Understanding Cohort Analysis: A Comprehensive Guide 101

• October 27th, 2021

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Long term success of a business depends not only on the acquisition of new customers but also a lot of it depends on retaining those customers and providing them with customized experiences catered to their requirements. Due to the large amounts of data that is being generated due to operations and sales of the companies, there has been a rise in the practice of implementation of Business Analytics to get useful insights from this Data to improve the profits and the customer experience. This is where Cohort Analysis fits in and helps provide a targeted experience to the customers. 

In this article, you will learn in-depth about Cohort Analysis, its importance, and the general methodology that is to be followed for its implementation.

Table of Contents

What is Cohort Analysis?

Cohort Analysis
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Cohort Analysis is a type of Behavioral Analytics in which large volumes of complex Data is broken into related groups to perform Analysis. These smaller groups or Cohorts tend to share common characteristics or experiences within a specific time span. This process enables an organization to recognize important trends and patterns across the life cycle of a user/customer through a Cohort rather than accessing each individual customer.

An organization can then strategize and optimize its techniques and tactics to target specific Cohorts. Cohort Analysis should not be confused with Cohort study in which Data is broken down into similar groups, whereas in Cohort Analysis, the Analysis is performed in regards to Big Data and Business Analytics.

Cohort Analysis is also used as a tool to measure user engagement over a period of time. Data from a given eCommerce platform, web application, or online game is used for Analysis and rather than looking at all users as a unit, it breaks them down into related groups.

Deep Actionable Cohort Analysis

The technique of Cohort Analysis is helpful in dynamically developing plans and strategies as it provides in-depth and actionable information rather than just being a vanity metric to document the current state of business to be included in the reports. Since just having the information about the number of users at a platform can prove to be useless, Cohort Analysis can provide actionable insights across different customer demographics to cater to their needs in a targeted manner.

Understanding Cohort Analysis with Example

Cohort Analysis: Customer Spending by Referral Source
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Techniques like Cohort Analysis help in deriving actionable insights. A social media platform may have a large undifferentiated dataset of all the users on the platform. This technique can help differentiate users across multiple user types depending upon the Time, Engagement, and various other trigger events. 

Like customers who log on to the platform on a daily basis can be put into one Cohort, users which use the platform only once a month can be put into another Cohort, users subscribing for ad-free service can be put into one Cohort. 

These different Cohorts can help the organization target the requirements of these users better and strategize their services and products accordingly. The analysts of the social media platform would be catered with a visualization of these different groups, helping them to track the behavior of these users in a targeted manner.

Steps to Perform Cohort Analysis

There are 5 general steps that are required to be performed under Cohort Analysis: 

Step 1: Determining the Right Set of Queries to Ask 

Cohort Analysis: Asking correct Questions
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There needs to be some structure to the Analysis to come up with information that is relevant to improving the product or service being offered by the organization. Therefore the right set of questions need to be asked and evaluated to ensure that.

Example: For a content platform, the right set of questions could revolve around the subject of increasing user engagement duration.

Step 2: Defining the Metrics

Cohort Analysis: Choosing Right Metrics
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The Analysts need to define the metrics on which the Data needs to be evaluated, it also includes identification of relevant events that can occur and need to be tracked

Example: user engagement, free trial activation, etc. Correlation between different events and metrics is also used to track certain trends.
Example: Increased user spending due to updated promotional offers.

Step 3: Defining the Specific Cohorts 

Defining specific Cohorts  - Cohort Analysis
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There is a requirement for analyzing all users or performing attribute contribution in order to find the relevant differences between them and creating the Cohorts in the process. 

These Cohorts could be defined based on the above-defined metrics and a tier system could be introduced to target these customers better.
Example: Regular users of the platform are provided with gold tier services whereas one-time users are only given base level benefits on a platform.

Step 4: Performing Cohort Analysis

Finally, perform the Cohort Analysis using Data visualization and identify trends and patterns across different Cohorts (usually differentiated using different colors).

This method can help target different types of customers and help provide an optimized experience depending upon the preferences of that Cohort.

Step 5: Evaluating Test Results 

 Cohort Analysis - Step 5 Evaluating Test Results
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The test results are analyzed and strategies based on the insights obtained using this approach are implemented. 

The effectiveness of those strategies needs to be evaluated on a regular basis as the Cohort and demographics are dynamic and change over a period of time. Hence, the identification of more relevant metrics can prove to be useful in developing better strategies.

Cohort Analysis with Retention Table 

Retentional tables can be used to perform Cohort Analysis or Retentional Analysis helps visualize the Data on whether the user carried out a specific action. It can further be leveraged to represent the retention of the user over a period of time.

Cohort Analysis with retention table
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For example, the table above depicts a group of users who initiated the“App Launch” event and their retention over a period of seven days. Dates of Initiation / Initial launch such as “4/02” and “4/03” are grouped together as Cohorts and their Retention Rate across time is observed.

More information about Retention Table can be found here.  

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Understanding Types of Cohort Analysis 

Cohort Analysis can be divided into two types:

  1. Acquisition Cohorts
  2. Behavioural Cohorts

1. Acquisition Cohorts

Acquisition Cohorts Illustration  - Cohort Analysis
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This type of Cohort Analysis segments the user base on the basis of the acquisition date and time of the service or when the user signed up for a product. This tracking can be performed with different frequencies relevant to the product like daily, weekly, monthly, etc. 

Example: A Food delivery application can analyze an Acquisition Cohort on a daily basis, on the other hand, a B2B service app can perform the Acquisition Cohort on a monthly basis. 

2. Behavioral Cohorts

Customer Lifecycle Stages  - Cohort Analysis
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This type of Cohort Analysis segments the user base on the basis of the actions they undertake while using the application. Special event triggers can be tracked to understand the behavior of demographically different users. 

Example: Such triggers on a Food Delivery app could be the choice of restaurants and frequency with which customers order food, and for a social media platform, it could be the pages a user follows or the posts they like. 

Understanding the Importance of Cohort Analysis

Impact of Retention and Churn on ARR - Cohort Analysis
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The majority of organizations use Cohort Analysis to track and improve the Retention Rate and reduce the Churn Rate of the users of their services or products. But this technique has wider applications to improve the business.

Example: An online Fashion Store can use this analysis to track the style and size preferences of different demographics of users and build the inventory accordingly to maximize sales. Similarly, it can also help identify targeted recommendations and promotional offers that can be sent to improve customer trust and build loyalty. In a broader sense, it can identify the correct path for feature adoption and upgrades across various online platforms. 

Cohort Analysis is widely used in the following verticals:

  • E-Commerce 
  • Mobile Apps 
  • Cloud Software 
  • Digital Marketing
  • Online Gaming
  • Website Security

For the purpose of the analysis of the Retention Rate of the users, the metric used is called Customer Retention Rate which is calculated with the help of the formula given below.

CRR = ((E-N)/S) X 100

Here, these terms mean the following:

  • E: Number of customers at the end of the time period. 
  • N: Number of customers acquired during that period. 
  • S: Number of customers at the beginning (or start) of the period. 
Customer Retention Rate  - Cohort Analysis
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A higher CRR corresponds to robust customer loyalty. Comparison of CRR with Industry average can help provide a clear picture of the scenario and help draft better strategies to implement changes and improvements.

Key Metrics for Customer Retention 

Other than CRR there are other metrics that can be used to analyze the Data. As there usually is too much information in such operations, Cohort Analysis can be used to highlight the important patterns and metrics to reach conclusions and solutions. These metrics to help differentiate Cohorts are stated below: 

  1. Repeat Rate
  2. Orders Per Customer
  3. Time Between Orders
  4. Average Order Value (AOV)

1. Repeat Rate

Repeat Rate formula  - Cohort Analysis
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This metric helps track the users which interact/purchase/transact with the company’s business repeatedly compared to the customers that terminate the relationship with a single interaction with the services/products.

2. Orders Per Customer

number of orders per customer illustraction - Cohort Analysis
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As the name suggests, this metric helps track the number of orders placed per customer. An increased Orders per customer metric indicates a strong retention rate for that product/service.

3. Time Between Orders

Duration between orders illustration  - Cohort Analysis
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This metric measures the average duration between the orders placed by a customer. The optimum value of this metric is dependent on the industry, type of product/service and the business model opted for by the company. This metric can help send targeted mail regarding promotions to dormant customers and reactivation / resubscribing reminders to present customers.

4. Average Order Value (AOV)

Average value of an order illustration  - Cohort Analysis
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This metric helps in the identification of high-value spenders and loyal customers and helps in differentiating high-value Cohorts which can be further targeted with marketing campaigns and promotional upgrades to earn customer trust and build brand loyalty.


In this article, you learned in-depth regarding the Cohort Analysis, its importance, and the general method to perform it in any Data-centric use case. This form of analysis provides the analysts with a method to have a critical view of the Churn and prevents it from getting overlooked due to growth. 

Example: There could be a surge in the monthly active users due to a new promotion of launch, but rather than interpreting the raw numbers as growth this method can help you track the Retention Rate of the customers. 

For a comprehensive guide on related techniques that can be utilized for analyzing your data, click on the jump links for Marketing Funnel Analysis and Customer Churn Analysis.

Detailed methodology for Setting up Cohort Analysis in Excel can be found here, and for Setting up Cohort Analysis in Tableau can be found here.

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