Cohort analysis is a research method that has been around since the 40s but has become increasingly popular since the advent of the internet.
Now its popularity is evergreen, being a valuable technique for growth hackers and marketers alike.
What is Cohort Analysis?
A cohort, in general, is a group of people that are observed over a period of time. In cohort analysis, we focus on cohorts of users rather than the whole user base, and how they interact with the product over time. The goal is to understand how habits form, how long it takes for the user base to reach maturity and stabilize, and what percentage of your users will remain active after a certain amount of time.
To get started, you need to decide which data to follow over time. It’s best to start by picking a very specific metric that’s reflective of your company’s goals. For example, if your goal is to have users buy your products frequently, you might want to track purchase frequency or the total number of orders. If you’re looking for users who will stick around long-term and invite other friends, you may want to track daily active users or weekly active users instead.
You can then begin by grouping all of your users into cohorts based on when they signed up or made their first purchase/action. Once you have these groups established, you can follow them over time as they interact with your product and see how they change over months or years.
Cohort analysis lets you:
See how customers change over time
Compare customer behaviors between different groups of people
Improve customer retention
Learn which marketing channels are best at acquiring new users
The most important thing about cohorts is that they share data points. This makes them much easier to analyze than random groups of people.
Cohort analysis is a way of looking at your website traffic or user base by grouping them into cohorts. The cohort, in this case, is the traffic or users who arrive at a certain time or during a certain period.
It can look at a variety of factors, including:
Which page do they arrive on
Where they come from
What device do they use
What keywords do they search for to get there
Where they are located
If you’re a marketer, you can use behavioral cohorts to identify which marketing campaigns are efficient. For instance, you can analyze the behavior of users who have signed up through Facebook ads and compare them to those who have signed up through Google Ads. The behavior cohort analysis will show if there is any difference in user engagement or retention between the two groups.
This kind of analysis is useful because it allows you to track and compare performance over time. Most businesses have a very limited understanding of customer behavior. In fact, their knowledge usually extends only to what they are able to observe and measure. These are important things, of course, but they only provide the most superficial understanding of customer behavior. This kind of approach to customer analysis can be likened to looking at the surface of the ocean and using that information to determine the depth of the water.
By breaking out your data into cohorts based on specific characteristics (such as signup date or paid user status), you can create meaningful segments that allow you to see trends or changes over time. This not only allows you to identify problems but also gives you insight into what works best for different groups of users so that you can focus your efforts on improving the experience for everyone.
Cohort analysis can be an incredibly useful tool for any business that wants to understand its customers or employees better.
Why Should You Use It?
The two major benefits of cohort analysis are that it helps you understand your customers and make better decisions.
It helps you better understand your customers by showing you how they behave over time. When you group your users into cohorts, such as by month or quarter of sign up, you can see which groups have the highest retention rates, longest engagement times, and other important metrics.
By using this type of analysis to see which groups have the best results and then looking at what these groups have in common, you can see what traits are most likely to lead to success with your product.
With a better understanding of your users, you can make more informed decisions about how to reach them and what features to implement. By looking at the successes of your top cohorts and determining what made them so successful, for example, you can reach out to similar audiences and create features that appeal more to those groups.
Why is It Important for Ecommerce?
Cohort analysis gives you the data necessary in order to understand what makes people buy from you so that you can do more of it. You’ll be able to figure out which marketing campaigns are successful and which ones aren’t. You’ll be able to see whether or not it’s worth it for you to invest in social media advertising, and if so, which platforms will give you the best return on investment. It even lets you know if your product is being purchased by men vs women.
The more information you have about who buys your products and how they find them, the more strategically and effectively you can market your business. And cohort analysis allows you to collect this information easily and efficiently.
Cohorts can be organized by date or time period, but they can also be organized by groups of users who share similar traits such as geographic location, demographic data, purchase history, or other data. Cohort analysis allows you to identify the most engaging group of users on your site and the least engaged group. The goal is to analyze which of those cohorts is more likely to make a purchase after visiting your online store.
It can also be used for:
Monitoring the behavior of various customer segments.
Understanding how the customer life cycle works for each cohort.
For effective follow-up with customers.
Getting insights on customer loyalty.
For planning long-term marketing strategies and improving revenue.
It is important for eCommerce because it can help them understand the customer lifetime value (LTV) of their customers. For example, they can analyze if a weekday is affecting their customers’ LTV and whether or not they should increase marketing efforts on a certain day over another. For instance, Black Friday may bring in more low LTV customers which will impact their business negatively. It may also help them discover what the buying cycle of their customers is so that they can better market to them.
You can learn these things by looking at cohort analysis reports for your eCommerce business. These reports will show you how many people visited your site in one month and then again in another month. You’ll also be able to see what kinds of things they did while they were there, such as browsing categories of items or viewing product details pages. If someone purchased something during both visits, that would count as a repeat customer.
What are the Types of Cohorts?
Before we get into the different types of cohorts, let’s take a quick look at what metrics you should be paying attention to when you’re analyzing your data:
- Customer Lifetime Value (CLV/LTV): this shows you how much profit you’re making from each customer over the course of their time with your company
- Average Order Value (AOV): this shows you how much money a customer spends each time they make an order
- Order frequency (OF): this shows you, on average, how often customers are placing orders
AOV & OF together show the amount of money customers spend on your site over time (CLV)
There are different types of cohorts that can help you understand your users’ behavior. They are:
1. Acquisition Cohort (Acquisition Date)
Use the Acquisition Cohort (Acquisition Date) to see how users interact with your product in the days, weeks, and months after they sign up for it. This can help you understand how your product’s onboarding experience affects user retention and engagement over time.
2. Retention Cohort (Activation Date)
A retention cohort is a group of users that purchased the same product at the same time and tried to use it to solve the same problem. If you want to see which of your products are most successful, look at their retention cohort.
PS: we’ve written this article about cohort analysis for retention that might interest you.
3. Revenue Cohort (Revenue Generated Date)
Revenue cohorts group your customers based on the date they generated their first purchase. This helps you see how your customers behave over time, whether they’re becoming more valuable or less valuable, and where they started the relationship with your business.
4. Behavior Cohort (Behavioral Data)
Behavior cohorts are groups of people that have a shared characteristic, like purchasing a product or visiting a website a certain number of times. They’re different from demographic cohorts because they don’t rely on demographic data like age, income, or gender—instead, they look for patterns in the way people behave.
In any business, it’s critical to understand your customers, but in eCommerce, it’s especially important because you have a much smaller window to communicate with them than if they were coming into a store.
Cohort analysis helps you understand who your customers are, how they behave, and how your store is performing over time. This type of data allows you to re-strategize your marketing campaigns, product lines, and pricing strategies to attract more buyers.
This analysis divides shoppers into segments based on certain parameters so that you can optimize marketing efforts for them—and ultimately increase sales.
The Best Tools for Cohort Analysis
There is a wide range of tools available for cohort analysis that vary in price and functionality. Here are some of the top:
Amplitude features automatic cohort analysis via its Funnel Analysis product. This allows you to track almost every action a user takes on your website or app – including purchases, clicks, and downloads – and see how often they return over time. This means that you don’t have to do any manual work in order to access this information – all of it is available through Amplitude’s highly visual dashboards.
Mixpanel provides a tool that allows you to identify and track your users based on the actions they take within your product. It tracks each action, or event, in real-time so that you can instantly see how many times a user has taken an action and what they have done before or after taking it.
Heap is great for users who are looking for an easy way to derive insights from their analytics data without having to build any custom reports or analytics views. It automatically captures data from your site or app and lets you analyze the data using the “funnel” tool. You can use this tool to see how your users behave between two points on your site or app.
Should You Use Google Analytics’ Tool?
What does it do? It lets you, group users, by when they first engaged with your site and then compare them over time to see how their behaviors change. For instance, if you have a lot of new users who leave after the first page, that might give you insight into what you need to improve on your homepage. If they leave after two pages, you might want to look into how relevant your content is.
Google Analytics’ cohort analysis is one of the most commonly used features for this purpose. It provides insight into the retention and engagement rates of your users over time, which can help you identify trends that may be impacting your overall performance and determine where improvements need to be made.
If you need to evaluate how your campaign is performing or want to analyze specific cohorts, analytics tools can provide insight into areas like acquisition and retention. If you’re looking to really dig into user behavior and see how people are interacting with your platform, cohort analysis tools are a great place to start. You could imagine using them for A/B testing different UI elements or product changes to get a better understanding of why certain variations may indeed be better.
Cohort analysis is one of the most powerful, but under-utilized, tools in a marketer’s arsenal. It’s consistently under-rated, under-used, and misunderstood by many marketers. In some ways it’s understandable; it does have its caveats. But the data speaks for itself: the benefits of the tool far outweigh any drawbacks, and using this kind of analysis correctly can provide marketers with powerful insights that help them make more informed decisions about their marketing budget and plan.