For many years, the industry standard for measuring mobile app user engagement has been some combination of Daily Active Users (DAU), Monthly Active Users (MAU) and Time Spent. Analyzing how often users are opening an app and how long they’re using it can paint a broad picture of how engaged an app’s user base is. However, there are limitations to only examining engagement metrics that are derived as an average of an entire app’s user base. Evaluating DAUs and MAUs, for example, groups the entire user base of an app into one cohort to give a simple average and doesn’t show the nuance between cohorts of app users, particularly how well an app is generating and retaining power users.
Power users are the most valuable customers of an app. They’re engaging the most often, have the highest lifetime value (LTV), know the most about your app, and are the most likely to refer other users to it. Growing and retaining a healthy base of power users is critical to an app’s long-term success. How can mobile companies hone in on this incredibly valuable cohort of users and determine how well an app is retaining and growing this segment?
Enter the Power User Curve, the latest feature in Sensor Tower’s suite of Consumer Intelligence tools.
The Power User Curve is a visual representation of an app’s user engagement by the total number of days its users are active in a given month. Power User Curves are particularly helpful for showing if an app is resonating with a highly engaged group of users and if the app is generating more power users over time. There are typically three types of Power User Curves:
Left-Leaning Curve: This curve indicates that an app has very few, if any, power users. This curve is common for apps in the Travel or Banking categories, for example, where daily usage shouldn’t be expected.
Right-Leaning Curve: This indicates there are a large number of power users who are engaging with an app for at least half of the days in the month.
Smile Curve: This curve has a higher concentration on the ends and lower in the middle. This indicates a high concentration of single-day users and a high concentration of daily users.
Examining the Power User Curves of apps in the same category can help contextualize which apps in the same space are the most sticky. In the chart below, we’re comparing the Power User Curves of two language-learning apps: Duolingo and Babbel.
As you can see, both of these apps have a high percentage of users who only log in one day per month, but Duolingo sees a slight uptick at the 30- to 31-day mark, meaning it has a larger group of power users who are engaging with the app every single day.
Including the Power User Curve as part of the overall assessment of an app’s engagement has a number of advantages:
The Power User Curve shows a level of nuance between cohorts of an app’s users. If engagement among the power user cohort is increasing over time, this can be a positive indicator of an app’s health, even if the DAU to MAU ratio may be decreasing or flat.
Examining the Power User Curve of an app over time can show if engagement is increasing among power users, which can help assess the performance of product updates and other in-app changes.
Power users are the most valuable segment of an app’s user base and understanding how an app is retaining and growing the cohort over time is an important metric for examining app health. If you’re interested in learning more about the Power User Curve and how it can be used to benchmark an app’s engagement and performance, reach out to firstname.lastname@example.org.