Cohort AnalysisvsChurn Rate

Cohort analysis groups customers by their signup date (or another shared attribute) and tracks their behavior over time. Instead of looking at blended metrics that mask trends, you see how each 'class' of customers performs independently. A SaaS company with 5% monthly churn might discover that January cohort churns at 3% while March cohort churns at 9% — the blended 5% hides a deteriorating acquisition quality problem. Amplitude found that companies using cohort analysis identify retention problems 6-8 weeks earlier than those using aggregate metrics.

Churn rate measures the percentage of customers who cancel or stop paying during a given time period. It is the silent killer of SaaS businesses — even a small monthly churn compounds into massive annual losses. A 5% monthly churn sounds manageable, but compounded over 12 months, you lose 46% of your customer base. To maintain the same revenue, you need to acquire enough new customers to replace nearly HALF your base every year. This is why the best SaaS companies obsess over churn — Slack's monthly churn below 1% means they retain 89% of customers annually, creating a compounding revenue machine.

The trap is treating all customers as one pool. Blended metrics create dangerous illusions: your overall retention might look stable at 85%, but if Q1 cohorts retain at 95% and Q4 cohorts retain at 70%, you have a ticking time bomb. By the time blended metrics show the drop, the damage has compounded for months. Another trap: analyzing cohorts too narrowly (daily) creates noise, or too broadly (annually) hides actionable trends. Monthly cohorts are the sweet spot for most SaaS businesses.

The trap is tracking only 'logo churn' (customers lost) and ignoring 'revenue churn' (revenue lost from downgrades). You could have 3% logo churn but 8% revenue churn if your largest customers are downgrading. Revenue churn is more dangerous because it hits your top line harder. The second trap: calculating churn from the wrong denominator. Always use start-of-period customers, not end-of-period or average. Using end-of-period inflates your denominator and makes churn look artificially low.

Build a cohort retention table: rows = signup month, columns = months since signup. Calculate retention rate for each cell. Look for two patterns: (1) Vertical drops — if a specific cohort has abnormally low retention, investigate what changed in acquisition that month. (2) Diagonal patterns — if ALL cohorts drop at month 3, you have an onboarding or value-delivery problem at that stage. Target: Month 1 retention ≥ 80%, Month 12 retention ≥ 50% for healthy SaaS.

Calculate two churn metrics monthly: Logo Churn = Customers Lost ÷ Start-of-Month Customers × 100. Revenue Churn = MRR Lost (cancellations + downgrades) ÷ Start-of-Month MRR × 100. Implement an exit survey on your cancellation page to identify the #1 reason people leave — the top reason is usually fixable. Target: under 5% monthly for SMB SaaS, under 2% for mid-market, under 1% for enterprise.