Let’s start off with the basics, “cohort” refers to a specific group of people who all share the same characteristics, such as belonging to one geographical area. So, how would a cohort group help an app developer? Well, it is not about the group but the analysis and how you make use of the information provided. In short, it’s the cohort analysis that will prove helpful to you as an app developer.
Worry not if you have no prior knowledge of the topic, as this article is a complete beginner’s guide with common questions like “what does cohort mean?”
What is Cohort Analysis?
Cohort analysis refers to the user behavior analytics in specific groups to help you track and understand their preferences. As a result, you get to direct specific surveys towards them and make improved decisions to help increase your revenue.
Moreover, this data will help you eliminate irrelevant leads and save time by not pursuing them in the first place. It will also help you target the areas where you are certain that the chances of making a profit are higher. Additionally, you also get to understand why some users have stopped using your app, also known as the churn rate. Therefore, it is a fantastic way to increase user retention rates, as it helps you understand the following:
- What kind of users are and aren’t engaging with your app?
- At what point do users lose interest in your app?
- What makes the users lose interest?
Types of Cohorts
If you want to know the answers to the questions mentioned above, you should familiarize yourself with the types of cohorts.
Acquisition cohorts refer to the group of people who signed up for your app at a particular time and those who became churn. Let’s consider this cohort example- you have just launched an app and notice a churn problem soon after. Therefore, you can rely on Acquisition cohorts to tell you precisely when the users start losing interest. It helps you figure out the following:
- The number of new users that have started using your application in each time frame.
- The amount of time since a user has signed up to your app.
This data will help you understand why users are leaving your app. For instance, they might have stopped using it after the first two days, indicating that the layout is too confusing to understand.
This type of cohort refers to the group of users divided according to their behavior towards your application. From the moment a user downloads your app, the way they navigate through it will determine whether they keep or delete it. As opposed to acquisition cohorts, behavioral cohorts display analytics according to particular actions taken by users. This data will help you:
- Determine which features increase the user retention rate.
- The number of users who have enabled push notifications and why.
Behavioral cohorts may not give you direct answers but will provide you with the data to connect users’ behavior with the retention rate. For instance, relating the two becomes easier when you know which users preferred specific features and which features failed to garner their attention.
Thus, you can also use this data to determine which features are worthy of being classified as “premium.”
Things to Consider When Performing a Cohort Study
Now, let’s talk about the right way to conduct a cohort study.
Performing a cohort analysis by yourself is not an easy task, and it is recommended to take the help of an analytics tool. Nonetheless, you should keep the following in mind:
Determine the Point When Users Churn
With the help of acquisition cohorts, you can determine when the users tend to stop using the application. The easiest way to make use of it is through the use of charts.
- Columns represent the amount of time since the users have joined the app.
- Rows represent the number of new users acquired at every time interval.
- Each cell indicates the original number of users acquired and retained in a particular period.
Keep the following in mind during an acquisition cohort analysis:
- Amount of time since the acquisition: Newer companies should use shorter periods, and established companies should focus on long-term growth.
- Scope: If your business uses a broader scope, it will be tougher to form an accurate report on why you cannot retain users. Therefore, it is better to stay within 90 days for accurate retention results.
- Churn rate: All applications will have varying churn rates; however, it depends on the type of application you have. For instance, many apps have a high churn rate of 10-15%, which you would want to decrease to 2-3%. Remember that a 0% churn rate is not possible, so keep your expectations real.
Let’s say that most of your users drop out after the first week of using the app. So, you must determine what changes. For instance, it may be that you’re offering a free trial for the first week. Many users may feel that the premium version is not worth paying for. As a solution, you may have to shift a few premium features to the basic plan.
Perform Behavioral Cohort Analysis
As mentioned above, finding the link between user retention and behavior pattern is not very straightforward. You have to take a deep dive into the analytical data to form user behavior and engagement combinations. The goal is to understand what’s common between the users you have retained and those who left the app.
Implementing Cohort Analysis Into A Business
To profit from applications, you must evaluate cohorts – users who have shared a similar experience over some time – or study the behavior of a single cohort to find a pattern that supports a growth hypothesis. Anything could be the basis for that study.
For instance, you could determine that consumers gained through display ads have a higher Customer Lifetime Value than those acquired through Instagram. The cohort analysis might then be used to prove the hypothesis.
As someone who isn’t an expert in data analysis, it’s best to find a reliable tool that provides accurate information. One fantastic tool is Appflow.ai, to help you out with cohort analysis by providing precise data that would otherwise take too long to collect. It is fun and easy to use while also reducing excessive hard work on your part. Thus, you can make better and faster decisions to improve your user retention.