What is Cohort Analysis? Types, Benefits, Steps, and More

What is Cohort Analysis

A business that better understands its customers is on the fast track to success. The trick is developing a way for the business to reach this level of understanding, getting to know their current (and potential) customers’ preferences, behaviors, and tastes. That’s why there’s cohort analysis.

This article discusses cohort analysis and how businesses can use it to increase sales and boost competitiveness. We’ll define the term, outline the different types, describe the typical steps, explain how and when to use it, and provide other valuable information and advice like a data analytics program professionals can upskill through.

So, let’s get started with that promised definition. What is cohort analysis?

What is Cohort Analysis?

Before defining cohort analysis, we need to establish what a cohort is. After all, we need to know exactly what’s being analyzed! In brief, a cohort is a group of people with shared characteristics. Let’s expand on that. A cohort is a group of people who share a common characteristic over a given period, such as people who became customers at about the same time, an entire graduating class of senior students, or contact tracing people during a pandemic. The defining characteristic can technically be anything but usually refers to a time-dependent grouping in this context.

So, cohort analysis describes tracking and investigating cohort performances over a period of time. It is considered a subset of behavioral analytics, where the analyst takes a group of users and analyzes their behavioral and usage patterns framed by their shared traits. This helps organizations better understand and track their actions.

Now, let’s see what kinds of cohort analyses exist.

The Types of Cohort Analysis

The two most common types of cohort analysis are:

  1. Acquisition cohorts . These groups are divided based on when they signed up for the company’s product. Typically, this user group’s shared characteristics allow churn and retention rates to be measured within a given timeframe.
  2. Behavioral cohorts. These groups are divided based on their behaviors and actions towards your product. This analysis type lets the business view its active users based on demographics and diverse behavioral patterns.

There’s an alternate way to break down cohort analyses.

So, the types of cohort analyses are directly related to the cohorts’ classifications. Now that we have sorted that out, what are the benefits of cohort analysis?

The Benefits of Cohort Analysis

The Drawbacks of Cohort Analysis

Every tool is flawed. Cohort analysis has its downsides, just like any other resource. These include:

The Typical Cohort Analysis Steps

These are the typical cohort analysis steps:

  1. Extract the raw data. With MySQL, you can import raw data from a database into a spreadsheet where your user attributes can be joined and segmented further.
  2. Create cohort identifiers. Now, group your user data into different buckets, such as date of first purchase, date joined, graduation year, all mobile devices located at a particular place and time, etc.
  3. Calculate lifecycle stages. After users have been split into cohorts, measure the time between events attributed to each customer so you can calculate lifecycle stages.
  4. Create tables and graphs. Pivot graphs and tables visually represent your user data comparisons and help calculate multiple-dimension aggregations of user data.

How to Use Cohort Analysis

Here’s how to conduct a cohort analysis using Microsoft Excel.

When to Use Cohort Analysis

Here are the situations where you should use cohort analysis.

Reading a Cohort Chart

Here’s an example of how to read a cohort chart. The image is courtesy of chartio.com. This chart plots weekly revenue per user. In this example, cohorts are defined as the users gained within a particular week.

Reading a Cohort Chart

The weekly cohorts are listed along the vertical axis, with the oldest cohorts at the top and the newest ones at the bottom. The horizontal axis shows the time periods since the cohort’s start. In our example, the period ranges from week 0, the week of acquisition, to week 4, which is four weeks from the week of acquisition.

The middle cells have the weekly revenue per user and the corresponding values for the metric you’re plotting. Our chart shows that, on average, users acquired the week of Nov 19 spent $3.70 during their acquisition week (which we’ve established as week 0). The following week, those same users, the ones acquired on the week of Nov 19, spent $1.09 on average, then the next week, week 2, they spent $0.73, etc.

Some analysts use color shading to show data trends better. Here’s the same data from our original example, but with shading added. The darker the color, the greater the user revenue.

Cohort Chart

Notice how the shading makes it easier to see how the cohorts’ values decay over time, indicating that the users spend more in the earlier weeks than later. The colors also make it easier to spot anomalies. Can you spot it?

Hint: Look at Week 0.

Do You Want to Learn Data Analytics?

Data analytics is a vital part of today’s digital marketing efforts. If you’d like to learn more about this fascinating field and gain marketable skills, consider this data analytics bootcamp . This 24-week online program helps you acquire expertise in core skills and tools such as Excel, ETL, Python, SQL, Tableau, Power BI, Generative AI, and more.

Glassdoor.com reports that data analysts earn an annual average salary of $90K . If you want a secure, well-paying career, take this program and gain the skills you need to enter this challenging and exciting vocation.

FAQ

Q: What is cohort analysis?
A: Cohort analysis describes tracking and investigating cohort performances over a period of time.

Q: What are the types of cohort analysis?
A: There are two types of cohort analysis: acquisition and behavioral.

Q: Who uses cohort analysis?
A: Cohort analysis is useful for data analysts, digital marketers, and especially healthcare professionals, although anyone who works with data and demographics can use it once they understand how it works.