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

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

Customer Health Scoring

Early Warning Signals

Win-Back Campaigns

Timing

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

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Supporting file: EXAMPLES.md

Retention & Churn Prevention — Examples

Example 1: Build Churn Early Warning System

Prompt

Design a customer health scoring system for our SaaS product. We have login data, feature usage, support tickets, and billing info.

What the skill does

1. Designs health score with weighted signals (usage 25%, feature depth 20%, support 15%, billing 15%, engagement 15%, NPS 10%)
2. Defines thresholds: Green (80-100), Yellow (50-79), Red (0-49)
3. Creates alert triggers for each risk level with recommended CS actions
4. Proposes automated intervention playbook for Yellow and Red accounts

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Example 2: Win-Back Campaign

Prompt

Design a win-back campaign for churned users. We had 340 cancellations last quarter. Top exit reasons: too expensive (40%), not using it enough (35%), switched to competitor (25%).

What the skill does

1. Segments churned users by exit reason
2. Designs 3 win-back sequences (one per exit reason): discount offer for price-sensitive, new feature showcase for low-usage, comparison content for competitor-switched
3. Sets timing: Day 1, 7, 14, 30 cadence
4. Estimates recovery: 8-12% of price-sensitive, 5-8% of low-usage, 2-3% of competitor-switched

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Example 3: Cohort Retention Analysis

Prompt

Analyze our monthly cohort retention curves. January cohort retained 68% at month 3, but March cohort only retained 52%.

What the skill does

1. Compares cohort curves to identify where March diverges from January
2. Investigates variables: onboarding changes, product updates, traffic source mix, seasonal factors
3. Identifies root cause: March cohort had 60% from paid ads (vs January 30%) with lower intent
4. Recommends: segment retention by acquisition channel, improve paid traffic onboarding, tighten ICP targeting

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Supporting file: REFERENCE.md

Retention Churn Prevention Reference

Reference data, benchmarks, and platform-specific details for the Retention Churn Prevention skill.