Cohort Analysis method and its benefits

Introduction :

In this blog, let's see about the cohort, cohort analysis, and the simple steps to create it. It is a type of behavioral analytics in which you take a group of users, and analyze their usage patterns based on their shared traits to better track and understand their actions. Cohort analysis 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.

What is Cohort Analysis?

First, let's define what a cohort is.

A cohort is simply a group of users who share similar characteristics. These characteristics can include acquisition date, demographic information (like age or location), user device, the marketing channel or campaign in which they were acquired, and other types of groupings. We may often see cohorts sharing the same characteristics of time; for example, users who were acquired at the same time, users who purchased a product at the same time, or users who subscribed at the same time. However, temporal or time-based cohorts aren’t the most valuable cohorts because they don’t tell you what to do next.

So then, what is cohort analysis?

Cohort analysis is the process of breaking up users into cohorts and examining their behavior and trends over time or over their customer lifecycle. It requires both the grouping of users and tracking them over time. As mentioned earlier, cohort analysis is a form of behavior analytics. It is also sometimes said to be a subset of segmentation and is sometimes used interchangeably with the term segmentation

Let us create a simple cohort analysis chart in a few steps. The data used here is superstore data.

Requirement: super store want to know how many new customers came, and how many of them are still active customer.

Steps to create Cohort analysis :

The first step is to create a calculated field. since we will be using this created calculated field as a measure in this case.

Calculated fields is selected from the drop-down button near the search bar.

A pop-up window will appear as shown below. There give "year" as the name. First, find the total count of customers and their first order date. From it calculate which year of acquisition this customer was. so each customer's minimum year date should be calculated first. So let's use fixed LOD (Level of Detail) and fix the minimum order date for each customer and enter the formula in the calculated field as shown below.

Then select "ok".The calculated field "year" is created as shown below.

The order date is given in columns and created calculated field "year" in rows, customer names in the text of marks.

To convert these customer names into numbers select the drop-down button of the customer name then select a measure and choose "count(distinct)".

A cohort analysis chart is created as shown below.

This cohort analysis shows that in the year "2021" we have 11 new customers, then in the year "2020" we have 51 customers out of which 45 are existing customers in the year "2021".

Benefits of effective cohort analysis :

• Accuracy

Performing a cohort analysis is a highly effective method of study as it helps to separate the clients into cohorts. Thus, individuals who joined the site during a particular period are grouped together e.g. the March cohort, the April cohort, and so on. This way, the analysis of their engagement and how it has changed over time, is unaffected by the individuals in other groups, thus keeping the groups completely independent of one another, and facilitating a more accurate study.

• Providing a clear distinction between Growth and Engagement

Separating the clients into cohorts is also effective in clearly defining the difference between growth metrics and engagement metrics. These two measurements can sometimes be confused with each other as growth is the successful addition of clients who use one’s product or service. Generally, added numbers automatically increase the overall engagement but it may only be the new clients who access the website, and will probably cease to do so after a while.

• Effective Comparison of data between Cohorts

A cohort analysis also helps one to compare the results between two or more groups. For instance, if the April cohort is more engaged in the product than the March cohort, an analysis may be required on any changes that may have occurred between the two months. In addition, further analysis may be performed on the groups themselves to see whether the product is possibly appealing to a particular set of people and not another.

• It can help you understand the long-term and current health of your business. By tracking users over time, you can see if your business is actually successful at acquiring users and retaining them.

• It can tip you off to whether or not changes in your site or product are affecting users. By tracking groups of users over time, you can zoom in and see if their change in behavior coincides with a launch of a new product feature or a change to the website.

• It can help your company make better and more informed decisions and predictions. By tracking users over time and examining a particular cohort’s behavior, you can take more strategic steps like developing a targeted campaign for your most valuable users all based on meaningful data.

• It allows you to track the customer life cycle of specific groups. By tracking groups of users, you can get a clearer view of how and when your customers engage with your product or business. This allows you to see customer lifetime value.

Conclusion :

A cohort analysis also helps in identifying times when engagement in the site drops. Since it is a study that takes time into consideration, decisions can be made fast in an effort to rectify the problem areas that may have resulted in the drop. By the factoring-in time, there is a clear temporal sequence when analyzing the relationship between first contact and consequent results.