Companies are often trying to make sense of how their various cohorts of customers are doing, such as understanding what has prompted a change in customer behaviour. This article will focus on analyzing cohort analysis.
Before diving right into the topic and trying to find a cohort analysis example, let's just learn some basics.
Cohort analysis is a type of longitudinal study that examines the associations between different variables and events over time.
It is a useful application in data science because it helps to understand how these variables are related to large groups of people from different parts of an organization, industry, or population.
1. A study of people who drink coffee regularly found that those who drank decaf had a higher risk of heart disease than those who drank regular coffee.
2. A study of overweight women found that those who exercised more had a lower risk of becoming obese than those who did not exercise.
3. A study of people born between 1975 and 1984 found that adults who consumed high amounts of red meat were more likely to have cancers of the pancreas, liver, and colon than those who consumed low amounts of red meat.
Now that you know a little bit about cohort analysis and what it can do for your data science project, let's take a look at an example to get started!
Your company is trying to understand how different cohorts of customers are doing. It wants to know what has prompted a change in customer behaviour and whether this change is permanent or temporary.
To do this, your company collects data from all of its customers every month for the past 120 months (from January 1st, 1975 until December 31st, 2016).
This will give you a sample size of 120,000 customers. You then use correlation and regression analysis to see how customer behaviour changes over time.
Over the past 120 months, there has been a significant increase in the number of customers who have quit or changed their subscription plan (regression coefficient = -0.508).
This suggests that more customers are abandoning your company due to dissatisfaction with their service.
Additionally, there was a decrease in the number of new subscriptions (regression coefficient = -0.111) over this time period, which suggests that fewer new customers are signing up for your service each month.
The cohort analysis can be described as a statistical technique used in business and health.
It is simple to analyse the data collected through cohorts with multiple variables.
The cohort analysis has six steps:
1. Identification of the target cohort
This is where you decide which subset of your data to analyze – in this case, customers who have been with your company for at least 120 months.
2. Sampling method
The sampling method will determine how you select the participants in your target cohort. There are a variety of options available, such as randomized or stratified sampling techniques.
3. Data collection
Generally speaking, you will collect data from all of your customers every month for the past 120 months. This gives you a large sample size to work with.
4. Analysis
The purpose of the analysis is to find relationships between variables using correlation and regression techniques.
5. Interpretation and conclusions
After completing the analysis, it is important to interpret and draw any conclusions that can be applied to your business or industry as a whole.
6. Further analysis and refinement
If the results of the analysis indicate that there is a need for further refinement, you may decide to revisit steps 2-5.
This blog has listed all the different types of cohort analyses that you should know about and what they are used for.
It is also important to know some of the limitations associated with cohort analysis and how to avoid them. I hope you also know a number of cohort analysis example now.
That's all for now! See you later with a different topic! Till that keep the conversation going in the comment section below.
