Cohort Analysis

 

 Cohort Analysis is a kind of behavioral analytics that divides the data into groups. It helps a company to see the data outline across the life cycle of its customers. Basically, this is a process of observing how their behavior changes.

 Cohort means “a supporter, a companion or a class of people who have unusual characteristics.” In ancient Rome, cohorts used to be a military unit with a certain number of men. The extended implication of the word “cohort” now surmises as a group of people with standard statistical factors.  

However there is a big difference between the words ‘cohort’ and ‘section’. We can understand the difference between these two words here.  

When alignment of any data is time reliant, we say it a cohort. Once, this time dependency is missing from the group examination, it comes under segmentation. 

 

Let us see some example of Cohort Analysis

 

In the finance sector, we can define cohort analysis as a logical scale for splitting user’s data and study the facts. Cohort analysis has its own significant role in today’s era where businesses have come closer to their customers.

 If we talk about e-commerce firms, Cohort analysis becomes very common. For example, online shopping is very common and most of us shop from online portals. When any e-commerce company starts gathering data of its customers, it reads information like expenditure budget, experience, returning, negative or positive feelings, comments, etc. 

  Now we can understand that when grouping is based on time, any characteristic other than time-dependent variables is referred to as a segment.

 Check out more examples to understand the concept. When an e-commerce company tracks and compares the data received from its acquired users to evaluate the traffic and revenue generation, he uses denotation like this:

 ·         Series 1- New users revenue

·         Series 2- Old users revenue

·         Series 3-  Total monthly revenue (add both series 1 and 2)

 

 

He performs an analysis by segregating Cohorts on a timely basis. After examining the data, the company makes the following classifications.

• As a proportion of monthly income, The cohort has grabbed the highest revenue in the new user segment from August 1 to October 30.
• From January 1 to March 30, Cohort has recorded the lowest revenue in the new user segment.
• As the payment from old users associated was quite low, monthly income could not rise even after having higher revenue from new user Cohort.

  

We can perform Cohort analysis in following manner:

 

 ·         Determine the Objective of Analysis: Like other analyses, Cohort also needs an objective that can be fulfilled. For this, the user must be pre-determined towards the goal of the analysis.

 

 

For example: Look out the income generated by another company for improving your webpage using cohort.

 

·         Carve out the metrics associated with the objective:  After determining the objective, the user should find out appropriate metrics that help to improve the success rate of Cohort analysis.

 

For example:  Number of retained customers, number of tickets sold, fee generated per user, etc.

 

·         Determine the important Cohorts:  If you are studying to find customer retention price on the webpage, you should identify customers as a Cohort between certain groups.

 

 For Example: old customers, new customers, and one-time customer