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cohort analysis

What is Cohort Analysis? How to Use to Improve User Retention (With Use-cases)

Cohort Analysis is a new stir in google. People are eager to know about this and they are literally talking about this. So why not us!

So let’s talk about it and spill the beans out of it.

What is cohort analysis?

In simpler terms ‘Cohort Analysis’ means group analysis. 


It is a technique that focuses on analyzing groups of individuals over time, getting deeper on the experiences of consumers and helping companies, what they can do with these experiences. Basically, it is grouping data into similar characteristics or identities. It can be according to size, time or other variable factors. 
Let’s take a look at some of those traditional numbers and ways of measuring them.

grouping users

Suppose You have a cosmetic store; MAC in Nykaa. A customer purchases an SPF containing BB cream, at a discount of 40%. You, as an owner of the MAC brand, ask your store manager to keep a track on people like her to retain them and see, what value they are contributing to the company. 

Now after three months, you come out with other attractive offers and asks your store manager to look through people like ‘smiley’ who purchased the product due to discount offers. By the way, the number of users who visited during the discount season i.e in that specific time frame is called a cohort.

Suppose, 65% of users who purchased BB cream never came back(Bummer!) while 15% visited the store once but did not complete sales and the remaining 20% bought something in this three months period.

How to fix this cohort analysis scenario :

You can make out conclusions from the above scenario:

  • The customers’ did not turn up because they were not interested in your products.

  • They forgot about the discounts on the vast amount of information.

  • You probably did not run retargeting advertisements right at the end of the discounted season, to remind them to buy more of your product if the users are interested.

  • Some customers did not buy the products due to extra shipping costs. So you can target such consumers with free shipping campaigns.

So with this cohort analysis, You, as an owner of brand MAC came up with two concern

  1. Retargeting Ad 
  2. Free shipping campaigns.

For improving the conversion rate for those groups and most importantly in all groups in the future in similar promotional events.

It is high time now to do them and analyze again if the above solution gives you fruitful results.

What is the difference between cohort and segment :

Cohort VS segment

Cohort VS segment

Cohorts have been used interchangeably but both are completely different


All users perform common events at the same time. Time is an important factor. The cohort is a subset of segments.


You can use almost every condition as a basis that is not event or time-based while segmenting a user.

A cohort can be divided into three broad categories :

1. Time-based Cohorts

As the name denotes, time-based cohorts denote identifying customer’s behavior who complete sales within a particular time frame depending upon the companies sales cycle.

TIme-based cohorts also help to determine the Churn Rate to the companies.

2. Segment based Cohorts

This cohort targets past customers. Customers are divided on the basis of services they signed up for. Say suppose Customer A signed up for the basic level of Graphology course And customer B signed up for Basic and Advance level of graphology. So both the customers might have different needs. Therefore, dividing them according to the needs or any other factors depending on the company.

3. Size based Cohorts

This Cohort takes into account the size of customers who are availing service or consuming companies’ products. They may be start-ups, small partnership firms, middle-size business or enterprise-level business. Comparing the different size consumers, the business company can analyze from where a large number of sales are coming from.

How can cohort analysis help your Business?

In a business application, we compare cohort-users sharing a common characteristics experience in a given time frame or even at times analyzing the single cohort to identify the pattern that supports a growth hub.

Cohort Analysis is often overlooked, but it can yield insightful information to improve acquisition claim, retention, and monetization. By analyzing the behavioral differences between the cohorts, product managers can spot patterns at multiple stages in the customer life cycle.

It helps the company to grow and improve its service in the market providing the right product at the right time. 

How to Improve user retention using Cohort Analysis

The most powerful function of Cohort Analysis is to see how customers leave and when they leave you can start analyzing the reason for their leave by digging the Cohort information and in the process trying to fix all the loopholes. 

To fight churn and helping the company to grow, the fixing process can be segmented into broader parts which are as follows-

  • Objective– Set a particular standard for the process. Whether you desire to reduce churn in the short run or long run. What is your goal?
  • Hypothesize– Deciding what questions to ask and what experiments to be conducted.
  • Testing– Run different tests to evaluate hypothesize.
  • Analysis– Analysing test data to evaluate testing whether it has met with a standard set.
  • Systemizing-Make any positive changes part of the system and use different marketing strategies to improve retention, covering up the loopholes.


If you are investing in acquisition there can be an instant surge in the Monthly Active Users(MAU). But let me tell you high MAU is not the indicator of growth.

Only proper use of Cohort will tell you how many of those acquisitions are actually sticking with you in the long run as a loyal customer. Cohort Analysis will actually tell you how much profit you are earning per customer.


Alisha is a content writer at NotifyVisitors, a marketing automation platform that helps small businesses to level up their marketing game.