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

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dc.contributor.authorSigg, Stephan
dc.contributor.authorLagerspetz, Eemil
dc.contributor.authorPeltonen, Ella
dc.contributor.authorNurmi, Petteri
dc.contributor.authorTarkoma, Sasu
dc.contributor.funderAcademy of Finland
dc.date.accessioned2024-01-17T14:37:27Z
dc.date.available2024-01-17T14:37:27Z
dc.date.issued2019
dc.description.abstractPopularity 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. © 2019 Association for Computing Machinery.en
dc.description.sponsorshipAcademy of Finland (grants 319017 and 297741)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid13
dc.identifier.citationSigg, S., Lagerspetz, E., Peltonen, E., Nurmi, P. and Tarkoma, S. (2019) 'Exploiting usage to predict instantaneous app popularity: Trend filters and retention rates', ACM Transactions on the Web (TWEB), 13(2), 13(25pp). https://doi.org/10.1145/3199677en
dc.identifier.doi10.1145/3199677
dc.identifier.issn15591131
dc.identifier.issued2
dc.identifier.journaltitleACM Transactions on the Weben
dc.identifier.urihttps://hdl.handle.net/10468/15400
dc.identifier.volume13
dc.language.isoenen
dc.publisherAssociation for Computing Machineryen
dc.rights© 2019, Association for Computing Machinery. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.en
dc.subjectApplication popularityen
dc.subjectMobile analyticsen
dc.subjectTrend miningen
dc.titleExploiting usage to predict instantaneous app popularity: Trend filters and retention ratesen
dc.typeArticle (peer-reviewed)en
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