Cohort Analysis
Cohort analysis is an analytics method in which users are divided into groups (cohorts) based on a common characteristic (usually the time of their first action), and then their behavior is tracked over time.
Simply put:
We compare different groups of users and see how they behave over time
What is a Cohort
A cohort is a group of users united by a common event, for example:
- they signed up on the same day/month;
- they made their first purchase in the same period;
- they installed an app in the same week.
How Cohort Analysis Works
Example:
- Cohort A: users who came in January
- Cohort B: users who came in February
Then compare:
- how many of them returned after 7/30 days;
- how many made a purchase;
- how their behavior changed.
What Can Be Analyzed
- User retention;
- Repeat purchases;
- LTV (customer lifetime value);
- Engagement;
- Conversions over time.
Example
| Cohort | D1 Retention | D7 Retention | D30 Retention |
| January | 40% | 25% | 10% |
| February | 50% | 30% | 15% |
The February cohort is clearly stronger.
Why Cohort Analysis Is Useful
It helps to:
- understand traffic quality;
- evaluate user retention;
- compare the effectiveness of marketing channels;
- identify changes in product or user behavior;
- forecast LTV.
Where It Is Used
- SaaS products;
- Mobile apps;
- E-commerce;
- Subscription services;
- Marketing analytics.
How It Differs from Regular Analytics
- Regular analytics shows “average values”
- Cohort analysis shows dynamics across user groups over time
Key Takeaway
Cohort analysis is a method of analyzing users by groups that helps understand how their behavior changes over time.
It is especially important for evaluating retention, traffic quality, and long-term customer value.
