A Beginner's Guide to Cohorts and Cohort Analysis

What is Cohort Analysis?

What will you call your group when you share common interests and tastes? Yes, you got it right. It’s called a cohort.

A Cohort is a group of users who share common characteristics in analytics. Cohorts share common features or experiences within a defined duration. A cohort is a type of behavioral analytics. A group is created according to shared traits. This helps to better track and monitor to understand their activities. Cohort Analysis helps you to figure target-oriented questionnaires. This process helps understand user churn causal. You can then develop product strategies based on that for user retention. When churn reduces, your revenue increases.

Cohort Analysis is a subunit of behavioral analytics. You can collect user behavior data from any application platform. It can be a web application or a gaming application. Here we sort users into different or related groups rather than a single unit. Instead of looking at all customers, Cohort Analysis focuses on small, connected groups. Smaller groups are isolated according to different parameters for analysis.

Measure the engagement of users in a website using Cohort Analysis. This analysis measures the improvement in user engagement. Sometimes user engagement is misunderstood to be better when website usage grows.

Cohort analysis segregates growth metrics and engagement metrics as these parameters are different. Sometimes growth can mask engagement metrics. This may affect the number of old users churning away from the app or platform under the growth numbers. This denotes the lack of engagement of website or application users over a period.

To reckon why users lost interest and seized to use the app the three W of user retention need to be addressed:

  • Who is engaged in your app or website?
  • When do they turn away?
  • Why do they lose interest?

Sorting users on different parameters sharing the same trait will help. The two types of Cohorts are:

  • Acquisition Cohorts: These are groups segregated during signup for your products or services.
  • Behavioral Cohorts: These are groups divided according to the behavioral pattern of the user.
Things to consider while studying Acquisition Cohort Analysis

How do we Employ Cohorts in Business Analytics?

In a specific time-duration, you can compare Cohorts or analyze single cohorts. This helps to identify a pattern of growth that supports growth metrics. Consider the following example. A greater number of users are added via display ads than on social media.

Examples of cohorts: We can use the Cohort Analysis to find the engagement metrics and growth metrics. In another instance, if we want to figure the reason for average retention in a business. We can use Cohort Analysis and create a correlation between the first purchase and the churn. We can represent users on their first purchase and plot against monthly retention.

Correlation doesn't imply to cause and effect of customers. Cohort analysis provides us insight into the tendency and ground for testing. Not the cause.

In a specific time-duration, you can compare Cohorts or analyze a single cohort. This helps to identify a pattern of growth that supports growth metrics. Consider the following example. The addition of a greater number of users happens via display ads than on social media. Let’s understand cohort analysis techniques in more depth.

Check When Users Churn

By Acquisition analysis, you can find out when in the customer lifecycle users churn. You can represent a timeline of the number of customers acquired in a time interval in one axis. The other axis represents the amount of time the user devoted. The point of intersection of both parameters denotes the acquisition number of customers.

Things to consider while studying Acquisition Cohort Analysis.

  • Time Period: Apply shorter periods for small size companies and longer periods for older companies.
  • Scope: Keep the scope of the retention period low to understand the retention accuracy. Example analyzing several days as low as weekly, quarterly, and half-yearly.
  • Expectations: The retention rate by and large depends on the segment. For example, for an easy download application, the churn rate would be relatively high. But if you keep multiple securities and barriers for downloads then the churn rate would be less.

Find the Tricky Features

Users churning away constantly in a time frame helps to understand behavioral causes. Behavioral Cohort helps us to figure what’s happening with users on a particular day. Some pointers to keep in mind for example:

  • Watch users who engage with the application features in the initial first two weeks
  • Monitor the users who regularly engage with the social features like a chatbot, in-app mail, etc
  • Monitor those users who permit push notifications during the customizing of their application

For certain user behavior, the correlation between behavior and retention will be apparent. The app engagement in the initial 15 days may not provide insights into user engagements. So, the average churn rate for an app is based on acquisition Cohort analysis.

Compare Behavioral Cohorts

It is a tough task to figure one persisting link between behavior and retention. It’s a blend of behaviors that keep any user engaged in an app. You need to identify a common behavior that incites user engagement. This also helps you to understand why users are not sticking around or engaged with the app. You can use a spreadsheet with defined parameters to study customer retention.

Cohort analysis is important for many reasons. It will enable you to know which customers are leaving. You can know why they leave so you can find a solution. It will help drive growth, engagement, and revenue for your platform or app.