Retention rate is percentage of customers who continue paying over time. Inverse of churn. Critical SaaS metric—high retention enables compound growth. Target: 90%+ monthly retention (10% churn). Cohort analysis essential.
Retention shows product stickiness. Calculation: Monthly Retention = (Customers at end of month ÷ Customers at start of month) × 100. Example: 100 customers on Jan 1, 95 remain on Feb 1 = 95% retention (5% churn). Cohort retention (better metric): Track specific signup cohorts over time. January cohort: Month 0: 100 customers. Month 1: 60 remain (60% M1 retention). Month 2: 48 remain (48% M2 retention, 80% M1→M2 retention). Month 6: 35 remain (35% M6 retention). WHY IT MATTERS: Retention determines if startup can scale. 95% monthly retention: Customer stays 20 months average. 90% retention: 10 months. 80% retention: 5 months. 50% retention: 2 months (impossible to scale). Compound effect example: 100 new customers monthly, 95% retention: Month 12: 1,140 customers. 100 new monthly, 80% retention: Month 12: 469 customers (2.4x difference). Retention benchmarks: B2B SaaS: 90-95% monthly (excellent), 85-90% (good), <85% (problem). B2C SaaS: 80-90% monthly. Consumer apps: 40-50% Month 1, 30-40% Month 2 (acceptable). Gaming: 20-30% Day 7 retention (normal). Improving retention: Onboarding (get to aha moment Week 1), Feature adoption (engage with core features), Customer success (proactive support before churn), Product improvements (fix reasons for churn), Engagement loops (email, push notifications bringing users back). Retention > Acquisition. Better to keep 95/100 customers and add 50 new (145 total) than keep 80/100 and add 100 new (180 total) because first scenario compounds better long-term.
Retention % = (Customers Remaining ÷ Starting Customers) × 100. Churn % = 100 - Retention %. Average Customer Lifetime = 1 ÷ Churn Rate93%+ monthly retention (7% churn). Average customer stays 14+ months. High retention = predictable revenue, enables content investment.
75% monthly retention (25% churn). Average customer lifetime 4 months. Needed constant acquisition to grow—sold for $1B but less valuable than 90%+ retention SaaS.
Had 70% monthly retention (30% churn). Average customer stayed 3 months. Couldn't raise Series A—investors saw leaky bucket. Shut down despite ₹50L monthly revenue.
Retention determines business viability. 95% retention enables unicorn path—revenue compounds. 80% retention = constant struggle to replace churned customers, can't scale efficiently. High retention increases LTV dramatically: 90% retention (10% churn) = 10 months average. LTV = ₹10K/month × 10 months = ₹1L. 95% retention (5% churn) = 20 months. LTV = ₹10K × 20 = ₹2L (double the value!). VCs won't fund <85% retention SaaS.
90%+ monthly retention (10% churn) for B2B SaaS. 85%+ acceptable for B2C. Below 85%, VCs concerned about PMF—unlikely to fund.
Track cohorts: January signups → measure monthly (Jan: 100, Feb: 90, Mar: 85...). Plot retention curves. Goal: Flatten curve (retention stabilizes) not straight down (continuous bleeding).
No PMF (product doesn't solve problem well enough), Poor onboarding (users don't reach value), Missing critical features (competitors better), Bad support (frustrated users leave), Wrong customers (targeting non-ICP).
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