Detection of Churned and Retained Users with Machine Learning Methods for Mobile Applications
This study aims to find the different behavior patterns of churned and retained mobile application users using machine learning approach. The data for this study is gathered from the users of a mobile application (iPhone & Android). As a machine learning classifier Support Vector Machines (SVM) are used for evaluating in the detection of churned and retained users. Several features are extracted from user data to discriminate different user behaviors. Successful results are obtained and user behaviors are classified with 93% and 98% accuracy. From the diversity perspective, results of this study can be used to evaluate the differences of churned and retained users in terms of diverse user groups.
KeywordsMachine learning SVM mobile applications churned and retained users diversity applications classification mobile devices push notification user experience
Unable to display preview. Download preview PDF.
- 2.Android’s Google Play Beats App Store, http://www.phonearena.com/news/Androids-Google-Play-beats-App-Store-with-over-1-million-on-apps-now-officially-largest_id45680
- 3.Microsoft by the Numbers, http://www.microsoft.com/en-us/news/bythenumbers/index.html
- 4.Gençer, M., Bilgin, G., Zan, Ö., Voyvodaoğlu, T.: A New Framework for Increasing User Engagement in Mobile Applications Using Machine Learning Techniques. In: Marcus, A. (ed.) DUXU/HCII 2013, Part IV. LNCS, vol. 8015, pp. 651–659. Springer, Heidelberg (2013)Google Scholar
- 6.Au, T., Guangqin, M., Shaomin, L.: Applying And Evaluating Models To Predict Customer Attrition Using Data Mining Techniques. Journal of Comparative International Management 6(1) (2003)Google Scholar
- 9.Coussement, K., Van den Poel, D.:Improving Customer Attrition Prediction By Integrating Emotions From Client/Company Interaction Emails And Evaluating Multiple Classifiers. Expert Systems with Applications, vol. 36(3(Pt. 2)), Pt. 2, pp. 6127-6134 (2009)Google Scholar
- 10.Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2007)Google Scholar
- 12.Wu, R.-C., Chen, R.-S., Chen, C.-C.C.J.Y.: Data Mining Application In Customer Relationship Management of Credit Card Business. In: IEEE Computer Software and Applications Conference, vol. 2 (2005)Google Scholar
- 13.Hoggart, C.J., Griffin, J.E.: A Bayesian Partition Model For Customer Attrition. In: Bayesian Methods with Applications to Science, Policy and Official Statistics (Selected Papers from The Sixth World Meeting of the International Society for Bayesian Analysis), pp. 223–232 (2001)Google Scholar
- 14.Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press (2002)Google Scholar
- 15.Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons (2001)Google Scholar