Companies often try to understand how their customers behave, and what is driving them to change. This article will concentrate on cohort analysis.

Let’s start with the basics before we dive into the subject and try to find an example of a cohort analysis.

What’s Cohort Analysis?

Cohort analysis examines the relationships between variables and events over time.

This is an essential application in data science, as it allows you to see how variables relate to large numbers of people from different industries, parts, or populations.

Cohort Analysis Example

1. An analysis of regular coffee drinkers found that decaffeinated coffee had a greater risk of developing heart disease than regular coffee drinkers.

2. A study that looked at overweight women revealed that people who exercise more are less likely to become obese than those who don’t exercise.

3. An analysis of people born between 1975-1984 found that those who ate a lot of red meat were more likely than those who ate fewer.

Let’s now learn a bit more about cohort analysis and how it can help your data science project.

Customer Behavior Example –

Your company wants to know how different customer groups are performing. It is interested in understanding what has caused a shift in customer behavior and whether it is permanent or temporary.

Your company will collect data every month from its customers for the past 120 months (January 1, 1975, to December 31, 2016).

This will give you an estimate of the number of customers. Then, you can use regression and correlation analysis to determine how customer behavior changes over time.

Here’s the result of your study.

There has been an increase in customers quitting or changing their subscription plans over the last 120 months (regression coefficient = 0.5008).

This indicates that customers leave your company more frequently because they are unhappy with their service.

Also, the regression coefficient was -0.111 decreased over this period. This suggests that your service is getting fewer customers each month.

Steps in the Cohort Analysis

A cohort analysis is a statistical technique that can be used in both business and health.

It is easy to analyze data from cohorts that include multiple variables.

There are six steps to a cohort analysis:

1. Identification of the target cohort

This is where you choose which subset of data to analyze. In this example, customers have been with you for less than 120 months.

2. Sampling method

You will select your participants using the sampling method. There are many options, including stratified or random sampling.

3. Data collection

In general, you will collect data every month from all your customers for the past 120 months. This allows you to have a large sample of customers.

4. Analyse

Analyses are used to determine relationships between variables by using regression and correlation techniques.

5. Interpretation and conclusion

It is crucial to analyze the results and make any applicable conclusions to your industry or business.

6. Additional analysis and refinement

You may revisit steps 2-5 if the analysis shows that further refinement is needed.

Conclusion

This blog will provide information on all types of cohort analyses and their uses.

It is important to be aware of some limitations and ways to avoid them when using cohort analysis.