People launching a website or app generally have a one-word answer when asked about their target audience.
A sports website would naturally call all sports enthusiasts its target audience.
An ecommerce app selling women footwear would call all women shoppers its target audience.
We can take many examples where this over-simplification is common. Boiling the target audience down to one segment simplifies the overall marketing process. Let’s take the example of the sports website again. It becomes very easy to write content for a sports website when the target audience is the entire world of sports enthusiasts.
Yes, this approach is easy.
Is this approach wise?
No.
Even a moderately trafficked website in a very specific niche can be segmented into various different groups, or cohorts.
The process of segmenting and analysing different cohorts of existing and prospective users is called cohort analysis.
Cohort Analysis – Why should businesses take it seriously?
Cohort analysis basically refers to studying different groups of a given set of audience. There are a myriad of objectives cohort analysis can help a company achieve. Let’s take an example to study the same.
Let’s assume you’re about to launch a new ecommerce app. You built it with a free online app maker to essentially turn WooCommerce to mobile app. You naturally have to prepare a marketing strategy to acquire new users from various channels.
Once you launch the app, you will be able to see which channels are bringing in the most traction. You can thus divide the incoming users into different cohorts based on where they’re coming from. This will help you see which channel is showing the most promise and provide the perfect marketing impetus to your campaign.
This is not where cohort analysis ends. You can use the same cohorts to study which channel provides the most valuable app users. If your goal as an ecommerce app is to maximize sales, targeting cohorts which buy products regularly makes more sense.
The overall theme of cohort analysis is information. It enables companies to see where their customers are coming, user behaviour on an app, their reaction to new features, updates, and so on.
How does cohort analysis help reduce app churn?
By segmenting audiences into different cohorts, companies are also able to study their behaviour on the app. This naturally leads them to find the unique app behavioral attributes of every cohort.
The following steps explain how cohort analysis helps reduce app churn and improve app retention –
Step #1 – Find cohorts where churn is high
The first step is finding the relevant cohorts where app churn is high.
The average churn rate in the app market is 3-5%. To find cohorts with high churn, use mobile app analytic tools to find cohorts that exceed this average churn rate.
For example, let’s say you have a blog discussing the details of various life insurance policies. You turn WordPress to mobile app and create a native app for your insurance blog. Upon close analysis, you find some cohorts have a churn rate of 11%. These cohorts tend to be users coming from social media and those from between ages 18-30.
Thus, cohort analysis enables users to see the exact attributes of users most likely to abandon and uninstall the app.
Step #2 – Study behaviour of cohorts in question
Once you find the cohorts abandoning the app, it’s time to study their behaviour on the app. Going over basic app metrics for such cohorts like session duration, returning frequency, content preferences, and so on is a good start.
The best way to find the reason behind the high churn rate is comparing returning frequency with a new change to the app. This will help you see whether certain users are leaving simply because of a change to your mobile app.
There are of course many possible reasons users uninstall an app. Cohort analysis helps you check the exact attributes of the users leaving. You can then go on to use a range of app metrics to figure out why those users are leaving.
Step #3 – Make changes to your app based on new data
You can make appropriate changes to your app based on the reason why certain app users are leaving. Let’s again go back to the previous example where users between the ages 18-24 were deserting the app you built with a free online app maker.
Since your app basically provides content on life insurance policies, it’s not surprising to see young users uninstalling the app. You can resolve this issue by writing content that discusses why young people should start thinking about life insurance at an early age.
You can also make your content more direct and incisive. These solutions will help reduce your churn rate and improve the overall experience you provide to your app users
In conclusion
Companies today have access to a lot of information about their users. However, a lot of it is worthless in the long run.
Cohort analysis helps companies get access to more suggestive information that can help them improve their app.
This piece provides brief insights into the basics of cohort analysis and a complete guide on how to reduce app churn with the help of the same.