Cohorts in Umami Analytics: How to Group Users and Understand Behavior Over Time

Updated: September 2026

Understanding what your visitors do is one of the most important parts of analytics. But looking at all of your users as one large group can hide meaningful patterns.

Cohorts in Umami Analytics let you create groups of users based on specific actions they took during a selected period. You can then apply those cohorts to your analytics reports and compare how different groups behave over time.

For example, you could create a cohort of users who:

Visited a particular page or URL
Triggered a specific event
Performed an important action during a particular date range

This makes cohorts particularly useful for analyzing campaigns, product launches, content, features, and other groups of users whose behavior you want to track separately.

What is a cohort?

A cohort is a group of users who share a particular characteristic or behavior.

In Umami, that characteristic can be based on when the users interacted with your website and what they did.

For example, imagine you published a new product page between January 1 and January 31. You could create a cohort containing users who visited that page during January.

Once the cohort has been created, you can use it as a filter in your analytics reports to see how those users behave compared with other visitors.

This is different from simply looking at a page's overall traffic. Instead of asking:

"How many people visited this page?"

you can start asking questions such as:

"What did the people who visited this page do afterward?"

That shift from aggregate traffic to behavioral groups is where cohorts become especially useful.

Why are cohorts useful?

1. Understand behavior after an important action

A visitor completing an important action doesn't necessarily tell you what happens next.

Suppose you have an event called signup. You can create a cohort based on users who triggered that event and then analyze their subsequent behavior.

This can help answer questions such as:

What pages do these users visit?
How engaged are they?
Do they return to the site?
How does their behavior compare with other visitors?

Cohorts give you a way to keep the users who performed an action together as a group.

2. Measure the impact of content or campaigns

Cohorts can be useful when you want to understand the behavior of people exposed to a particular page or campaign.

For example, you might create a cohort of visitors who reached a landing page during a campaign period. You can then apply that cohort to your reports and examine the behavior of that audience.

This provides more context than looking only at the landing page's pageviews.

3. Compare different groups of users

Analytics becomes much more useful when you can compare groups.

You could create separate cohorts for users who performed different actions or interacted with your website during different periods. Applying the appropriate cohort to your reports lets you investigate whether those groups behave differently.

This can help reveal patterns that aren't obvious when looking at your entire audience.

4. Create static groups for historical analysis

One particularly useful feature of Umami cohorts is the ability to create a static cohort.

When creating a cohort, you can select a Custom Range for the date range. This keeps the users in that cohort fixed rather than allowing the group to change as new data comes in.

Static cohorts are useful when you want to preserve a particular historical audience for later analysis.

For example, you could create a cohort containing users who triggered an event during a specific campaign period and then return to that same group later.

How to Create a Cohort in Umami

Creating a cohort only takes a few steps.

Step 1: Open Cohorts

From the Umami Analytics side navigation, click Cohorts.

On the Cohorts screen, click Add cohort.

This opens the cohort configuration screen.

Step 2: Choose your filters

The next step is to define which users should belong to the cohort.

Umami lets you filter users based on:

Date range
URL visited
Event triggered

Think carefully about what question you want the cohort to answer before choosing your filters.

For example, if you want to analyze users who visited a particular product page, configure the cohort around that URL and the relevant date range.

If you want to analyze users who completed a particular action, configure it around the corresponding event.

Step 3: Choose the date range

The date range determines when the qualifying user activity occurred.

This is an important part of cohort creation because the same URL visit or event can represent very different audiences depending on the period you choose.

For example:

A one-week range could represent visitors from a specific campaign.
A one-month range could represent users acquired during a product launch.
A custom historical range could represent a particular group you want to analyze later.

Creating a static cohort

If you want the users in your cohort to remain static, select Custom Range from the date picker.

This is useful when you want to create a fixed historical group that doesn't change as additional analytics data is collected.

Once you've configured the filters and date range, click Save.

Your cohort is now available for use in your analytics reports.

How to Apply a Cohort to Your Analytics

Creating a cohort is only half the process. The real value comes from using that cohort to analyze your data.

To apply an existing cohort:

Add a filter to your analytics report.
Switch to the Cohorts tab.
Select the cohort you want to analyze.
Click Apply.

The selected cohort is then applied to your analytics view, allowing you to examine the data specifically for that group of users.

This makes it possible to investigate how a particular audience behaves without having to recreate the same filters every time.

Practical Cohort Examples

The best way to understand cohorts is to think about the questions you want your analytics to answer.

Users who visited a specific page

Suppose you want to understand visitors who reached a particular page.

Create a cohort using:

Date range + URL

You can then apply the cohort and examine the behavior of those users elsewhere on your website.

This is useful for understanding the audience that interacted with a specific piece of content, landing page, product page, or other URL.

Users who triggered an event

Events are another powerful way to define a cohort.

For example, if your website tracks an important interaction as an event, you can create a cohort based on users who triggered that event during a selected period.

This allows you to investigate the behavior of users who completed that action.

Users from a specific period

You can also use the date range itself to define a meaningful group of users.

For example, a custom date range could represent a campaign, launch, promotion, or other period you want to study.

Using a Custom Range is particularly useful when you want that audience to remain static.

Cohorts vs. Regular Filters

Regular filters are excellent when you want to narrow down the data you're currently looking at.

Cohorts go a step further by letting you save a meaningful group of users and reuse it.

Imagine that you frequently analyze users who triggered a particular event. Without a cohort, you may need to recreate the relevant filtering conditions each time.

With a cohort, you can define that audience once, save it, and then select it from the Cohorts tab whenever you want to analyze the group.

This makes recurring analysis considerably easier.

Managing Your Cohorts

Your existing cohorts can be managed directly from the Cohorts screen.

From there, you can:

Edit a cohort to change its configuration.
Delete a cohort when you no longer need it.

If a cohort is used for an ongoing analysis workflow, consider using a clear, descriptive name so that its purpose is immediately obvious later.

A good cohort name should tell you what the audience represents—for example, the action or URL and the period it covers.

Tips for Getting More From Cohorts

Start with a question

Don't create cohorts simply because you can. Start with a question you want to answer.

For example:

"How do users who completed this action behave afterward?"

Then configure a cohort that gives you the appropriate audience.

This keeps your analysis focused and makes the results easier to interpret.

Use meaningful date ranges

The date range is often just as important as the action itself.

If you're measuring a campaign or launch, use the dates that correspond to that activity. If you're creating a historical audience that you want to preserve, consider using a Custom Range to create a static cohort.

Use events for meaningful actions

If your site already tracks important interactions as events, cohorts can turn those events into reusable audiences.

This is particularly useful for actions that are more meaningful than simply viewing a page.

Create cohorts you'll actually reuse

Cohorts are most valuable when they represent audiences you expect to analyze repeatedly.

Instead of creating dozens of nearly identical cohorts, focus on groups that help answer recurring questions about your website or product.

Turn Your Analytics Into Audience Analysis

Traditional analytics often starts with numbers: pageviews, visitors, events, and other aggregate metrics.

Cohorts let you look at those numbers from another angle.

Instead of treating every visitor as part of one large audience, you can isolate users based on what they did and when they did it. You can then return to those groups and investigate their behavior in more detail.

Whether you're analyzing visitors to an important page, users who triggered an event, or a fixed audience from a particular period, cohorts give you a practical way to turn raw analytics data into more focused behavioral analysis.

If you regularly find yourself asking "What did the users who did X do afterward?", cohorts are a feature worth adding to your analytics workflow.

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