What is a Cohort Analysis?
Cohort Analysis is the selection of data from a collection of the larger data set and instead of analyzing the users as a single unit, the analysis separates the data of users into smaller groups based on different characteristics for the ease of analysis.
Cohort Analysis is a subset of behavioral analytics. Cohort means “class of people who have common characteristics.” In ancient Rome, the cohort used to be a military unit that had a certain number of men. The extended usage of the word “cohort” now means any group of people that have a common statistical factor. It is usually carried out for the sake of making analysis robust and relevant. It is used to track and analyze the performance of cohorts over time.
When groupings are time-dependent, it is known as a cohort. When groupings are not time-dependent, it is known as a segment.
Example of Cohort Analysis
Cohort Analysis is common usage in today’s world where businesses have moved closer to their customers. The most common examples can be seen in eCommerce businesses. Take, for example, an e-commerce business that generates data on its customers that have purchased products from the online portal. The data also gives insights into customer expenditures, product ratings, product returns, and other related metrics. Notably, when the groupings are done based on time, any characteristic other than time-dependent variables, it is referred to as a segment.
Another example is when acquired users are tracked and compared across different time periods.
Check out this illustration:
In this example, we have a webpage owner who wants to evaluate the traffic on his webpage and the revenue that it is creating. Following are some denotation:
- Series 1 – New-users revenue
- Series 2 – Old-users revenue
- Series 3 – Monthly revenue (Add series one and series 2)
He performs an analysis by segregating cohorts on time-basis. He makes the following classifications based on his analysis above:
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- Cohorts in the time-period August to October have amassed highest revenue in the new-user segment (as a proportion of monthly revenue)
- Cohorts in the time-period January to March have the lowest revenue in the new-user segment.
- Despite higher revenue from new-user cohorts, monthly revenue did not rise because of poor revenue from old-user cohorts.
This chart gives the webpage owner useful patterns in his business variables and helps him make strategic advancements.
Performing Cohort Analysis
It can be performed in the following manner:
#1 – Determine the Objective of the Analysis
Like every other analysis, it also needs objectives that it can fulfill, and for that, the user must preliminarily determine the need to use.
E.g., Find the revenue generated by a website or a complex issue like strategizing for improvement to the webpage using cohort.
#2 – Carve out the Metrics that Associate with the Objectives
After having the determined objective of the analysis, the user should look for appropriate metrics that will improve the success rate of the cohort analysis.
E.g., number of retained customers, number of tickets sold, per-user fee generated, etc.
#3 – Determine if all Cohorts are Necessary
If the study is about finding customer retention rate on a webpage, then the user should determine which customers to identify as a cohort between certain groups like old customers, new customers, one-time customers, etc.
#4 – Perform the Analysis
Once all the above steps are carried out, the user can start doing his analysis. He can find out how his webpage has fared in metrics like customer views, customer retention over a certain time period. During this analysis, the user should be careful in determining the actionable insights that are coming out of the analysis. The analysis will always show you what you want to see. Beware of holding any biases.
#5 – Prepare and Present Results
Log down the results of the analysis in whichever form they are required, for example, charts, tables, summarized text, etc. A cohort analysis is very specific to its user, and hence its results should be properly (simplified) communicated to others.
- It gives its users accuracy and effectiveness when they perform the segregation of large data sets.
- The analysis is helpful is working on a wide range of data.
- It helps in easier and quicker decision making. Dealing with large data sets presents common problems related to arranging and segregating data into relevant groups. Cohort analysis, by its nature, is a tool to address this problem.
- From the purpose of business, the marketing and sales teams can clearly demarcate between the clients contributing to engagement and growth purposes.
Such methods are a useful tool when working with data. However, there are some limitations that users should know beforehand:
- Biases – Analyses are always subjective, and biases form an inevitable part of analysts’ thought process. Like any other analysis, it can also fall prey to the biases of the users (analysts). Some of the biases are selection bias, decision bias, personal bias
- Cohort analysis can be used only when data have clear and distinct groups of characteristics. However, the possibility of the absence of characteristics is almost nil. Nonetheless, only those data that have some form of statistical correlation should be used
- Cohort analysis is only a tool that provides time-dependent metrics to users.
Cohort analyses are mostly used in marketing studies. Marketing campaigns are built on cohort analysis, whereby comparison among different study groups based on specific variables allows changes in marketing strategies. Some of the factors that are used are the target audience, ad content, new product lines, etc. Other examples are spending patterns of customers or their feedback on products and services.
Cohort analysis shows patterns in the life cycle of a user (or customer) and helps in deciding strategies around customer retention and engagement. It should provide actionable insights if successfully implemented. It is incredibly useful for understanding business changes, business markets, and seasonality.
This has been a guide to what is cohort analysis and its meaning. Here we discuss examples and performance of cohort analysis along with benefits and limitations. You may learn more about financing from the following articles –