April 19th, 2025
What Is Cohort Analysis? Definition, Examples, & Benefits
By Connor Martin · 6 min read
Has your business ever needed a marketing superpower? A means of figuring out the secrets behind customer behavior that would then allow your company to adjust its marketing to maximize profits?
That’s what a cohort analysis allows you to do. When you perform cohort analysis, you identify patterns and key behaviors that can inform many of the business decisions you make.
In this blog, we’ll explain what a cohort analysis is and how to use one to enhance your marketing efforts. Plus, as your data analyst experts, we’ll provide the best practices that ensure the cohort analysis charts you produce deliver the data your business needs.
Key Takeaways
• Cohort analysis segments users based on shared characteristics or behaviors, helping businesses understand customer retention, optimize marketing campaigns, and enhance product development.
• The two main types of cohort analysis—acquisition cohorts and behavioral cohorts—help businesses track how customers join and interact with their products, revealing key insights for improving customer experience and engagement.
• By visualizing cohort data and identifying trends, businesses can take data-driven actions to reduce churn, increase customer lifetime value (CLV), and maximize profitability.
What Is Cohort Analysis?
A cohort analysis is a method through which you group users or customers based on shared characteristics or behavior. For instance, a marketer may generate a cohort analysis report to analyze the buying patterns among customers of a specific age group or gender.
These reports matter because they allow a marketer – or any statistician – to get to the root of user behavior. A company may have different cohorts, each focused on a specific trait, that helps it to uncover things like purchasing habits, how much the average user in that cohort spends, and what products attract them the most.
They can then use that cohort data to boost their marketing efforts, such as creating campaigns designed to appeal to a specific user group, with each tweaked for maximum effectiveness based on how different cohorts behave.
Types of Cohort Analysis
There are several types of cohort analysis, with each having a different purpose depending on the shared characteristics of the users placed into the cohort. The following two are the most common.
Acquisition Cohorts
Acquisition cohort analysis involves dividing our users into groups based on where, when, and how they signed up for or purchased your product. For instance, you may have different cohorts within this analysis that cover customers you obtained via your website, social media, telephone calls, and canvassing.
With your acquisition cohorts defined, you can start looking for patterns related to customer retention and churn rates. For instance, you might find that customers you acquire via canvassing have a longer customer lifecycle than those obtained via social media. The data you collect prompts you to ask why. Perhaps those customers earned via canvassing show that your users appreciate a more personal touch, leading to them sticking with your company longer.
What’s most important is that you may not have even noticed this variance in churn rate across acquisition mediums were it not for your cohort analysis.
Behavioral Cohorts
Rather than dividing based on acquisition, behavioral cohorts are divided based on customer behavior data. This type of cohort analysis is all about action, specifically why your users do what they do, which again allows you to identify trends.
For instance, you may create cohorts based on different demographics, such as age, in a behavior cohort. The cohort table you produce might then show you that customers from the “x” age category group use specific product features more than those from your “y” or “z” group. Why does that matter? Thanks to that data, you now know that you can increase your marketing on the identified product feature toward the “x” group. As for the “y” and “z” groups, they see less value in that particular feature.
So, marketing campaigns designed for those groups would focus on other features or benefits of your product based on what their group data demonstrates.
Why Cohort Analysis Matters
Why use cohort analysis at all? In addition to marketing optimization, as we touched on earlier, there are several reasons to use this method of data management and analysis, including:
• Understanding Customer Retention and Churn
• Optimizing Marketing Campaigns
• Enhancing Product Development
• Improving Customer Lifetime Value
Understanding Customer Retention and Churn
An effective cohort analysis can reveal why your users (or customers) are leaving by enabling you to track retention rates over time for different cohorts. We see this from the acquisition cohort and behavioral cohort examples. On the acquisition side, grouping users based on where you got them may show you that churn is higher in a certain acquisition stream. An accompanying behavioral cohort might demonstrate why.
For example, you may discover that users who came to your business from social media tend to leave quicker than others in an acquisition cohort. A behavioral cohort may help you to see that those users are of a particular age group or prefer specific features in your product that most aren’t finding.
Optimizing Marketing Campaigns
Once you know which groups prefer which acquisition channels, you know who and how to target users based on those channels. Sticking with the social media example, a cohort retention analysis may reveal that the users you acquire from social media stick around when they realize they have access to a specific product feature. Now, you know what features to market to those social media users in the future.
It's all about using data to tweak and optimize the campaigns you already have. Your marketing becomes more targeted – each campaign can focus on a specific cohort and its patterns – so you ultimately generate more revenue and extend your average customer lifecycle.
Enhancing Product Development
A customer cohort analysis can show you what’s “wrong” with your product as much as it can demonstrate the features that your users love. As you analyze cohorts, you may see users dropping off at a specific part of their journey with your product. Is that because the product is missing a feature they can find elsewhere? Even if that isn’t the case, your cohort analysis at least prompts you to ask the question, which leads to further research that can fuel the next iteration of your product.
Again, it’s about understanding your users. That understanding allows you to emphasize the cohort-group-specific features your product has and to develop that product to ensure higher retention rates later.
Improving Customer Lifetime Value
The previous three benefits of a cohort analysis combine to lead to improved customer lifetime value (CLV). This metric offers an estimate of how much profit your company generates from the average customer or user. Your cohort analysis helps you to improve your marketing, develop better products, and understand the reasons behind customer churn.
Combine the actions you can take from those three datasets and you end up with a product that keeps customers happier across multiple cohorts. Happy customers stay with your business for longer, leading to a higher CLV.
How to Perform a Cohort Analysis: Step-by-Step Guide
We’ve explained what a cohort analysis is. However, you also need to know how to perform one, which is where these steps come into play:
Step 1: Define Your Goal
A cohort chart isn’t going to tell you much if you haven’t first defined what you need to learn from that chart. Think of it like conducting a medical study – that study is worthless without a hypothesis that guides the study’s methodology.
The same is true of a cohort analysis. Define your goals – such as reducing customer churn or increasing CLV – before you start the analysis.
Step 2: Select the Right Cohort Type
With your goal defined, you need to choose the right cohort type. This step is all about how you’re going to group your users, along with choosing between the acquisition and behavioral cohorts we mentioned earlier.
If you need to know where your customers come from, an acquisition cohort is the right choice. The data you gather then allows you to determine patterns that aid in marketing. Behavioral cohorts are best used when you have a goal involving your existing customers, such as reducing churn rate.
Step 3: Gather and Segment Data
Cohort analysis is all about pattern recognition, which clues you into how you must gather and segment your data. A tiny cohort – especially one that is diverse – provides no useful patterns. Ideally, your cohort groups will be as large as possible and segmented so that each person within each group has a specific attribute.
Segmenting by age is the most obvious example. You could have multiple cohorts for different age ranges, such as 18-25, 26-35, 36-50, and so on. Each user’s age is the specific attribute that determines which cohort they enter, with the patterns gathered clearly representing acquisition and behavioral data related to defined age groups.
Step 4: Visualize Cohort Data
Your initial cohort analysis will give you a lot of numbers, such as hundreds of customer lifespans in days or months. Next up – visualizing that data.
Line charts are excellent for trend recognition, as you can see peaks and troughs across multiple cohort groups. You can also use bar charts and pie charts if you want to compare cohorts, as you might with the age groups example we’ve mentioned. Regardless of your visualization choice, the goal is always to locate and interpret the patterns your cohort analysis reveals.
Any sudden spikes or troughs indicate something important for your business. For instance, a spike in user retention may show that there’s a product feature that appeals to a specific segment of your audience. A trough would often show the opposite – people are turned off by that feature, don’t connect with your marketing, or otherwise aren’t engaged with whatever you’re trying to implement.
Step 5: Interpret Insights and Take Action
The final step of cohort analysis is the simplest of all, at least in concept: Take action.
What that action is depends on the question your study is designed to answer and the data you’ve gathered. Interpreting the data isn’t enough. Once you have that data, take an action – such as creating a new marketing campaign – that’s relevant to the data.
Real-World Examples of Cohort Analysis in Action
Though we’ve spoken about cohort analysis in marketing and business terms so far, there are many other real-world examples of their use.
For instance, one of the most famous examples of cohort analysis comes from the 1976 Nurse’s Health Study. That study saw researchers create a cohort of women to investigate the long-term ramifications of oral contraceptive use. A second-generation cohort was created in 1989, with the third generation being recruited in 2010. The study yielded vital information both about the contraceptives women used at the time and their lifestyle habits, which informed later medical policies.
The Framingham Heart Study is another medical example. This study saw researchers recruit 5,209 male and female participants in 1948. A second-generation cohort began in 1971, with the third following in 2002. Having three generations of cohorts enabled researchers to study cardiovascular risk factors related to genetics and ongoing behavior.
Best Practices for Successful Cohort Analysis
Beyond the steps discussed earlier, keep the following best practices in mind when developing your cohort analysis:
• Always gather the required data – from user-level data to transaction data or any other form of data needed to answer your starting question – before conducting your analysis.
• Cohort identifiers, which are the conditions that lead to a person being placed in a cohort, are essential and must be clear to prevent issues with your results.
• Leverage automation to fetch and parse data, especially if your cohort analysis is set to run for a long time.
• Create a cohort table as your first type of visualization before moving on to line and bar charts.
Let AI Conduct Your Cohort Analysis in Seconds. Try Julius for Free Today
As with so many other forms of data analysis, it can be hard to properly visualize the patterns your cohort analysis demonstrates. That’s where the right AI tools can help. With Julius, you gain the ability to “chat” with the data you collect. You can create a cohort analysis, feed the data into Julius, and then ask the AI to produce charts, insights, and even action steps based on the data you enter.
Julius is your AI-powered data analyst. Find out why Julius is trusted by over two million users – Try it for free today.
Frequently Asked Questions (FAQs)
What is a cohort study in simple terms?
A cohort study is a research method that tracks a specific group of people (a cohort) over time to analyze patterns, behaviors, or outcomes. Businesses and researchers use cohort studies to uncover trends, such as how customer habits evolve or how lifestyle choices impact health.
What is an example of a cohort analysis?
A common example of cohort analysis is tracking customer retention based on acquisition sources. For instance, a company might compare how long customers who signed up via social media stay engaged versus those acquired through email marketing, helping refine future campaigns.
What are the two types of cohort analysis?
The two main types of cohort analysis are acquisition cohorts and behavioral cohorts. Acquisition cohorts group users based on when or where they joined, while behavioral cohorts track users based on specific actions they take, such as product usage or purchase frequency.
What are the benefits of cohort analysis?
Cohort analysis helps businesses improve customer retention, optimize marketing strategies, and enhance product development by identifying key user trends. It also provides insights into customer lifetime value (CLV), allowing companies to make data-driven decisions that maximize profitability.
