retention-churn-prevention · diff
v1.0.0 to v1.0.0
93 added, 1 removed. Audit A to A.
---
name: retention-churn-prevention
description: Customer retention analysis, churn prediction, cohort analysis, win-back campaigns, and loyalty program design. Use when the user asks about churn, retention, customer lifetime value, cohort analysis, or win-back strategies.
license: MIT
origin: custom
author: Rebecca Rae Barton
author_url: https://github.com/thatrebeccarae
metadata:
version: 1.0.0
category: growth
domain: retention
updated: 2026-03-18
tested: 2026-03-18
tested_with: "Claude Code v2.1"
---
- # Retention Churn Prevention
+ # Retention & Churn Prevention
+ Analyze churn, predict at-risk customers, and design retention strategies.
+
## Install
```bash
git clone https://github.com/thatrebeccarae/claude-marketing.git && cp -r claude-marketing/skills/retention-churn-prevention ~/.claude/skills/
```
+ ## Churn Analysis Framework
+
+ ### Churn Types
+
+ | Type | Definition | Signal |
+ |------|-----------|--------|
+ | **Voluntary** | Customer actively cancels | Cancellation request, downgrade |
+ | **Involuntary** | Payment failure, card expiry | Failed charge, dunning |
+ | **Silent** | Stops using but does not cancel | Usage decline, no logins |
+
+ ### Churn Rate Calculation
+
+ ```
+ Monthly churn rate = Customers lost / Customers at start of month
+ Annual churn rate = 1 - (1 - monthly rate)^12
+ Net revenue retention = (Start MRR + Expansion - Contraction - Churn) / Start MRR
+ ```
+
+ ### Benchmarks
+
+ | Metric | Excellent | Good | Concerning |
+ |--------|----------|------|-----------|
+ | Monthly churn (SaaS) | <1% | 1-2% | >3% |
+ | Annual churn (SaaS) | <5% | 5-10% | >15% |
+ | Net revenue retention | >120% | 100-120% | <100% |
+
+ ## Customer Health Scoring
+
+ | Signal | Weight | Healthy | At Risk |
+ |--------|--------|---------|---------|
+ | Product usage | 25% | Daily/weekly | Monthly or less |
+ | Feature adoption | 20% | 5+ features | 1-2 features |
+ | Support sentiment | 15% | Positive/none | Negative |
+ | Billing health | 15% | On time, expanding | Late, downgrading |
+ | Engagement | 15% | Opens, clicks | Ignores |
+ | NPS/CSAT | 10% | Promoter (9-10) | Detractor (0-6) |
+
+ ## Early Warning Signals
+
+ | Timeframe | Signal | Action |
+ |-----------|--------|--------|
+ | 7 days | Login frequency drops 50%+ | In-app nudge, value reminder |
+ | 14 days | Key feature usage stops | CS outreach, usage tips |
+ | 30 days | No logins for 2+ weeks | Personal CS email, re-engagement |
+ | 60 days | NPS detractor, unresolved ticket | Executive escalation, save offer |
+ | 90 days | Cancellation signals | Retention call, custom offer |
+
+ ## Win-Back Campaigns
+
+ ### Timing
+
+ | Post-Churn Period | Response Rate | Approach |
+ |------------------|---------------|----------|
+ | 0-7 days | 15-25% | Immediate save, address exit reason |
+ | 7-30 days | 8-15% | New feature announcement, incentive |
+ | 30-90 days | 3-8% | Major update, significant discount |
+ | 90+ days | <3% | Annual check-in |
+
+ ### Win-Back Sequence
+
+ ```
+ Email 1 (Day 1): Address exit reason, offer to help
+ Email 2 (Day 7): New features since they left
+ Email 3 (Day 14): Comeback incentive (discount or extended trial)
+ Email 4 (Day 30): Final offer with urgency
+ ```
+
+ ## Retention Levers
+
+ 1. **Onboarding** — Time to first value predicts retention more than any other factor
+ 2. **Engagement loops** — Regular touchpoints (weekly reports, digests)
+ 3. **Feature adoption** — Users who adopt 3+ features churn 50% less
+ 4. **Community** — Community members have 2-3x higher retention
+ 5. **Switching costs** — Integrations and data create healthy lock-in
+ 6. **Proactive support** — Reach out before problems become cancellations
+
+ ## CLV Calculation
+
+ ```
+ Simple CLV = ARPU / Monthly Churn Rate
+ Full CLV = ARPU * Gross Margin % * (1 / Churn Rate)
+ CLV:CAC ratio target: >3:1
+ ```
+
+ ## Integration with Other Skills
+
+ - **klaviyo-analyst** — Design retention email flows and win-back sequences
+ - **customer-journey-mapping** — Map retention and advocacy stages
+ - **google-analytics** — Cohort analysis and engagement metrics
+ - **cro-auditor** — Optimize cancellation flow to save more customers