Cohort Analysis is one of the real-time concepts that every Data Science professional should know. Cohort Analysis helps companies analyze and understand the behaviour of their customers or users over time. So, if you want to know what Cohort Analysis is and how a Data Science professional can help businesses using this technique, this article is for you. This article will take you through everything about Cohort Analysis that you should know as a Data Science professional.
What is Cohort Analysis & When to use it?
Cohort analysis is a real-time data science concept that helps companies analyze and understand the behaviour of their customers or users over time. It is used to identify trends and patterns in customer behaviour based on when they started using a product or service.
By grouping customers into cohorts based on when they first engaged with a product or service, companies can analyze the behaviour of different groups of customers over time and use this information to make informed decisions about product development, marketing and customer retention strategies.
Let’s take an example of a real-time business problem to understand when to use Cohort Analysis. Suppose a streaming service wants to know how long customers stay subscribed to their service and if there are any customer behaviour patterns to improve retention rates.
Using cohort analysis, the company can group customers based on when they first subscribed and analyze the average length of subscriptions for each segment. It can help the business identify which customer segments have the highest retention rates and which segments need more attention to improve retention.
Some Valuable Terms You Should Know for Cohort Analysis
Below are some valuable concepts and terms that every data science professional should know before starting with cohort analysis:
- Cohort: A group of customers or users who share a common characteristic, such as the month or year of their first engagement with a product or service;
- Cohort period: The period during which a cohort is analyzed, such as the first three months after a customer first engages with a product or service;
- Cohort retention rate: Percentage of customers in a cohort who continue to use a product or service over time;
- Cumulative retention rate: The percentage of customers who continue to use a product or service over time, regardless of when they first engaged with it;
Process of Cohort Analysis
It doesn’t matter what tool you use for analyzing cohorts, but here’s the process that you should follow while analyzing cohorts as a Data Science professional:
- Define Cohort: Determine the characteristic that will define the cohort, such as the month or year a customer first engaged with a product or service.
- Determine the period of the cohort: Decide the period during which the cohort will be analyzed, for example, the first three months after a customer first engages with a product or service.
- Calculate Cohort Retention Rate: Determine the percentage of customers in each cohort who continue to use the product or service over time.
- Visualize the data: Create a cohort chart to visualize each cohort’s retention rates over time.
- Interpret the results: Analyze the graph to identify trends and patterns in customer behaviour, and use this information to make informed decisions about product development, marketing, and customer retention strategies.
You can see an example of analyzing cohorts using Tableau here.
Summary
Cohort analysis is a real-time data science concept that helps companies analyze and understand the behaviour of their customers or users over time. It is used to identify trends and patterns in customer behaviour based on when they started using a product or service. I hope you liked this article on an introduction to Cohort Analysis in Data Science. Feel free to ask valuable questions in the comments section below.
