Cohort AnalysisvsEngagement Metrics

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.

Engagement metrics measure how actively and deeply users interact with your product. The most important is the DAU/MAU ratio (Daily Active Users ÷ Monthly Active Users), also called the 'stickiness ratio.' A 50% DAU/MAU means half your monthly users come back every day. Facebook's DAU/MAU is 66%, making it one of the stickiest products ever built. For SaaS, a 13-20% DAU/MAU is average, 20-30% is good, and 30%+ signals exceptional engagement that predicts strong retention.

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 surface-level engagement (page views, sessions) instead of meaningful engagement (core actions completed). A news site with 10 million page views but 2-minute average session time has shallow engagement — users skim headlines and leave. A project management tool with 500K sessions where users create tasks, assign team members, and complete workflows has deep engagement. Vanity engagement metrics (views, clicks) correlate poorly with retention; value-delivered metrics (workflows completed, goals achieved) correlate strongly.

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.

Define your 'engagement stack' — 3 tiers of user engagement: (1) Passive: user logged in / opened the app. (2) Active: user performed a core action (sent a message, created a document, ran a report). (3) Power: user used advanced features or collaborated with others. Calculate DAU/MAU and track all three tiers separately. Target: at least 40% of MAU should be 'Active' tier. Set engagement alerts: if a user drops from Power to Passive, trigger a Customer Success touchpoint within 48 hours.