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Quantitative Indicators of a Successful Mobile Application Quantitative indicators of a successful mobile application PETER SKOGSBERG KTH Information and Communication Technology Degree project in Communication Systems Second level, 30.0 HEC Stockholm, Sweden Quantitative indicators of a successful mobile application Peter Skogsberg [email protected] Master of Science Thesis Examiner: Professor Gerald Q. Maguire Jr. Academic supervisor: Professor Gerald Q. Maguire Jr. Industrial supervisor: Kenneth Andersson, The Mobile Life Communication Systems School of Information and Communication Technology KTH Royal Institute of Technology Stockholm, Sweden Abstract The smartphone industry has grown immensely in recent years. The two leading platforms, Google Android and Apple iOS, each feature marketplaces offering hundreds of thousands of software applications, or apps . The vast selection has facilitated a maturing industry, with new business and revenue models emerging. As an app developer, basic statistics and data for one's apps are available via the marketplace, but also via third-party data sources. This report regards how mobile software is evaluated and rated quantitatively by both end- users and developers, and which metrics are relevant in this context. A selection of freely available third-party data sources and app monitoring tools is discussed, followed by introduction of several relevant statistical methods and data mining techniques. The main object of this thesis project is to investigate whether findings from app statistics can provide understanding in how to design more successful apps, that attract more downloads and/or more revenue. After the theoretical background, a practical implementation is discussed, in the form of an in-house application statistics web platform. This was developed together with the app developer company The Mobile Life, who also provided access to app data for 16 of their published iOS and Android apps. The implementation utilizes automated download and import from online data sources, and provides a web based graphical user interface to display this data using tables and charts. Using mathematical software, a number of statistical methods have been applied to the collected dataset. Analysis findings include different categories (clusters) of apps, the existence of correlations between metrics such as an app’s market ranking and the number of downloads, a long-tailed distribution of keywords used in app reviews, regression analysis models for the distribution of downloads, and an experimental application of Pareto’s 80-20 rule which was found relevant to the gathered dataset. Recommendations to the app company include embedding session tracking libraries such as Google Analytics into future apps. This would allow collection of in-depth metrics such as session length and user retention, which would enable more interesting pattern discovery. Keywords : mobile, smartphone, application, app, Android, iOS, statistics, data, metrics, quantitative, measure, downloads, rating, Pareto, successful, developer, publisher, data mining, R, ETL, API i Sammanfattning Smartphonebranschen har växt kraftigt de senaste åren. De två ledande operativsystemen, Google Android och Apple iOS, har vardera distributionskanaler som erbjuder hundratusentals mjukvaruapplikationer, eller appar . Det breda utbudet har bidragit till en mognande bransch, med nya växande affärs- och intäktsmodeller. Som apputvecklare finns grundläggande statistik och data för ens egna appar att tillgå via distributionskanalerna, men även via datakällor från tredje part. Den här rapporten behandlar hur mobil mjukvara utvärderas och bedöms kvantitativt av båda slutanvändare och utvecklare, samt vilka data och mått som är relevanta i sammanhanget. Ett urval av fritt tillgängliga tredjeparts datakällor och bevakningsverktyg presenteras, följt av en översikt av flertalet relevanta statistiska metoder och data mining-tekniker. Huvudsyftet med detta examensarbete är att utreda om fynd utifrån appstatistik kan ge förståelse för hur man utvecklar och utformar mer framgångsrika appar, som uppnår fler nedladdningar och/eller större intäkter. Efter den teoretiska bakgrunden diskuteras en konkret implementation, i form av en intern webplattform för appstatistik. Denna plattform utvecklades i samarbete med apputvecklaren The Mobile Life, som också bistod med tillgång till appdata för 16 av deras publicerade iOS- och Android-appar. Implementationen nyttjar automatiserad nedladdning och import av data från datakällor online, samt utgör ett grafiskt gränssnitt för att åskådliggöra datan med bland annat tabeller och grafer. Med hjälp av matematisk mjukvara har ett antal statistiska metoder tillämpats på det insamlade dataurvalet. Analysens omfattning inkluderar en kategorisering (klustring) av appar, existensen av en korrelation mellan mätvärden såsom appars ranking och dess antal nedladdningar, analys av vanligt förekommande ord ur apprecensioner, en regressionsanalysmodell för distributionen av nedladdningar samt en experimentell applicering av Paretos ”80-20”-regel som fanns lämplig för vår data. Rekommendationer till appföretaget inkluderar att bädda in bibliotek för appsessionsspårning, såsom Google Analytics, i dess framtida appar. Detta skulle möjliggöra insamling av mer detaljerad data såsom att mäta sessionslängd och användarlojalitet, vilket skulle möjliggöra mer intressanta analyser. Nyckelord : mobil, smartphone, applikation, app, Android, iOS, statistik, data, mätvärden, kvantitativ, mätning, nedladdningar, betyg, Pareto, framgångsrik, utvecklare, utgivare, data mining, R, ETL, API iii Acknowledgements Many thanks go to my academic supervisor and examiner at KTH Royal Institute of Technology, Professor Gerald Q. Maguire Jr., who has been very helpful with extensive feedback throughout the writing process. My academic opponent, Sarwarul Islam Rizvi, also contributed with helpful report feedback. Thanks also go to my industrial supervisor Kenneth Andersson at the company The Mobile Life [129], whose ideas for implementation and functionality of the web platform has been highly useful. Special thanks go to Professor Michael D. Smith, co-author of [30], and Jan Katrenic from [122], for taking the time to correspond with me via email to explain details of their respective works. Finally, I wish to thank Remco van den Elzen at Distimo [126] and Christian Poppelreiter at Flurry [125] for their kind permission to use their graphs and illustrations in this thesis. v Table of Contents Abstract........................................................................................................................................i Sammanfattning........................................................................................................................ iii Acknowledgements ....................................................................................................................v Table of Contents......................................................................................................................vii List of Figures............................................................................................................................xi List of Tables.......................................................................................................................... xiii List of abbreviations .................................................................................................................xv 1 Introduction ........................................................................................................................1 2 Background.........................................................................................................................3 3 Smartphone platforms.........................................................................................................5 3.1 Google Android ...........................................................................................................5 3.1.1 Devices .................................................................................................................5 3.1.2 Marketplace – Google Play ..................................................................................6 3.2 Apple iOS.....................................................................................................................6 3.2.1 Devices .................................................................................................................6 3.2.2 Marketplace – App Store......................................................................................6 3.3 Mobile web ..................................................................................................................7 3.3.1 jQuery Mobile.......................................................................................................8 3.3.2 PhoneGap..............................................................................................................8 3.4 Others...........................................................................................................................8 3.4.1 Windows Phone....................................................................................................8 3.4.2 BlackBerry............................................................................................................9 4 Demographics...................................................................................................................11 5 Business models ...............................................................................................................13
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