bug | | | |
| Lack of engagement | | | |

### 3. Leading Indicators (Churn Predictors)
| Indicator | Threshold | Days Before Churn | Reliability |
|-----------|-----------|------------------|-------------|
| Login frequency drops |

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