retention-churn-prevention

Retention & Churn Prevention

Analyze churn, predict at-risk customers, and design retention strategies.

Install

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