Cohort analysis is a kind of behavioral analytics that breaks the data in a data set into related groups before analysis. These groups, or cohorts, usually share common characteristics or experiences within a defined time-span.[1] [2] Cohort analysis allows a company to "see patterns clearly across the life-cycle of a customer (or user), rather than slicing across all customers blindly without accounting for the natural cycle that a customer undergoes."[3] By seeing these patterns of time, a company can adapt and tailor its service to those specific cohorts. While cohort analysis is sometimes associated with a cohort study, they are different and should not be viewed as one and the same. Cohort analysis is specifically the analysis of cohorts in regards to big data and business analytics, while in cohort study, data is broken down into similar groups.
The goal of business analytics is to analyze and present actionable information.[4] Large, undifferentiated datasets may include a variety of user types and time periods. Cohort analysis analyzes the users of each cohort separately. In cohort analysis, "each new group [cohort] provides the opportunity to start with a fresh set of users,"[5] allowing the company to look at only the data that is relevant to the current query and act on it.
For example, in eCommerce, customers who signed up in the last two weeks and who made a purchase may constitute a cohort. For software, users who signed up after a certain upgrade, or who use certain features of the platform, may constitute a cohort.
An example of cohort analysis of gamers on a certain platform: Expert gamers, cohort 1, will care more about advanced features and lag time compared to new sign-ups, cohort 2. With these two cohorts determined, and the analysis run, the gaming company would be presented with a visual representation of the data specific to the two cohorts. It could then see that a slight lag in load times has been translating into a significant loss of revenue from advanced gamers, while new sign-ups have not even noticed the lag. Had the company simply looked at its overall revenue reports for all customers, it would not have been able to see the differences between these two cohorts. Cohort analysis allows a company to pick up on patterns and trends and make the changes necessary to keep both advanced and new gamers happy.
"An actionable metric is one that ties specific and repeatable actions to observed results [like user registration, or checkout]. The opposite of actionable metrics are vanity metrics (like web hits or number of downloads) which only serve to document the current state of the product but offer no insight into how we got here or what to do next."[6] Without actionable analytics, information may not have any practical application; the information may simply be a non-actionable vanity metric. While it is useful for a company to know how many people are on their site, that metric is useless on its own. For it to be actionable it needs to relate a "repeatable action to [an] observed result".
Cohort analysis has four main stages:[7]