From 15% to 5% Churn: Our ML Playbook

Most companies measure churn wrong. Before fixing it, measure it correctly: Gross vs Net Churn: - Gross: Lost MRR / Starting MRR - Net: (Lost - Expansion) / Starting MRR - Logo: Lost Customers / Starting Customers Cohort Analysis: Track each signup month separately. You will find churn patterns: - Month 1: 11% average (fixable) - Month 2-3: 5% average (normal) - Month 4-12: 2% average (good) - Month 13+: 4% spike (renewal problem) Churn Reasons: Our ML analysis of 10M churns found: - 34%: Did not see value (onboarding issue) - 27%: Too expensive (pricing/packaging issue) - 19%: Switched competitor (product gaps) - 12%: Business failed (unpreventable) - 8%: Other/unknown

Implement ML churn prediction to identify at-risk customers 30 days early: Technical Implementation: 1. Connect Stripe to QuantLedger (2 minutes) 2. ML models analyze historical patterns (24 hours) 3. Daily predictions with risk scores start flowing Risk Segmentation: - Critical (>80% churn risk): Executive intervention - High (60-80%): Success manager outreach - Medium (40-60%): Automated campaigns - Low (<40%): Monitor only Intervention Playbooks: Critical Risk: - CEO/founder calls within 24 hours - Offer strategic session or QBR - Prepare retention offer if needed - Success rate: 67% High Risk: - Success manager schedules check-in - Review usage and identify gaps - Offer training or implementation help - Success rate: 42%

Execute targeted save campaigns based on churn reasons: Value Perception Fixes (34% of churn): - Redesign onboarding for "aha moment" in 48 hours - Weekly value reports showing ROI - Feature discovery campaigns - Success milestone celebrations Result: 72% reduction in value-based churn Pricing/Packaging Fixes (27% of churn): - Usage-based discounts for at-risk accounts - Pause option instead of cancel - Downgrade paths to retain some revenue - Annual plans with 20% discount Result: 61% reduction in price-based churn Competitive Switches (19% of churn): - Feature parity roadmap communication - Lock-in with annual contracts - Exclusive features for long-term customers - Integration moats Result: 44% reduction in competitive churn

Churn reduction is never done. Monthly optimization cycle: Week 1: Analyze Previous Month - Review save campaign performance - Identify new churn patterns - Update intervention playbooks Week 2: Test New Tactics - A/B test intervention messages - Try new save offers - Experiment with timing Week 3: Scale Winners - Roll out successful tests - Update automation rules - Train team on new approaches Week 4: Predict Next Month - Review ML predictions - Prepare for seasonal patterns - Allocate resources Results compound. Month 1: 13% churn. Month 3: 9% churn. Month 6: 5% churn. Month 12: 3.8% churn.