What if you could predict which users will stick around and which will churn before it happens? Cohort analysis gives you exactly that superpower—showing user retention patterns that help you build a stickier product and predict revenue.
Our cohort analysis tool makes retention tracking effortless with visual heatmaps, automatic calculations, and insights that turn data into action plans.
See Your Retention Patterns In Action
Start tracking weekly and monthly cohorts in under 5 minutes. No complex setup required.
What You'll Learn
- How to read cohort heatmaps and spot retention patterns instantly
- The difference between healthy and concerning retention curves
- Real examples of companies who improved retention by 60%+ using cohort insights
- How our automatic cohort tracking makes analysis effortless
How Our Cohort Analysis Works
📊 Visual Cohort Heatmaps
Our cohort tables use color coding to make patterns instantly visible. Dark green = strong retention, red = concerning drop-offs. No more staring at spreadsheets.
Example: Week 1 cohort shows 45% Day 1 retention (green), 22% Week 1 (yellow), 15% Week 4 (steady orange) - healthy pattern.
📈 Weekly & Monthly Tracking
Automatically groups users by signup week or month and tracks their return behavior. Switch between timeframes to find the right granularity for your business.
Pro tip: B2B tools work best with weekly cohorts, consumer apps with daily or weekly.
🎯 Custom Retention Events
Track any action as your retention metric: logins, purchases, feature usage, or custom events. Our system automatically calculates percentages and trends.
Example: SaaS company tracks "created project" instead of just logins for better retention signal.
🔍 Cohort Comparison Tools
Compare cohorts before/after product changes, across traffic sources, or between user segments. See exactly which changes improve retention.
Use case: Compare retention for users from different marketing campaigns to optimize acquisition spend.
Real Cohort Success Stories
LearnFlow: 68% Improvement in Course Completion
Problem: Students started courses but rarely finished them.
Cohort Insight: Week 2 retention was only 12%. Students who completed the first module had 4x higher completion rates.
Solution: Redesigned first module to be shorter and more engaging. Week 2 retention jumped to 32%, course completion increased 68%.
TeamSync: $2.1M ARR Saved Through Churn Prevention
Problem: Monthly churn was creeping up but they couldn't pinpoint why.
Cohort Insight: Users who didn't invite teammates within 7 days had 70% higher churn. Recent cohorts showed declining invite rates.
Solution: Added teammate invitation prompts in onboarding. Invite rates increased 45%, saving $2.1M in potential churn.
FitTracker: 3x Retention Through Habit Formation
Problem: Fitness app users would use it intensely for 2 weeks then disappear.
Cohort Insight: Users with 7+ consecutive days of logging had 85% Month 2 retention vs 15% for others.
Solution: Added streak tracking and achievement badges. 7-day streak rate doubled, Month 2 retention tripled.
🎯 The Cohort Optimization Framework
- Identify your "aha moment" - the action that correlates with long-term retention
- Use cohorts to measure what % of users reach this moment
- A/B test changes to increase aha moment completion
- Monitor cohort retention improvements across all periods
How to Spot Retention Patterns
✅ Healthy Patterns
- Smile Curve: Initial drop followed by stabilization (40% → 25% → 22% → 20%)
- High Week 1: 30%+ retention after first week signals strong product-market fit
- Improving Cohorts: More recent cohorts show better retention than older ones
- Flat Tail: Retention stabilizes rather than continuously declining
⚠️ Warning Signs
- Cliff Drop: Retention falls off rapidly with no recovery (40% → 8% → 3%)
- Low Week 1: Under 20% retention suggests onboarding issues
- Declining Cohorts: Newer users have worse retention than older ones
- No Plateau: Retention keeps declining without stabilizing
Getting Started with Cohort Analysis
How quickly can I see cohort data?
Immediately after setup. Our system retroactively builds cohorts from your existing data, so you'll see historical patterns right away. New cohorts update in real-time.
What's the difference between cohort analysis and retention reports?
Cohort analysis groups users by time period and shows how each group behaves. Retention reports typically show overall retention averages. Cohorts reveal trends and let you compare specific time periods.
Can I segment cohorts by user properties?
Yes. Filter cohorts by traffic source, device type, plan type, geographic region, or any custom property you track. This helps identify which acquisition channels bring the stickiest users.
How does cookieless tracking affect cohort accuracy?
Our cookieless system actually improves cohort accuracy by tracking 100% of users (not just those who accept cookies). User identification persists across sessions and devices for more reliable retention measurement.
Ready to Understand Your User Retention?
Start tracking cohorts today and discover the patterns that drive sustainable growth.
No credit card required • See historical cohorts immediately