Exploiting Usage to Predict Instantaneous App Popularity

Abstract

Popularity of mobile apps is traditionally measured by metrics such as the number of downloads, installations, or user ratings. A problem with these measures is that they reflect usage only indirectly. Indeed, retention rates, i.e., the number of days users continue to interact with an installed app, have been suggested to predict successful app life-cycles. We conduct the first independent and large-scale study of retention rates and usage trends on a dataset of app-usage data from a community of 339,842 users and more than 213, 667 apps. Our analysis shows that, on average, applications lose 65% of their users in the first week, while very popular applications (top 100) lose only 35%. It also reveals, however, that many applications have more complex usage behaviour patterns due to seasonality, marketing, or other factors. To capture such effects, we develop a novel app-usage trend measure which provides instantaneous information about the popularity of an application. Analysis of our data using this trend filter shows that roughly 40% of all apps never gain more than a handful of users (Marginal apps). Less than 0.1% of the remaining 60% are constantly popular (Dominant apps), 1% have a quick drain of usage after an initial steep rise (Expired apps), and 6% continuously rise in popularity (Hot apps). From these, we can distinguish, for instance, trendsetters from copycat apps. We conclude by demonstrating that usage behaviour trend information can be used to develop better mobile app recommendations.

Mobile App Usage Pattern Prediction Using Hierarchical Flexi-Ensemble Clustering (HFEC) for Mobile Service Rating

“…It does not design to increase the grouping of customer variable selection function. [13] predicted the app's popularity with retention rates and trend filters. Commonly, the popularity of the mobile app measured by installations, downloads and user ratings.…”

A Longitudinal Study of Pervasive Display Personalisation

“…In order to describe engagement of users with mobile applications over longer periods of times, researchers previously utilised 'retention' as a standardised metric defined by the number of days between the first and last use of the mobile application [51]. In the context of Tacita, using 'retention' as a metrics is not suitable due to the nature of the application in which requests and 'engagement' can happen without the requirement to the user to explicitly interact with the application.…”

Tortoise or Hare? Quantifying the Effects of Performance on Mobile App Retention

“…As measure of user behavior we consider n day retention rate, which is the fraction of users continuing to use an app n days since first use. Retention is widely used to measure the success of apps as higher retention corresponds to higher adoption and level of engagement [46]. As source of retention information we use the list of running applications collected by Carat.…”

“…While the total number of apps on the marketplaces is high, a large fraction of them vanish without ever attracting a significant user base, and the majority of the rest struggle to maintain their user base over time. Specifically, studies on mobile app usage suggest that over a quarter of installed apps are only used once [25], and even apps used for more than a day are unlikely to stay relevant longer than a fortnight [46].…”