What is cohort analysis and how to use it?
Posted: Thu Jan 02, 2025 10:54 am
The latest feature of Google Analytics, cohort analysis is a new asset of choice to integrate into your web marketing toolbox. Cohort analysis can allow you to detect new levers to boost your website traffic, in particular by analyzing in depth the evolution of user behavior on your site.
Summary
Cohorts: what are they?
Cohort analysis can seem confusing at first, and for good reason: it is rather unusual in the Google Analytics ecosystem. First, cohort analysis is dynamic, unlike most Google Analytics features. Cohort analysis is done continuously over time, unlike features like session counts or page views, which you typically analyze over a given period of time. Second, cohort analysis alone will not allow you to identify obvious growth levers: you will necessarily have to cross-reference these analyses with other KPIs to optimize your site's performance.
Defining Google Analytics Cohorts
Here is the definition that Google Analytics gives to explain what cohort analysis is:
A cohort is a group of users who share a common characteristic, identified in this report by an Analytics dimension. Thus, all users with the same acquisition date form a cohort. The Analytics report allows you to identify and analyze the behavior of these cohorts.
What is a cohort analysis?
If this definition still seems nebulous to you, don't worry, let's take the time to dive into this functionality in more detail by taking a concrete example:
Let's say you're the manager of a clothing store. You decide to uruguay whatsapp data launch a promotional campaign by offering 20% off coupons to certain customers. One of your customers, Martin, receives this coupon, and takes the opportunity to visit your site, browse your different items, and finally decides to buy a pair of jeans. As a manager, you want to know how many customers like Martin will return to your site after buying these jeans? How long does it take for these customers to come back to you? You ask one of your colleagues to analyze the behavior of Martin and all the customers who are similar to him. You have just created your first "cohort". A few months later, your colleague tells you that 70% of customers similar to Martin never returned to your site, 20% came back but didn't buy anything, and only 10% came back to buy a new product.
This is where cohort analysis begins. Continuing with our example, let’s imagine that of the 20% of customers similar to Martin who actually returned to your site, an overwhelming majority of them added a product to their cart, but stopped at the “shipping costs” page. It seems that all of Martin’s customers were put off by your shipping costs, so you might want to launch a “free shipping” campaign for these customers. Congratulations, you’ve just analyzed your first cohort, and activated a growth lever!
Summary
Cohorts: what are they?
Cohort analysis can seem confusing at first, and for good reason: it is rather unusual in the Google Analytics ecosystem. First, cohort analysis is dynamic, unlike most Google Analytics features. Cohort analysis is done continuously over time, unlike features like session counts or page views, which you typically analyze over a given period of time. Second, cohort analysis alone will not allow you to identify obvious growth levers: you will necessarily have to cross-reference these analyses with other KPIs to optimize your site's performance.
Defining Google Analytics Cohorts
Here is the definition that Google Analytics gives to explain what cohort analysis is:
A cohort is a group of users who share a common characteristic, identified in this report by an Analytics dimension. Thus, all users with the same acquisition date form a cohort. The Analytics report allows you to identify and analyze the behavior of these cohorts.
What is a cohort analysis?
If this definition still seems nebulous to you, don't worry, let's take the time to dive into this functionality in more detail by taking a concrete example:
Let's say you're the manager of a clothing store. You decide to uruguay whatsapp data launch a promotional campaign by offering 20% off coupons to certain customers. One of your customers, Martin, receives this coupon, and takes the opportunity to visit your site, browse your different items, and finally decides to buy a pair of jeans. As a manager, you want to know how many customers like Martin will return to your site after buying these jeans? How long does it take for these customers to come back to you? You ask one of your colleagues to analyze the behavior of Martin and all the customers who are similar to him. You have just created your first "cohort". A few months later, your colleague tells you that 70% of customers similar to Martin never returned to your site, 20% came back but didn't buy anything, and only 10% came back to buy a new product.
This is where cohort analysis begins. Continuing with our example, let’s imagine that of the 20% of customers similar to Martin who actually returned to your site, an overwhelming majority of them added a product to their cart, but stopped at the “shipping costs” page. It seems that all of Martin’s customers were put off by your shipping costs, so you might want to launch a “free shipping” campaign for these customers. Congratulations, you’ve just analyzed your first cohort, and activated a growth lever!