So your task is to "monitor user engagement" in your mobile app. But wait, what really defines good user engagement? "Engagement" can be interpreted in a number of ways and can be applied in different ways in different mobile app categories. As Intercom elaborates, "For to-do apps, participating users should log in daily to add and complete items, while for invoicing apps, participating users may only log in once a month. There is no consistent level of engagement across products. quantifiable definition of While there is no quantifiable definition of engagement across different verticals, there are some basic metrics that mobile professionals tend to use. Using any standard analytics tool, you might start by tracking metrics such as daily application launches, churn rates, and daily sessions per DAU. These are all important metrics, but you can do more to measure user engagement. There is one tool that can give you a deeper understanding of user engagement: Action Cohort Analysis . Action group analysis: what is it? "Group" is a term used to describe a group of people who are united by the same attributes. For example, people born between 1980 and 1990 who attended private universities are a cohort. For mobile app cohorts, a cohort is a group of users who performed one or more common actions within a specific time frame . In app analytics, action group analytics allows you to create groups of users based on how users behave in your app over a specific time frame (whether it's a day, a week, a month, etc.) .
Action group analysis can help you understand how users are actually interacting with your app and examine behavioral trends over time. By enabling you to treat your users as specific, defined groups rather than individual aggregated units, you can better identify engagement and usage patterns across the user lifecycle. These patterns can help you find the data that really matters and allow you to make more informed decisions about the optimization of your application. To help you better understand how cohort analysis works, let's first review the actual setup industry mailing list this tool. When creating an action group, you need to set three parameters. Now that you've defined the time frame, first action, and partial actions, it's time to get started. If you have defined the time range as the current month or current week, you will need to wait for the behavioral analysis tool to populate with enough data. Remember, in cohort analysis, a user is counted only once per cohort, but can be included in multiple cohorts. For example, let's say you have a weekly group based on completing the first action of a "login" event. This means that users who are actively logged in at least once a week will appear in every group, not just the group they originally logged in to. Take a look at the following visualization of action groups based on weekly time periods. Notice how the same person is not repeated in the same group, but appears in multiple groups based on the action you originally selected.
If you've defined the initial action as "start first session", you won't see that person repeat in the group because that action can only happen once. Action Queue Analysis 4 Pro Tip: Cohort Analysis + User Session Recording Action group analysis is a great way for you to ask specific questions about user behavior and/or usage trends, and then examine the relevant data. However, in order to confidently act on these data, you need to try to understand why you are seeing these numbers. A key question to ask here is why some of your users didn't complete the second action (trust us, at least "some"). Many assumptions and "educated guesses" may be involved. Today's users are complex, and applying a reason for incomplete operations can be challenging. There is an effective workaround that can save you time, resources and stress. It comes in the form of a user session record. Mobile application analytics tools that provide user session recordings, such as Appsee, enable mobile professionals to view session recordings of users in specific operational groups. For example, let's say you set up an action group with the initial defined action "Started First Session" and the second action "Triggered Event: PurchaseComplete". Using qualitative analysis tools, you can watch session recordings of users who did not complete the second action within a specific time frame. Maybe some users trying to pay via Paypal crashed? Maybe there are confusing text elements in the billing and shipping address sections? The answers to these questions can be visualized by pairing session recordings with cohort analysis. Action Queue Analysis 5 How does this combination apply to other app categories?
Let's take a look at some powerful use cases. Business/ProductivityAction Group Analysis Use Case 1 So you have a business application that enables users to manage their tasks. A powerful action group for your app might be tracking how many users started a session, then went back and clicked the "+" button to add a new task. This group will help you check if users are adding new tasks to their task list as frequently as you expect. With user session records, you can also see which friction points might prevent them from adding tasks.