Cohort Analysis Types and How to Do It
Do new users stay engaged with your product after their first month? If you don’t know, cohort analysis is a great tool to report on customer retention.
Many companies track retention or active users monthly without considering how the user journey affects these numbers. As a result, their reports distract their teams from problems that are not worth solving.
That said, cohort analysis helps you focus on the customer lifecycle and watch how engagement levels fluctuate after two or more weeks. So let’s go over:
- What cohort analysis is.
- The two types of analysis.
- The key benefits you should get from it.
- How to perform effective cohort analysis.
- A real-life cohort analysis example to learn from.
What is cohort analysis?
Cohort analysis is a data-driven method used to track and analyze specific groups of users, known as cohorts, based on shared characteristics or behaviors over time. This allows you to understand how user engagement evolves, as well as identify patterns like when users are most likely to churn or which actions increase long-term retention.
What are the two types of cohort analysis?
Cohort analysis can be broken down into two main types: acquisition-based and behavior-based.
Both types allow you to track user behavior and retention over time, but they differ in how they group users and what kind of questions they help answer. So let’s look into them:
- Acquisition cohort: Groups users based on when they first signed up for your product or service. Typically used to track retention and engagement over time, showing how new users behave after they join.
- Behavioral cohort: Groups users based on specific actions they’ve taken within the product, such as completing onboarding or using a particular feature. This is used to see how certain behaviors impact retention and engagement compared to others.
What are the key benefits of cohort analysis?
Cohort analysis goes beyond traditional user tracking by allowing you to dive deeper into how specific groups of users go through the user journey.
Instead of simply looking at overall user numbers or engagement, cohort analysis shows the exact of users who joined each month/week and measures their retention over time.
Not only that, it also lets you filter these cohorts based on in-app behaviors—and compare how their usage of your product affects their retention.
This level of detail brings many key benefits:
- Helps you identify exactly when and why specific users churn.
- Allows you to see which actions drive long-term user engagement to different cohorts.
- Helps prioritize product changes by understanding which features boost retention.
- Provides clear data to support strategic decisions around user growth and product development.
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How to do cohort analysis step by step?
To effectively use cohort analysis, there are a few tricks you can follow to get insights into your user base, create a strategy, and optimization test.
Here are the steps to do it:
Use acquisition cohorts to identify when users churn
This step involves grouping users by their acquisition date to track their retention over time.
To perform this, first organize your users into cohorts based on when they first signed up (weekly or monthly cohorts are common). Then, analyze each cohort’s retention rates over time to see the weeks or months when users start dropping off.
This way, you can pinpoint the critical moments when engagement declines. For example, if users who joined in January have a sharp drop-off after the third week, you can investigate potential causes. It could be caused by ineffective onboarding, feature complexity, or anything that happens during that period.
Then, with this insight, you can start drafting a strategy to address churn causes and improve your retention rates.
Define core events to build behavioral cohorts
You can beyond the acquisition date and define a few core events within your product that indicate engagement or progress, such as “completed profile,” “invited team members,” or “made a purchase.” Use these events to build behavioral cohorts within your acquisition cohorts.
For instance, within your June cohort, you can compare the retention curve of users who came back to complete event X (e.g., “used Invoice”) versus those who completed event Y (e.g., “used Report”).
This reveals which events or features correlate with higher retention, highlighting the “sticky” aspects of your product for that cohort.
To go even further, you can compare it with other acquisition cohorts and ask some questions, for example:
- Was there any difference in their onboarding process?
- Did retention improve with the cohorts that experienced the revamped onboarding process you implemented two months ago?
This behavioral analytics process can give you the foundations to understand what features can be more valuable for your users and start a retention strategy.
Iterate your findings and develop retention strategies
By now, you know where the churn rate is highest and which features are “sticky.” This means you can start creating specific retention strategies to address those issues.
Basically, this is the phase where you begin testing your educated assumptions and planning interventions based on the insights you got earlier. And it usually involves implementing specific changes at the right points in the user journey and monitoring if they make a difference.
For example, if you found a cohort with a higher drop-off after their trial period, you can try introducing “sticky features” earlier in the onboarding process to see if users stick with it throughout their trial period (and eventually become paid users).
Test and repeat
Once your changes are in place, monitor the retention rates of future cohorts to see if they improve.
If retention increases, great—you’ve found a solution! If not, use the cohort data to tap into more insights, repeat the process, and keep testing.
For example, after analyzing your user cohorts, you noticed a worrying trend: a significant drop-off in retention after the free trial period.
Your initial hypothesis was that users weren’t fully grasping the value of your product, so you revamped your onboarding experience to introduce a key feature earlier on. However, after implementing this change and analyzing new cohorts, you found no improvement in retention.
This led you to a new hypothesis: users need more guidance and incentives to fully adopt all core features, not just the one highlighted in the onboarding. Perhaps they were trying that one feature, but missing other crucial aspects of your product that deliver the real value.
Now, you might consider implementing an interactive tutorial that walks users through each core feature step-by-step. Or, you could introduce a reward system that grants users badges or discounts for completing specific actions related to each core feature.
What is a real-life example of cohort analysis?
Customer retention cohort analysis is often used to improve user retention by identifying the causes of customer churn.
Here’s our hypothetical scenario:
As a product manager of an instant messaging app, you’re trying to boost your retention figures.
You start by conducting an acquisition cohort analysis just like the one below in order to find out when users stop engaging with the core feature of your app. We are looking at repeat engagement of the same feature here.
The chart reveals that most users churn between days 2 and 3 of engaging with the feature for the first time. There could be multiple reasons for that, so you work with your team to create a couple of hypotheses:
- Users who complete the onboarding checklist, have better retention.
- Users who don’t invite at least 3 teammates within the first 2 days, tend to churn.
To test the hypothesis, you create behavioral cohorts:
- users who complete and don’t complete the onboarding checklist.
- users who invite 3 or more teammates in the first two days and those who don’t.
For each, you create the report and compare the trends. This reveals that completing the checklist doesn’t affect retention dramatically. However, inviting 3 teammates does.
Based on that, you decide to optimize the checklist so that it drives the desired behavior (teammate invites).
After updating it, you follow up with another cohort analysis where you compare the before and after retention figures.
The new checklist has improved your retention but there’s still room for improvement, so you keep iterating on it to optimize it even further.
Cohort analysis FAQs
Is cohort analysis good?
Cohort analysis is essential for understanding how specific groups of users behave over time. It offers actionable insights into retention, churn, and user engagement patterns, allowing you to make data-driven decisions that lead to better product development.
What is the difference between cohort analysis and retention?
Their main difference is that cohort analysis is a method used to track and analyze user groups, while retention measures how long users continue to engage with a product over time.
In this context, cohort analysis helps break down retention data by grouping users into cohorts, providing a more detailed view of when and why users drop off.
What is the difference between segmentation and cohort analysis?
Segmentation divides users based on demographic, geographic, or behavioral traits at a single point in time, and it’s normally used for personalization. Meanwhile, cohort analysis often groups users who signed at a specific date over a period of time with the goal of comparing their retention, engagement, and behaviors.
In short, cohort analysis focuses on how customer behavior evolves, while segmentation is more static.
Conclusion
Cohort analysis is a powerful tool for understanding user behavior and improving retention by identifying when and why users drop off.
Whether you’re looking to perform customer churn analysis, identify sticky features, or improve your onboarding process, cohort retention analysis provides the data-driven clarity needed to make informed decisions.
Want to see how Userpilot can help you perform cohort analysis and implement retention strategies? Book a demo to see how you can do this without coding anything!
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