Building Cohort Analysis Google Analytics: 6 Easy Steps


Setting Up Cohort Analysis in Google Analytics

Cohort Analysis is a type of Behavioral Analytics, in which the users are grouped and tracked on the basis of their shared traits to make sense of their actions. Cohort Analysis is instrumental when it comes to reducing churn, asking the right questions, and make informed product decisions to propel revenue growth.

Coming to the topic of Cohort Analysis Google Analytics, Google Analytics is a household name in Digital Marketing. This is because it offers everything you might want to know about the visitors on your website from website analysis to in-depth insight into user behavior. In this blog, you will understand how to go about setting up Cohort Analysis in Google Analytics along with its significance, advantages, and disadvantages along the way.

Table of Contents

Introduction to Cohort Analysis Google Analytics 

When you group your user data into separate groups based on certain criteria like age/gender/location/spends etc. and try to seek patterns and gain more insights across these groups, this procedure is called Cohort Analysis.

In the context of Business Analytics, a Cohort would refer to a subset of users that are specifically segmented by acquisition date. The Cohort Analysis happens to be an underrated feature offered by Google Analytics since it lets you look at the impact of different Digital Marketing activities individually on a specific group of recipients cutting through the noise.

Understanding the Importance of Cohort Analysis Google Analytics

Cohort Analysis Google Analytics lets you organize users into groups based on certain common criteria, like date/time period of performing an action,  demographics, or buying capacity. Using this information, you can study their behaviour like repeat sales, choice of products or services, response to short-term marketing efforts, retention, etc.  

As a lesser-known Business Analytics technique, Cohort Analysis comes in handy when you need to compare the variables and changes across your Digital Marketing campaigns. Here are a few factors that influence the user behaviour that you may want to capture using Cohort Analysis in Google Analytics:

  • Target Audience.
  • Ad Content.
  • Website Redesigns.
  • Sales, Discounts, and Promotion Campaigns.
  • Channels.
  • Campaigns/Experiments.
  • New Product Lines and Service Offerings.

Cohort Analysis proves to be valuable as it filters the Growth metrics from the Engagement metrics. This is helpful since Growth can often mask Engagement problems.

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Steps to Set up Cohort Analysis in Google Analytics

Cohort Analysis in Google Analytics Hub
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Step 1: Go to your “Google Analytics Hub”, from the default templates select “Cohort analysis”. 

Step 2: In the screen that follows, Name your Cohort Analysis and specify a Date Range.

Naming Cohort Analysis
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Step 3: What metric you’d like to study, for each of these groups? This is specified in the “Values” section,  e.g. the “Purchase Revenue” metric will let you know the amount you spent on your website. You can further refine the metric to include, let’s say, only high spending users.

Metric to Use for Cohort Analysis
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“Metric Type” determines if you want a “SUM” of all those values or want to show “per user” values. I.e. total of all purchases by a user in a specified time period or every single purchase in the time period of interest.

Step 4: Then, select Segments to include in the report, in our example one can choose between Direct traffic, Mobile traffic or Paid Traffic, etc.

Segments to include in Cohort Analysis Report
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You can choose to use “Segment Comparisons”. In the Cohort “Inclusion Criteria”, the characteristic that will be used to group the data into Cohorts, can be chosen. Next, you can specify the “Return Criterion” condition, a Cohort will leave the group as soon as this condition is violated. The available options can be “First Touch (acquisition date)”, “Transaction” or “Specific Event”, etc.

P.S. Something to be careful about, a user will be assigned to all Cohorts for which he/she meets the inclusion criteria, for as long as he/she does not meet the return criteria. Also, you can choose “Cohort Granularity” to be Daily/Weekly/Monthly, this will decide the duration for which the Cohorts remain in their respective groups. 

Step 5: Whether you want a “Standard” calculation or make it “Cumulative”?

Breakdown Dimension for Cohort Analysis
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You can select the “Breakdown Dimension”, amongst values like User Source/Medium/Campaign/Gender, etc. This breakdown criterion will break down your Cohorts group further into sub-groups and show how Cohorts differ along with these criteria. 

Step 6: Below is a sample Cohort Analysis generated from customer data from an e-commerce website.

Cohort Analysis generated from Customer Data
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The findings above can be summarized as:

  • Between October 6th and October 12th, this website had 270 users with at least one transaction. 
  • Of these 270 users, 14 had at least one transaction in the following week (Oct 13th to Oct 19th). 
  • For the 4 week period ending Nov 2, three users bought something or the other every week. 
  • Since repeat transactions drop drastically by week4, this is the right time to send promotional offers/emails to engage the users again. 

Here are a few takeaways from this little exercise:

  • Start running experiments to test your audiences, channels, and website designs on a weekly or monthly basis. Take time out regularly to check on the results of these experiments.
  • If you come across a significant increase or reduction in certain metrics like User Retention for a given Cohort, take a look at your Marketing calendar to see what changes could be driving these changes in your metrics. You can then turn these Cohorts of interest into custom segments to assess their behaviour further.

Understanding the Advantages & Disadvantages of Cohort Analysis

Advantages of Cohort Analysis Google Analytics

  • Cohort data is generally free of seasonal swings and sentiment-based fluctuations in their activity. 
  • It helps you target your products/services to a particular Cohort group, e.g. youngsters between the age of 16-23 years. 
  • A major advantage of using Cohort Analysis in Google Analytics is that it can be used to identify trends and find the exact point when people are churning.
  • It gives you the exact time you should be re-engaging with your users and the exact rate of acquisition for new users to maintain your Website Conversion Rate.
  • It also allows you to make comprehensive strategies for Retention after getting an understanding of what works and what doesn’t.

Disadvantages of Cohort Analysis Google Analytics

  • It can take a long time to collect Cohort data, substantial time is elapsed till the collective activity of the Cohort group gains sufficient numbers/traction to make it usable for a story. 
  • The above fact requires a lot of funds to collect the data over a longish period of time. 
  • The main disadvantage of leveraging Cohort Analysis Google Analytics is that for now Cohorts can only be grouped under Acquisition Date as a metric in Google Analytics.
  • Tracking Retention and keeping track of the returning users on your website can lead to some inconsistencies in Google Analytics.
  • The possible association between a Marketing Campaign and an increment in the metrics is a correlation, not causation.
  • Using Cohort Analysis in Google Analytics does not give you the information on why a particular customer decides to leave your camp. You would have to deploy another form of Cohort Analysis for that: Behavioral Cohort Analysis.


In this blog, you have read about what Cohort Analysis Google Analytics is all about, along with an understanding of the significance, advantages, and disadvantages of utilizing Cohort Analysis Google Analytics.

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Pratik Dwivedi
Technical Content Writer, Hevo Data

Pratik Dwivedi is a seasoned author specializing in data industry topics, including data analytics, machine learning, AI, big data, and business intelligence. With over 18 years of experience in system analysis, design, and implementation, including 8 years in a Techno-Managerial role, Pratik has successfully managed international clients and led small to medium-sized teams and projects. He excels in creating engaging content that informs and inspires.

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