App developers are aware that just downloading an app is the beginning of repeat visits and using the app, which translates to customer retention. Hence for long-term success, the app needs to have must-have high-value propositions well beyond normal vanity metrics. Cohort Analysis uses cohort analysis or behavioural analytics of groups over a specified time to help businesses and their data and analytics-driven decisions in various processes. For Ex: Using a cohort table to figure out if the onboarding user experience is good or not, customers engage with the app daily, monthly etc.
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Cohort analysis meaning analytics of user behaviour, taken from the business data by dividing the users into groups with a common experience/characteristic in a specified time of the process life-cycle, can be very useful for business growth. Cohort analysis definition focuses on the user engagement factor of the growth of an application, website or eCommerce platform, thus separating engagement metrics and growth metrics. For Ex: Many new users on a platform interpreted as growth masks the older users who actually use the app and are far lesser in cohort analysis numbers that really matter.
Let’s use the below cohort analysis example data as an example of the daily cohort table of users launching the app and revisiting it within a time frame of 10 days.
The triangular retention table cohort analysis chart is useful to draw the following inferences like the number of users launching the app on a particular day, the retention rate for the day, the differences in retention rates over a week etc. This table’s benefits are that the valuation metrics are displayed and reveal improvements in how to use cohort analysis with the following.
Thus the cohort analysis successfully represents the retention rates over the products and users lifetime cycle based on the various acquisition and behavioural cohorts over a specified time-frame. Such information is vital to improve the product, customer engagement, retention of customers, tweaking of processes and much more factors that impact revenue generation and efficiency besides the growth of a business.
Cohort analysis and cohorts are representative of user behaviour over a time-scale. A cohort may be a user group with specified actions in a time-specified timeframe like 1-day, 3-days, etc. The cohorts can be divided into 2 groups for behavioural analytics.
Acquisition Cohorts: These cohorts represent user groups based on installing the app for the 1st time. It could be over a month, week or year to give daily, yearly, monthly cohort charts. Tracking this metric helps improve customer-engagement with the app through simplifying glitches reported, making the app user-friendly etc.
Behavioural Cohorts: These cohorts are informative of user behaviours pertaining to the use of the app within a specified time. For Ex: Did they install it, launch it, uninstall it and in what time-frame?
Cohort analysis evaluates the behavioural changes of groups of users who are observed over a specific time. Let’s study the meaning of cohort analysis and how a behavioural cohort, like a group reading reviews before their product purchase. For Ex: An user, after launching or installing the app, makes many decisions exhibiting behavioural changes towards the app, which results in whether they continue on the app or not. Some users may like a core feature A and not use the other features like B or C. Other users may find it does not cater to their needs and dropoff. These behavioural cohorts can be analyzed and used for a variety of purposes in a variety of fields to drive business growth.
Take the example of users who read reviews before purchasing a product. Questions that need to be asked in this cohort analysis are
Thus, one can segregate into user groups and answer questions like the best times to re-engage with the user, market the product, the growth in conversion rate, customer-engagement maintenance, and more. Several questions throw up remedial actions that lead to better customer-retention rates, which drive growth.
Cohort analysis provides answers to behavioral questions over specific time periods that reflect a particular character driving growth. Using such data in a quantitative and systematic approach allows the development of strategies aimed at customer retention, engagement, growth of app and revenue generation.
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