In today’s digital world, data analysis is a must for all business development. Mobile apps are not exceptional. Though data are not difficult to get nowadays, gaining actionable insights from data analysis is never easy, especially when there are not enough metrics for cross-validation. That’s why app marketers should learn app cohort analysis to better utilize the data and turn them into actionable insights.
What is App Cohort Analysis?
App cohort analysis is a kind of user behavioral analytics that breaks app users in a data set into related groups before jumping into the analysis. These groups are called cohorts and usually share common characteristics or experiences in an app within a defined time span. To put it plainly, app cohort analysis divides its users into different groups based on their similarities or behaviors inside the app over a certain period of time. In this way, app marketers can visualize and compare users' journeys of different groups inside the app in a clearer way. Accordingly, they can take targeting measures for different cohorts to prolong their LTV by preventing users from leaving at critical moments.
5 Types of Cohort Analysis for Apps
There can be tremendous types of cohort analysis depending on what significant issues are to be analyzed in different apps. For apps, there are at least five major types of cohort analysis to be considered:
- Acquisition Cohorts: Acquisition cohorts divide users into different groups by their acquisition channel. Check the subscription rate and revenue of different cohorts so as to find out what the most effective acquisition channels are.
- Behavioral Cohorts: Divide users by the actions they have taken or not taken in the app within a time frame. There can be lots of behaviors in the app, such as installation, subscriptions, unsubscriptions, etc.
- Segment-Based Cohorts: Doing cohorts based on user segments allows app marketers to classify cohorts flexibly, which helps identify which segment of users brings the largest revenue. Find out the crucial moment when a group of users tends to leave and take methods to prevent that.
- Demographic Cohorts: Divide users by their age or location and analyze the critical metric that is most important for app growth. It helps app marketers to better know where their most potential marketplace is and what kind of users to target.
- Technographic Cohorts: Technographic cohorts mostly divide users by the devices or platforms they are using to access the apps. Studying revenue from these different devices may show some technical issues that are keeping users from paying.
5 Benefits of Cohort Analysis
Looking at a single metric sometimes does not provide any sign of abnormality going on in the app. For example, the downloads number is increasing, but actually, the conversion rate goes down. At this moment, you may need cohort analysis to gain actionable insights and find ways to market apps. Here are five benefits of cohort:
- Identify the most potential marketing channels
Studying cohorts based on their acquisition channels helps get a good understanding of the effectiveness of different marketing channels. It tells which channel has the highest conversion rate, therefore helping the app marketing team make decisions on which channels they are going to spend the budget on.
- Improve app user conversion rate
App marketers may encourage users to do specific actions more efficiently when they identify with cohort analysis that some behavior patterns in apps can lead to more conversion.
- Clarify different life-cycle of app users
Cohort analysis allows a company to "see patterns clearly across the life-cycle of a customer, rather than slicing across all customers blindly without accounting for the natural cycle that a customer undergoes." By seeing these patterns of time, a company can adapt and tailor its service to those specific cohorts. For example, sending notifications to the different cohorts according to their time zone can increase the app open rate.
- Recognize the causes of app user churn
Doing cohort analysis based on their behaviors in the app can help identify why users leave. For example, users who had used the main feature of the app tend to subscribe and become paid users. The hypothesis can be that those who leave apps do not even know what the app can do. So app marketers should consider guiding users to use the feature via email or in-app messages.
- Improve lifetime value of an app users
Cohorts can help filter app users who have a longer lifetime value. Investigate the difference between cohorts with various lifetime values and find out the element that leads to higher LTV. Then apply it to other cohorts, which are likely to improve their LTV.
How to Perform Cohort Analysis?
There are two different ways to perform cohort analysis: one manually and the other automatically.
Perform Cohort Analysis in a manual way:
Step 1: Decide on what issues to handle. The purpose of the analysis is to get an insight into the user data and come up with an idea on how to solve the problem. So take a look at the app and figure out what issues to handle. For example, thinking about the reason why the revenue is lost by merely staring at the revenue number does not help. Then consider it an issue to handle.
Step 2: Find out the metrics that can help handle the issue. A proper cohort analysis requires the identification of an event, such as a user checking out, and specific properties, like how much the user paid. In the above example of lost revenue, the paying user who stops paying should be considered as an event to study on.
Step 3: Identify different cohorts. Analyze users' behaviors in the app to discover the crucial differences between users. Then list the differences, which served as cohort variables to separate the users into different groups.
Step 4: Perform cohort analysis. Use data visualization to do a cohort analysis to find out the real issue from multiple cohorts. For example, a cohort of users don’t unsubscribe from the service but they are not paying, whose reason can be there are some problems with their paying method.
Step 5: Verify the hypothesis. Insight acquired from the data can be mistaken sometimes, so it is necessary to verify the result. For example, look at a specific app user account to check the subscription status.
Perform Cohort Analysis in an automatic way:
Performing cohort analysis in an automatic way involves the help of a data analytics tool that has a feature of cohort analysis. Different analysis tools have different metrics for cohort analysis. For example, appflow.ai offer cohort metrics, such as installs, subscriptions, countries, segments, acquisition channels, etc. Doing cohort analysis is simple with such a tool. Just choose the metric and what you want to compare, then the table with highlights will be presented where you can directly study on.
Cohort analysis is vital for app marketing and growth. On the one hand, it provides insight into the data, which would not be available by just looking at a single metric of data. On the other hand, it helps make data-based decisions on user acquisition, user engagement, user conversion, user retention, marketing and monetization strategies.
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