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

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorSigg, Stephan
dc.contributor.authorLagerspetz, Eemil
dc.contributor.authorPeltonen, Ella
dc.contributor.authorNurmi, Petteri
dc.contributor.authorTarkoma, Sasu
dc.contributor.departmentDepartment of Communications and Networking
dc.contributor.departmentUniversity of Helsinki
dc.contributor.departmentUniversity College Cork
dc.date.accessioned2019-06-03T14:12:26Z
dc.date.available2019-06-03T14:12:26Z
dc.date.issued2019-04-01
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.en
dc.description.versionPeer revieweden
dc.format.mimetypeapplication/pdf
dc.identifier.citationSigg , 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/10.1145/3199677en
dc.identifier.doi10.1145/3199677
dc.identifier.issn1559-1131
dc.identifier.otherPURE UUID: 504a6a75-39ae-4ad4-a24c-1b6339075f7e
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/504a6a75-39ae-4ad4-a24c-1b6339075f7e
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85065784897&partnerID=8YFLogxK
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/32507992/ELEC_Sigg_exploiting_usage_to_predict_instantaneous_app_ACM.pdf
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/38255
dc.identifier.urnURN:NBN:fi:aalto-201906033340
dc.language.isoenen
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.ispartofseriesACM TRANSACTIONS ON THE WEBen
dc.relation.ispartofseriesVolume 13, issue 2en
dc.rightsopenAccessen
dc.subject.keywordApplication popularity
dc.subject.keywordMobile analytics
dc.subject.keywordTrend mining
dc.titleExploiting usage to predict instantaneous app popularity: Trend filters and retention ratesen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
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