Data Analytics

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

Written by John Terra
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Updated on April 11, 2024

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.

Also Read: Tutorial: Data Analysis in Excel

The Types of Cohort Analysis

The two most common types of cohort analysis are:

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.
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.

Time-based. This method separates customers into groups based on a specific timeframe.
Size-based. This method refers to the potential client base’s budget.
Segment-based. This analysis divides customers based on their subscription plan.

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

It can determine your business’s health. A great indicator of a healthy business is increased revenue, even if the company isn’t gaining new customers. Businesses can conduct cohort analysis to identify which cohorts or customer groups contribute the most to revenue. This process, in turn, allows your business to focus on upselling other goods and services to the consumer.
It helps you understand your customers better. Cohort analysis allows businesses to better understand their customers by tracking their behavior over time. This can help find patterns and trends that may not be immediately obvious from simply looking at vanity metrics.
It provides enhanced customer segmentation. Businesses can devise more targeted and effective marketing campaigns by dividing user groups, creating specific cohorts, and offering personalized customer experiences.
It increases customer retention. Cohort analysis helps businesses analyze retention rates and identify potential churn risks. With this information, you can take proactive steps to improve customer experiences.
You can use it to optimize your app for increased consumer interest. Cohort analysis can optimize user experiences and increase customer lifetime value by spotting trends and patterns in the customer lifecycle.

The Drawbacks of Cohort Analysis

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

It is often essential to follow many subjects for an extended period to successfully perform a cohort analysis and then perform follow-up protocols after the study. This effort can be costly and demand a lot of time and resources, possibly causing attrition in studies that span many months or years.
Cohort studies can lead to ethical problems in health studies when the evidence begins to be collected to indicate a risk factor. When this happens, the investigator must educate those with the risk factor. If the investigator chooses to wait and watch for results instead, this leads to ethical quandaries.
Although cohort studies perform well in instances of rare exposure where a population was exposed to a particular factor, cohort analysis is not an effective analytics tool when working with rare diseases or those with a long latency.

Also Read: Overview: What is Exploratory Data Analysis?

The Typical Cohort Analysis Steps

These are the typical cohort analysis steps:

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.
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.
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.
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.

Import the data. Either import customer information from a database or manually enter it into the spreadsheet. Ensure the imported data has the information you want to separate customers by. For example, if you wanted to see if the month the customer downloads the app influences engagement, you’d want a column that lists every download date.
Create your cohorts. Create a final column and label it “Cohort.” Enter a formula that categorizes the customer in that row based on your established criteria. For example, if you wanted to group customers by which month they downloaded the app, you could use a formula like this: “date(YEAR(X), MONTH(X), 1)”.
Create your “Months” column. Now, create a “Months” column to the right of “Cohort,” and place this formula in the first cell: “IF(ISNUMBER(X), ROUND((X-Y)/30,0), “active”)” The first argument ascertains if that row’s download date contains a value. If it doesn’t have the value, it outputs the value “active” to show the customer is still using the app. But if the download date in the row does contain a value, it subtracts Y from it, with Y being the date the customer deleted the app. Finally, it divides this number by 30 to generate the months the customer stayed active on the app.
Create your pivot table. Hit the “Insert” tab in the top ribbon, and select “PivotTable” in the “Tables” group. Excel automatically chooses the data range in your spreadsheet, but you can manually adjust the range in the dialog box. Choose if you want the table to appear in that existing or new worksheet, then click “OK.” Drag “Months” from the fields list to the “Columns” area, then drag “Cohort” from the list of fields to the “Rows.” Finally, drag “Customer” to the “Values” area, and note that the number in each field now indicates the number of customers you lost per period.
Create the Retention Rates sheet. Select your pivot table, then right-click and select “Copy.” Next, click the plus (+) button next to the existing sheets at the bottom of your spreadsheet, and double-click on the sheet name to rename it “Retention Rates.” Now, paste the data from the pivot table into the “Retention Rates” spreadsheet. Select the “Grand Total” column at the right, then right-click and select “Cut.” Right-click the column after the “Cohort” column, then click “Insert.” Paste the “Grand Total” column into the new column. Now, select the entire table, right-click, and select “Copy.” Finally, right-click anywhere below the table and select “Paste.”
Calculate the number of customers you retain each month. Select the first value after the “Grand Total” column in the top table. Type an equal sign “=” and reference the first value in the “Grand Total column.” Now, type a minus sign “-,” then reference the corresponding cell in the second table. Once you’ve done that, press “Enter” and drag the fill handle to all cells in that row. Finally, select all the values in the first table, then press “Crtl” + “D” to copy that formula to all the cells.
Finally, calculate your retention rates. Select the second table, then right-click and select “Copy.” Next, right-click at any point above the first table, then click “Paste.” In this new first table, choose the first cell to the right of the “Grand Total” column and type “=,” referencing the corresponding cell in the second table. Next, type a divide symbol and reference the cell to the left of that cell. Press “Enter” and copy that formula into all the other cells in the row using the drag and fill handle. Finally, convert the values in the cells to percentages by selecting all the cells and clicking the percent symbol in the “Number” group. And you’re done!

When to Use Cohort Analysis

Here are the situations where you should use cohort analysis.

Business use cases and marketing campaigns
When you want in-depth insights into customer/user behavior and website performance
When you’re trying to reduce early customer churn
Contact tracing, especially during a pandemic or disaster

Also Read: Data Analyst Job Description: What Aspiring Professionals Need to Know

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.

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.

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.

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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.