Cohort Analysis

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by Foti Panagiotakopoulos Founder at GrowthMentor

Table of Contents

What Is a Cohort?

A cohort is a group of customers, clients, or users who share something in common. This commonality is always time-based, such as:

  • sign-up date
  • first purchase date
  • referral date

Customers can be part of more than one cohort, depending on what you decide to analyze.

Cohorts vs. Segments

Cohorts and segments are similar, but there’s an important distinction to be made.

Cohorts are groups that share an experience (e.g., when they first bought a product). Segments are groups that share a characteristic (e.g., age, location, company size, etc.).

In other words, cohorts are based on behavior while segments are based on attributes. As a result, there’s very little overlap with the insights gained from cohort analysis and segment analysis.

What Is Cohort Analysis?

Cohort analysis studies the behavior of a group (i.e., cohort) of customers, clients, or users over a period of time. More specifically, cohort analysis tracks changes in behavior within a cohort over time (e.g., not converted → converted).

Cohort analysis is used by businesses to better understand how their customers interact with their product, service, or brand over time. The goal is to identify trends and commonalities in customer behavior in order to improve the customer experience and increase retention.

How To Perform a Cohort Analysis (with Examples)

Today, many platforms allow you to automate and derive insights from the analysis, including Google Analytics, Segment, Mixpanel, and so on. But here is an example to help you understand the mechanics behind it.

There are three ingredients involved:

  1. Cohorts: The groups you’ll be tracking (e.g., sign-ups by month).
  2. Behavior: The behavior or action you’ll be tracking in the cohorts (e.g., conversions).
  3. Time Period: The time period over which you’ll track the cohort (e.g., monthly).

Cohort analysis is far easier to understand visually, so we’ll show the data in a table.

Cohort Analysis Example

User retention rate by sign-up month
CohortCustomersMonth +0Month +1Month +2Month +3Month +4Month +5
Jan 68378%67%65%43%40%35%
Feb59869%67%65%61%58%57%
Mar1,58088%73%71%64%53%41%
Apr2,04578%69%63%57%50%43%
May1,95579%68%66%60%52%48%

In this example, we’re seeing user retention rates for cohorts defined by their sign-up month. There are tons of observations you could make about this table – the unusually high 5-month retention rate for February sign-ups, for example. 

Cohort analysis can’t explain why things like this are happening, but it can reveal trends that happen over time. For instance, the data above raises some important questions:

  • Would it make sense to raise the February acquisition budget, because of the higher retention rate? 
  • Is there a way to increase 5-month retention for the lower-performing cohorts? 
  • Why (on average) is there such a big drop in retention after 1 month?

These highly targeted questions show the real power of cohort analysis.

How to Use the Results of a Cohort Analysis 

Once you’ve performed a cohort analysis, it’s time to put the results to use. There are a few areas where the results of a cohort analysis can be especially useful:

1. Acquisition

Cohort analysis is great for tracking the performance of different acquisition times and channels. This can help you identify which are most effective at acquiring high-value customers, allowing you to reallocate your marketing resources.

2. Retention

Cohort analysis can also be used to track retention rates. This is useful for understanding which cohorts are more likely to stick around (and why), and for identifying any trends in customer behavior.

3. Engagement

In addition to tracking retention rates, cohort analysis can also be used to track engagement rates. This is useful for understanding how different cohorts interact with your product, service, or brand.

4. Revenue

Cohort analysis can also be used to track revenue. This is useful for understanding which cohorts generate the most revenue, and for identifying any trends in customer behavior.

Learn About Cohort Analysis and More 

Contact our growth mentors if you need further assistance understanding cohort analysis, recommendations for cohort analysis software, or any targeted advice using your own cohort analysis for your online business. GrowthMentor has over 400 mentors ready to help your startup achieve more than you ever dreamt was possible. 


Suggested mentors to help you make sense of Cohort Analysis

Michael Taylor

Marketing Memeticist

Data-driven, technical marketer with 11 years experience, 8,000 experiments run, and $50m optimized across all 4 major growth channels. Author of Marketing Memetics, Co-Founder at Vexpower, Ex-Founder at Ladder.

Dimitris Lianoudakis

SaaS Intrapreneur

I am the product of the relationships I have built, the countries I have lived and worked in, and the people I have shared that time with. An entrepreneur and analyst at heart with a strong belief in repeating successful actions that bring constant value to the customer.

Kuba Rdzak

Growth @ Juo.io 🧑🏽‍🚀🚀 • Growth Marketer, Media Buyer & certified Team Manager • Top 1% CXL • Ex-Ladder.io

Hello! I’m Kuba and I’d love to help you grow and overcome challenges 🙂 I’ve spent 70+ hours helping Mentees. I also helped 150+ companies with finding PMF or GTM strategies, scaling paid ads (Facebook, LinkedIn etc.), PPC, tracking, CRO and marketing automation. Check my reviews and let’s meet! 🙂

Jason Barbato

Growth, Inbound, Product Marketer. Advisor and Mentor. Former Best-In-Class Enterprise Growth Hacker at IBM.

Currently a SaaS and startup growth consultant and Senior Director of Growth at HYPR. Former VP of Growth at Orange Pegs, an award-winning growth agency. From 2016-2019 I developed, launched, and scaled a $40M+ growth hacking program at IBM, running 200+ experiments across the Cloud marketplace.

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