Cohort Analysis

Cohort Analysis Meaning

Cohort Analysis is the process of breaking down the data into small groups called cohorts and using them for analysis. It could be customer data from e-commerce websites, game user data, or data on streaming service subscribers.


The groups are related to each other, have a defined time frame and share a common statistical trait. Cohort analysis comes from behavioural analysis. As a result, they help in studying customer behaviour and improving their experience.

For instance, you find appropriate recommendations on YouTube because someone is analyzing your viewing preferences. This helps the company to serve you well.

Example of Cohort Analysis

Let’s dive into some detailed examples of cohort analysis. It has gained much relevance today because businesses have moved closer to their customers. Therefore, marketing studies frequently use such tools of analysis.

Using cohort analysis, marketing campaigns allow firms to compare their customers based on different factors. The factors could be spending patterns of customers, product reviews, preferences, etc. The comparison helps in making strategic marketing decisions.


Take the example of an e-commerce business that generates massive data on its customers. The data ranges from purchased products, customer spending, click-through rate, product ratings, product returns, and other metrics.

Cohort analysis conducted by e-commerce businesses will show them the behavioural patterns in a customer’s life cycle. This helps in preparing strategies to target the customers better to boost customer retention and engagement.

Example#2 Another example is when the existing users are tracked and compared across different periods. Do not confuse cohorts with segments. The segments are not defined by a period of time.

Check out this illustration:

Cohort Analysis graph

In this example, a web page owner wants to evaluate the traffic on his web page and the revenue it is creating. The following are some denotations:

  • Series 1 – New-users revenue
  • Series 2 – Old-users revenue
  • Series 3 – Monthly revenue (Add series one and series 2)

In the given illustration, the webpage owner is carrying out an analysis by classifying cohorts on time-basis.

He then makes the following classifications based on his analysis.

  • Cohorts in the time-period August-October have amassed the highest revenue in the new-user segment (as a proportion of monthly income)
  • Cohorts in the time-period January-March have the lowest revenue in the new-user segment.
  • Despite higher revenue from new-user cohorts, monthly income did not rise because of low payment from old-users.

This chart gives the webpage owner useful patterns which can help him undertake strategic business changes.

How to perform Cohort Analysis?

It can be completed in the manner described below.

Cohort Analysis

#1 – Determine the Objective of the Analysis

Like most analysis, cohort analysis also needs to define certain objectives that it has to fulfil. Examples could be finding the revenue generated by a website. Or complex issues like strategising for improvements to the webpage traffic.

#2 – Carve out the Metrics that Associate with the Objectives

After having the determined objective of the analysis, the analyst should look for appropriate metrics. The data is separated using metrics that also define the features of cohorts. Some simple examples of metrics are the number of retained customers, the number of tickets sold, the per-user fee generated, etc.

#3 – Determine if all Cohorts are Necessary

If the study is about finding customer retention rate on a webpage, then the analyst should appropriately determine which customer cohort would best serve the study’s objective. The available options could range from certain old customers, new customers, one-time customers, etc.

#4 – Conduct the Analysis

After diligently carrying out the steps mentioned above, the analyst can start doing his analysis. Retaking the same example. The web page owner can ascertain how his webpage has fared in different metrics over a period of time. These could be like customer views, customer retention, call to action, etc.

During this analysis, the analyst should be careful in determining the research’s actionable insights. The research will always give a true picture. Be careful not to hold any biases that could hinder the objectiveness of the findings.

#5 – Prepare and Present the Results

Note down the results of the analysis in an appropriate format. They could be charts, tables, or a summarized text. The results of the analysis should be clearly communicated to others.


  • Cohort analysis gives its users accuracy and effectiveness when they segregate large sets of data.
  • The data comes with a varying variety making it difficult to classify them easily. By its nature, this analysis is a tool to address this problem.
  • From the purpose of business, it helps the marketing and sales teams in classification. They can easily classify their clients based on their engagement over the years. So, it assists in easy and quick decision making.


  • Biases – Most analysts possess some form of biases or prejudice. The study may lose objectivity if it falls prey to the prejudices of the analysts. Biases could be selection bias, decision bias, personal bias, etc.
  • Only such data that are statistical in nature can be used for this kind of analysis.
  • The traits have to be defined by a defined period of time.

Key Takeaways

  • Cohort analysis is the process of classifying data into different groups called cohorts. The groups have common traits and are defined by a fixed time period.
  • After that, the groups are analysed thoroughly with the use of certain metrics.
  • Cohort analysis is an important marketing tool that is used for targeting customers in a better way.

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