The Smartest Way to Track Retention (Without Fooling Yourself)
Most founders track growth but not enough track who’s sticking around.
That’s where cohort analysis comes in. It shows you whether users are coming back, dropping off, or getting more valuable over time.
If you want to understand product-market fit, retention, and long-term revenue, cohort analysis is your best friend.
What Is a Cohort?
A cohort is a group of users who share a common characteristic or experience within a defined time period. Most commonly used one is the time they started using a product or service. For example, all users who signed up in January form a January cohort. Cohort analysis is the process of tracking and analysing these groups over time to understand patterns such as retention, engagement, or revenue.
Key Points to Consider:
- Cohorts can be based on other shared characteristics, not just sign-up date (e.g., first purchase, feature adoption).
- The main purpose of cohort analysis is to observe how specific groups behave over time, which helps in making data-driven decisions.
Example:
A cohort could be all users who made their first purchase in March, and cohort analysis would track how their purchasing behaviour changes in the following months.
Cohort analysis lets you track what those users did later:
- Did they return in Month 2?
- Did they place another order?
- Did they upgrade or churn?
Instead of looking at users overall, you track behaviour by group based on shared characteristics or start dates. This approach makes it much easier to identify what’s truly working, uncover trends, and spot issues that might be hidden in overall averages.
Example
You onboard 100 users in January and 100 in February.
- In Month 1: 100% of each cohort is active (they just joined)
- In Month 2: 50% of Jan users return, but only 30% of Feb users do
- In Month 3: 30% of Jan users are still active, but only 10% of Feb
That tells you February retention is weaker. It could be because of any reason which maybe that your onboarding changed or February new users were lower intent.
Why Cohort Analysis Matters
| Insight | What It Helps With |
|---|---|
| Product-market fit | Are people using your product long-term? |
| Retention health | Are users dropping off after onboarding? |
| Marketing quality | Are you acquiring the right kind of users? |
| Growth forecasting | How much repeat usage can you expect? |
| Revenue expansion (SaaS, D2C) | Are customers increasing spend over time? |
What to Track in a Cohort Table
| Time Period | Number of Users | Month 1 | Month 2 | Month 3 | Month 6 |
|---|---|---|---|---|---|
| Jan Cohort | 100 | 100% | 50% | 30% | 15% |
| Feb Cohort | 100 | 100% | 30% | 10% | 5% |
The higher the drop-off, the faster you’re losing users.
The “stickier” the percentages, the closer you are to retention-driven growth.
India-Specific Startup Use Cases
- D2C brands track repurchase rate by cohort
- SaaS startups use MRR or NRR per signup month
- Fintechs measure feature adoption post-onboarding
- Edtech, Healthtech, and Social Media track user dropout patterns after Day 7 or Day 30
When to Start Using Cohort Analysis
- If you’re past MVP and have substantial number of users per month
- If your numbers look fine, but you’re not sure who’s actually staying
- If growth looks good but LTV isn’t improving
- If investors are asking about retention or usage quality
Common Mistakes
- Looking only at total active users (without splits)
- Not comparing cohorts across product changes
- Ignoring small but loyal segments
- Confusing acquisition spikes with true engagement
Pro Tip
Combine cohort analysis with qualitative feedback from each batch.
If retention dipped in March, talk to March users. Their feedback + data = better product fixes.
Final Thought
Growth isn’t just about new users, it’s about how long they stay and how much they love you.
Cohort analysis turns chaos into clarity. When you measure who sticks and why, you stop guessing and start building for loyalty.