Strategy

Cohort Analysis

A practical product method for comparing user groups, tracking retention, and understanding behavioural change over time.

How to use cohort analysis to compare user groups, understand retention and engagement, and measure the impact of changes over time.

25 May 20234 min read

Quick take

If you want to understand how different groups of users behave over time, use cohort analysis.

What it is

is a quantitative UX and product method used to group users based on shared characteristics and track their over time.

A cohort might be users who signed up in the same week, used a for the first time, or came from a specific .

Instead of looking at all users as one group, shows how changes across different groups and over time.

The goal is to understand , , and the impact of changes on different user segments.

Cohort analysis is useful when averages hide important differences between user groups.

When to use it

Use this method when time and matter.

It is most useful when:

You want to understand retention over time
You need to compare behaviour between user groups
You are measuring the impact of product changes or releases
You want to identify trends in engagement or drop-off
You are analysing onboarding or feature adoption

It is less useful when:

You only need a snapshot of current behaviour
Data is limited or poorly structured
Cohorts are not clearly defined
Cohort analysis is often used alongside feature usage analysis and funnel analysis to provide deeper insight into behaviour over time.

Key takeaway

Use cohort analysis when you need to understand how behaviour evolves across meaningful groups, not just across the total user base.

How to run it

Set up properly.

Before you start, be clear on how cohorts are defined, what or metric you want to track, and what time period you will analyse.

Ensure is consistent and aligned across cohorts.

Run the method.

is structured and comparative.

Group users into cohorts based on shared characteristics. Track over time for each cohort. Measure metrics such as , , or conversion. Compare performance between cohorts. Identify trends and changes over time.

Focus on differences between groups rather than overall averages.

Capture and make sense of it.

The value comes from understanding over time.

Look across to identify trends, differences between cohorts, impact of product or design changes, and long-term .

Use this to guide improvements and .

What to look for

Focus on:

Retention
How many users return over time
Engagement
How actively cohorts use the product
Behaviour changes
Differences before and after updates
Cohort comparisons
Variations between different user groups
Trends
Patterns across time periods

Where it goes wrong

Most issues come from:

If cohorts are not meaningful, the will not be either.

poorly defined cohorts
inconsistent or incomplete data
focusing on averages instead of differences
ignoring external factors
overcomplicating the analysis

What you get from it

Done properly, this method gives you:

clear understanding of user behaviour over time
insight into retention and engagement patterns
ability to measure impact of changes
evidence to guide product decisions

Key takeaway

It helps you see how behaviour evolves, not just what is happening now.

Get in touch

If this sounds like something you need, we can help you understand how different users behave over time and what drives .

No guesswork. No assumptions. Just clear you can act on.

FAQ

Common questions

A few practical answers to the questions that usually come up around this method.

What is cohort analysis in UX?

is a method used to group users and track their over time.

When should you use cohort analysis?

Use it when analysing , , or the impact of changes across different user groups.

What is a cohort?

A cohort is a group of users who share a common characteristic, such as sign-up date or .

How does cohort analysis improve products?

It helps identify trends, measure impact, and understand long-term .

What tools are used for cohort analysis?

Tools such as Google Analytics, Mixpanel, Amplitude, and Tableau are commonly used.

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Previous feedback

Will Parkhouse

Senior Content Designer

01/20