For any app or business owner, product success is not only about getting thousands of downloads, but it is also dependent on the customer’s repeat visits.  Because, when you spend a lot of effort and money acquiring new customers, but if they lasts only for a few days it’s a loss of effort and money. To achieve a higher customer retention rate, you should go beyond the metrics such as MAU or DAU to measure the growth and customer retention superficially. In this case, cohort analysis comes as a smart solution. Cohort analysis looks at all the user data as one and split them into related groups for analysis. The vanity metrics like DAU or MAU don’t add much value to your business or help you in making any business decisions, not only can they waste your efforts, but also mislead you with wrong business decisions. In general, these groups usually share some common experiences or characteristics within a constrained time span. 

Let’s look deeper at the basics of the cohort analysis and find out how it is measured. 

What is the Cohort Analysis? 

In general, Cohort analysis is a kind of behavioral analytics that takes the customer information from a given web application, or eCommerce site, or any online gaming site. Instead of considering all clients as one unit, it breaks them into related subsets for analysis. These related cohorts or groups typically share common experiences or characteristics within a specified time-span. It also enables you to compare the metrics and behavior of different cohorts over time. This analysis helps you identify the lowest-performing and highest performing cohorts and the factor that drives this performance. 

Cohort Analysis is a measure that is used to quantify user engagement over time. It assists in understanding whether user engagement is really showing signs of improvement after some time or just seems to improve due to the growth.

Cohort analysis turns out to be a significant tool in customer retention, as it assists with figuring outgrowth metrics from other engagement metrics because growth can easily be covered up with user engagement challenges. Due to the growth factors or numbers, the lack of activity from the old users are being covered with the attractive growth numbers of acquired users. This will ultimately lead to a lack of engagement. 

Why should you use cohort Analysis?

Cohort Analysis is a better perspective for looking at data. Its application isn’t restricted to a particular industry or operation. For instance, eCommerce firms can execute cohort analysis to spot items that have room for more potential sales growth. In the field of digital marketing, it can help to identify web pages that show good performance based on conversions, websites, and sign-ups. Likewise, in product marketing, the cohort analysis can be utilized to figure out the success rate of new features and churn rates.

When we are talking about the cohort analysis, there are two types of cohort analysis:

– Acquisition cohorts

– Behavioral cohorts

Acquisition Cohorts

In Acquisition cohort analysis the users are divided based on the time of acquiring or the when they signed up for a service or product. Based on the product, user acquisition can be tracked weekly or monthly. 

Behavioral Cohorts

Behavioral cohorts divide consumers based on their daily activities which they perform within the app during certain periods of time. 

Cohort analysis in decision making

Cohort analysis helps businesses progress towards data-driven decision-making.

Mobile marketers are involved in multiple tasks such as tweaking the customer onboarding process, running campaigns, introducing new product features, and much more. Cohort analysis can help customers to evaluate the effectiveness of each of these activities.

Additionally, it has multiple benefits that will assist you to perform in a much better way. Below listed are some of the benefits of implementing cohort analysis:

– Predicting future consumer behavior, with current data

– Determining activities, changes, or features that retain customers 

– Proactively planning for user engagement activities depending on feature adoption 

– Implementing a non-intrusive marketing framework that is completely based on a data-driven process

– All these activities together and individually help in increasing customer retention.

Cohort Analysis Example

For instance, let’s consider a group of users who do specific actions in a particular time frame. Cohort analysis can be used to monitor the duration that various cohorts stay active in the app after performing certain activities. 

In the below example, let’s see how we can utilize both acquisition and behavioral cohorts to find what exactly your customers are doing and the time frame of the activities. 

Acquisition Cohort 

In the above example shows the observations below:

– It clearly reflects that about after the day 1 over, 70% of the users stop using the app 

– After the initial drop rate, the next drop occurs on day 2 which goes up to 15%. 

– At the end of the week, the app levels with over 12% users who are still active on the app. 

Hence the overall analysis states that the app needs more focus on the onboard experience to boost the customer retention rate. 

It is also evident that the acquisition cohort is useful for identifying the status and trends where users are churning.  However, these data are not sufficient to determine why the users are leaving. 

Behavioral cohorts 

In the above example, we had displayed the user segments who decided to proceed with the transaction. 

As part of behavioral cohort analysis, marketers ask targeted or specific questions to users and make well-informed product decisions that will reduce churn

By conducting the behavioral cohrt analysis, marketers can get insights on the following points:

– Which is the ideal time to re-engage with your customers and when is the idle time for Remarketing?

– What is the acquisition rate of new customers to maintain your app conversion rate?

Takeaway

The efficiency of cohort analysis lies in the data, it enables you to make data-driven decisions and determine which users leave and when they leave. Additionally, it can also be used to understand why the users leave, so you will be able to fix it.