Cohort Retention Matrix (%)
Month-over-Month Retention by Acquisition Cohort
| Cohort | M0 | M1 | M2 | M3 | M4 | M5 | M6 | M7 | M8 | M12 |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-Q1 | 100% | 94% | 89% | 85% | 82% | 78% | 75% | 73% | 72% | 70% |
| 2023-Q2 | 100% | 96% | 92% | 88% | 85% | 82% | 80% | 78% | 77% | - |
| 2023-Q3 | 100% | 95% | 91% | 87% | 84% | 81% | 79% | - | - | - |
| 2023-Q4 | 100% | 97% | 93% | 89% | 86% | - | - | - | - | - |
Retention Trend Analysis
Retention Curve by Acquisition Channel
Cohort Health Distribution
Customer Risk Segmentation
🚨 High-Risk Cohorts (Churn Velocity >40%)
Identified Cohorts: 128 customers across SMB web and partnership channels
Characteristics: Feature adoption declining 20%+ MoM, support tickets increasing, low engagement with product updates
Action Taken: Automated win-back campaigns, proactive CS outreach, feature training programs
Results: $200K+ revenue recovered, 35% re-engagement rate
⚠️ Medium-Risk Cohorts (Churn Velocity 20-40%)
Count: 247 customers | ARR at Risk: $485K
Intervention: Weekly cohort health reports, usage-based segmentation, targeted feature adoption campaigns
Performance: 62% of at-risk customers stabilized, prevented $280K+ in churn
✅ Healthy Cohorts (Retention >85%)
Count: 1,625 customers | Avg Retention: 92%
Characteristics: High feature adoption, low churn, strong engagement, expansion potential
Strategy: Identify expansion opportunities, upsell high-value features, develop case studies
Retention by Acquisition Channel
M6 Retention Rate by Channel
Churn Risk Distribution
Feature Impact on Retention (A/B Testing)
Feature Rollout Results - Retention Improvement by Cohort
| Feature | Test Cohorts | Control Group | M3 Retention Lift | Confidence Level | Impact |
|---|---|---|---|---|---|
| Advanced Reporting | 89% | 75% | +18% | 95% | High Priority |
| API Access | 92% | 81% | +14% | 92% | High Priority |
| Custom Branding | 84% | 79% | +6% | 78% | Medium Priority |
| SSO Integration | 88% | 76% | +16% | 94% | High Priority |