What is cohort analytics?
Cohort analytics is a type of analytics that uses data to break users into specific groups, or cohorts, based on certain common characteristics.
Cohort analytics creates defined segments, or “cohorts,” of users based on common important characteristics or experiences, so you can identify patterns of behavior across the user lifecycle.
These characteristics can be, but aren’t limited to:
- demographic (e.g., age or location)
- behavioral (e.g., number of times a feature is used or amount of purchases made)
- technographic (e.g., app or SDK version)
Why is cohort analytics important?
Cohort analytics goes beyond generic segmentation, breaking down your users based on specific characteristics or actions they complete within a defined time-span.
Cohort analytics allows you to track your users and analyze their behavior separately, or cater to their specific needs. Common examples of cohorts include:
- Power users – Users that made 3 or more purchases in the last 7 days
- Recent upgrades – Users that upgraded their plan in the last 30 days
- Inactive users – Users that haven’t used your app in the last 14 days
These cohorts can also include criteria to include or disqualify users with specific characteristics (e.g., use an iPhone or log in on Chrome).
Using cohorts provides you with more insights by allowing you to be more particular in analyzing your users instead of relying on broad metrics that may be misleading.
For example, a decrease in your total users can be alarming and calls for immediate attention, but you can’t take action unless you know which users specifically are no longer active. By breaking down your overall user base into free users and paid users, you can find out which of the two is decreasing more rapidly and act on the insights more effectively. For example, if more of your free users are leaving, you can try giving them a month long trial to paid features.
Cohort analytics can help you learn where advanced users need more features, where less advanced users need more guidance, and where every user faces friction in their user flow. cohorts. Cohorts analytics show product teams where advanced users need more features, where less advanced users need more guidance, and where every cohort typically runs into trouble along their user journey.
Learn how tracking cohort growth over time can help you grow your business.
How do I run a cohort analysis?
Cohort analytics platforms exist to automate the tedious parts of cohort analysis. Without them, teams must design their own system in-house or export data into a Microsoft Excel spreadsheet and pepper it full of complex equations. Each time the team tweaks or tests their product, they’ll need to export the data again manually. A cohort analytics platform eliminates steps in this process. It integrates into the product directly to track cohorts, alerts teams to meaningful changes in cohort behaviors, and makes algorithmic suggestions. There are 4 steps to performing cohort analysis:
1. Frame the question
Finding an answer requires you to know what questions to ask. The first step in running a cohort analysis is to frame the question you want to know the answer to. Examples:
- Who are my power users?
- Which users are not opting in to new features?
- How many high-value users use Chrome?
2. Define what you’re looking for
Once you’ve framed your question, figure out a way to define the aspects of the question that will help you get to your answer. This helps you define your cohort. Examples:
- What’s my definition of a power user?
- Which specific features am I thinking about?
- What constitutes a high-value user? A paid account? Lots of purchases?
3. Create the cohort
Now that you’ve defined what the cohort should be, creating a cohort is simple. All you need to do is enter the criteria and logic for someone to qualify for that cohort in your product analytics tool.
- Who are my power users?
- Which users are not opting in to new features?
- How many high-value users use Chrome?
Cohort analytics can help you find a lot of insights on your users, how they use your product, and what they love or hate about it. Try it with your data to see how it can help you understand your users better.