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