Cohorts in marketing: a complete guide

Data & AnalyticsBy Juliana Amorim and Mariana Bonanomi

In the realm of data analysis, one term that frequently emerges is "cohorts." While it may sound complex and intimidating at first, cohorts are an invaluable method for understanding user behavior, tracking trends, and making informed decisions. In this blog post, you will find out what cohorts are and how they can empower businesses to gain deep insights into their target audience.

What are cohorts?

The term can scare anyone unfamiliar with it. But don't worry, it is not rocket science, and we have great news: if you are a marketer, it can lead you towards deep and insightful analysis on understanding the user lifecycle, finding trends among users, increasing customer retention and engagement, and reducing churn.

So, what does it mean? Wikipedia's definition sums it up quite nicely:

In statistics, marketing and demography, a cohort is a group of subjects who share a defining characteristic (typically subjects who experienced a common event in a selected period, such as birth or graduation).

Specialists use them to compare groups of people to understand how marketing strategies results vary over time and what data each period displays.

You can, for instance, analyze adults who have practiced sports from 30 to 40 years of age and compare them with adults who have not practiced sports between the same ages. Moreover, it is possible to draw a parallel with those who have practiced sports before the age of 30.

Once you assemble the comparisons, you can analyze how health metrics vary between these three groups and understand the effect of sports at different ages.

But we're not here to talk about people fond of jogging or playing basketball on weekends. As marketers, let's talk about:

  • The difference between cohorts and segments
  • The difference between performing a cohort analysis and churn analysis
  • How we can use cohort analysis to optimize marketing campaigns
  • What cohorts aspects you should consider for the analysis.

Cohorts vs. segments

Analyzing customer segments is an excellent strategy for understanding your audiences, finding out about user trends, and even measuring customer loyalty. Although the simple idea of clustering users may prompt you to think segments and cohorts are the same, there are significant distinctions between them that you should know of.

Segmenting lets you quickly view users' specific shared characteristics or single actions, such as clicks on a CTA button. On the other hand, cohorts are subsets of segmentation: they're bounded on given shared activity performed during a specific time range and its consequences. In some cases, finding out about the unified experiences of a grouping of users is table stakes for a critical application of segments in your marketing and product analytics.

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Cohort analysis vs. churn analysis

Another common misunderstanding this analysis and its principal value for marketing strategies is related to using it interchangeably with the term "churn analysis". Although both can help you avoid churn in the future by considering actions or events from the past, they are different regarding juncture, scope, and used sources.

While churned customers may take part in a cohort before they left, a churn analysis only happens after the user stopped using your product, left your website, or canceled the subscription. Meanwhile, the cohort analysis is mostly about customers who are still on board with you.

Thus, churn analysis helps you determine what constraints or shortcomings in your offering correlate to customers leaving. Instead, cohort analysis is mostly about awareness of what is already effective and what you can tweak even more to take users to aha-moments. That's why the sources for performing churn analysis are usually customer support tickets, feedback forms, or negative testimonials. On the other hand, the source for using cohorts is usually metrics such as average order value, time spent on your website, or registration time.

Now that you are familiar with what a cohort analysis is and what it's not, let's discuss how you can use it in your strategies.

How can marketers use cohorts?

There are hundreds of possibilities for marketers to use cohorts. The question is not how to create them but how they can produce insights. When thinking about which cohorts to use, you should ask yourself:

  • Why can these cohorts deliver the insights to impact my marketing strategy?
  • Can I see what is working and what is not in my current marketing strategy?
  • What goals do I need to set to succeed this year? Will these cohorts help me achieve these goals?

Let's talk about three types of cohorts usually useful for most marketers.

1. Registration time or first purchase

These cohorts analyze people's behavior based on their registration date or first purchase. It can show you, for example, how long it took them to reach a specific goal after their first visit, what path they took, and how long it took them to come back. Then, you can correlate these findings with the marketing strategy you choose by the time you acquired them to understand how it impacts these metrics.

As marketers, we always expect campaigns to be efficient and create a sense of urgency for users: they must enter the site and register (or make a purchase) right afterward. However, it doesn't always work that way. Sometimes, paid campaigns resonate more like inbound marketing and only generate interest for further action.

Using a cohort analysis to evaluate how long the user took to complete an action helps you understand which impact the copy and the overall communication strategy have on business growth.

Although we usually take a closer look at the results of a campaign in the short term (such as conversion rates), it is crucial to learn how they perform in the long term.

Example of campaign's cohort table with 6 rows and 6 columns, each row representing a campaign and each column representing a cohort
Example of campaign's cohort-based on time to first purchase

2. Frequency of visits

For a business with a SaaS model, the frequency with which the user accesses the website usually determines the probability of success in using the product.

Remember: customers who don't use and don't see value on the platform tend to cancel their subscription, quickly increasing the business churn.

Suppose you manage the commercial area of a SaaS. In that case, you must understand the main characteristics of a heavy user: the engaged customer who uses the product assiduously. You must identify contextual elements and behaviors of heavy users to search for leads with this same profile.

Finally, you can set up an analysis to evaluate which acquisition channels achieve more frequent users.

Take a relative metric, such as the percentage of users who churned among those who converted. Due to that, you will understand which channel has more chance to acquire customers that won't add value to the business in the long run.

3. Repurchase dates

Every business needs loyal customers. It doesn't matter if it is an e-commerce, a SaaS, a consultancy, or a retail store. Customers who come back and make purchases regularly are essential to business growth.

Using cohorts may be an excellent way of strengthening customer loyalty. You can, for example, analyze customers with more than one purchase and segment them according to the purchased product category. Doesn't it answer your question yet? Then cluster them according to which channel hit them the first time: inbound marketing channels or a specific paid marketing campaign. There are countless possibilities.

Here are some things you can learn from this analysis:

  • Is there a category of products that drives more repurchases? Find the most related product to this behavior and promote it more often in your customer acquisition campaigns.
  • Does any marketing channel attract more loyal customers? It is common to see that, in general, users coming from paid campaigns or search campaigns are more likely to make repeated purchases than customers coming from social media campaigns. If this is the case, it may be interesting to focus your investments on paid searches.
  • You can probably split your marketing campaigns between seasonal and regular campaigns throughout the year. Which of them brings more loyal customers? In which ones should you invest more?

What aspects to consider when using cohorts

As you can see, we can draw countless insights from the grouping of people and their behavior over time.

Each business is unique, as are its marketing challenges. The three listed examples are just ideas for how cohorts can optimize the marketing strategy. Still, it's important to know that you will only get the full value from them by looking at specific aspects to extract insightful data.

Cohort type

It is the base of the report. Some platforms group cohorts based on the date of the first session.

Cohort size

Here, you define the range for the grouping. For example, if you're doing a short-term analysis to assess the performance of a recent campaign, you might want to look at the data by day. On the other hand, if the objective is to analyze the long-term marketing strategy, choose the grouping by month.


Which metric do you want to analyze? It can be the most basic data such as session, revenue, and pageview, or more sophisticated ones, as calculated metrics.

For example, if you choose to look at user retention by day, you'll be able to know the share of users who visited a session on specific dates.

Data range

Here, we define the analysis period, which varies according to your choice. For example, in many tools, if you choose "day" as the cohort's size, you can select the period of 7, 14, 21, or 30 days.

Some platforms allow you to create the report after defining the listed aspects to evaluate how each cohort's performance varies over time.

Depending on the metric you choose to analyze or the rules you use to create the groupings, some paid softwares may meet your needs. Another possibility is to use spreadsheets to assemble yours.

Cohort analysis in spreadsheets

Using a spreadsheet, you can set up a table where the rows are the cohort groups and the columns the time grouping range.

As you have all the available data, you can create groups from several records: date of first purchase, last purchase, registration, first visit, the first purchase of a particular product, date of purchase, launching campaigns, etc.

Ideally, the grouping of the columns aligns with the cohort groups. For instance, if you have monthly cohorts, you should ideally group columns by month.

Example of campaign's cohort table with 7 rows and 6 columns, each row representing a month and each column representing a cohort
Example of campaign's cohort based on month of first session

To assemble the analysis in a spreadsheet, use a pivot table. If you don't know how to do it, this tutorial can help you create your first one.

To recap

Here is a summary of what we covered:

  • Cohorts are groups of people who share the same event or characteristic in a given period
  • They can help marketers analyze different groups of customers and leads. These can be compared, revealing which characteristics correlate to the best results
  • Sometimes, the most expected behaviors may not be the ones that bring about good results. Cohort analysis is helpful at this point to understand what creates value for the business
  • There are several tools for analyzing cohorts, such as data analysis platforms or even spreadsheets
  • You can use cohort analysis in many fields besides marketing and sales.


Using cohort analysis is an effective strategy for understanding user behavior over time and identifying opportunities for improvement. We hope this post has been useful for you to understand how it can help boost your business results. If you want to start using cohorts to personalize your users' experience, create your free account and explore our platform by yourself.

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