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
- Onboarding — Time to first value predicts retention more than any other factor
- Engagement loops — Regular touchpoints (weekly reports, digests)
- Feature adoption — Users who adopt 3+ features churn 50% less
- Community — Community members have 2-3x higher retention
- Switching costs — Integrations and data create healthy lock-in
- 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
