Exploiting usage to predict instantaneous app popularity:Trend filters and retention rates

Sigg, Stephan and Lagerspetz, Eemil and Peltonen, Ella and Nurmi, Petteri Tapio and Tarkoma, Sasu (2019) Exploiting usage to predict instantaneous app popularity:Trend filters and retention rates. ACM Transactions on the Web, 13 (2). ISSN 1559-114X

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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 lifecycles. 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.

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Journal Article
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ACM Transactions on the Web
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10 Jul 2019 15:35
Last Modified:
18 Sep 2023 01:30