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

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Sigg, Stephan
dc.contributor.author Lagerspetz, Eemil
dc.contributor.author Peltonen, Ella
dc.contributor.author Nurmi, Petteri
dc.contributor.author Tarkoma, Sasu
dc.date.accessioned 2019-06-03T14:12:26Z
dc.date.available 2019-06-03T14:12:26Z
dc.date.issued 2019-04-01
dc.identifier.citation Sigg , S , Lagerspetz , E , Peltonen , E , Nurmi , P & Tarkoma , S 2019 , ' Exploiting usage to predict instantaneous app popularity: Trend filters and retention rates ' ACM TRANSACTIONS ON THE WEB , vol. 13 , no. 2 , 13 . https://doi.org/10.1145/3199677 en
dc.identifier.issn 1559-1131
dc.identifier.other PURE UUID: 504a6a75-39ae-4ad4-a24c-1b6339075f7e
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/exploiting-usage-to-predict-instantaneous-app-popularity-trend-filters-and-retention-rates(504a6a75-39ae-4ad4-a24c-1b6339075f7e).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85065784897&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/32507992/ELEC_Sigg_exploiting_usage_to_predict_instantaneous_app_ACM.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/38255
dc.description.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 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. en
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Association for Computing Machinery (ACM)
dc.relation.ispartofseries ACM TRANSACTIONS ON THE WEB en
dc.relation.ispartofseries Volume 13, issue 2 en
dc.rights openAccess en
dc.subject.other Computer Networks and Communications en
dc.subject.other 113 Computer and information sciences en
dc.subject.other 213 Electronic, automation and communications engineering, electronics en
dc.title Exploiting usage to predict instantaneous app popularity: Trend filters and retention rates en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Communications and Networking
dc.contributor.department University of Helsinki
dc.contributor.department University College Cork
dc.subject.keyword Application popularity
dc.subject.keyword Mobile analytics
dc.subject.keyword Trend mining
dc.subject.keyword Computer Networks and Communications
dc.subject.keyword 113 Computer and information sciences
dc.subject.keyword 213 Electronic, automation and communications engineering, electronics
dc.identifier.urn URN:NBN:fi:aalto-201906033340
dc.identifier.doi 10.1145/3199677
dc.type.version acceptedVersion

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