Predicting replacement of smartphones with mobile app usage

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Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, 10041 LNCS pp. 343 - 351
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© Springer International Publishing AG 2016. To identify right customers who intend to replace the smart phone can help to perform precision marketing and thus bring significant financial gains to cell phone retailers. In this paper,we provide a study of exploiting mobile app usage for predicting users who will change the phone in the future. We first analyze the characteristics of mobile log data and develop the temporal bag-of-apps model,which can transform the raw data to the app usage vectors. We then formularize the prediction problem,present the hazard based prediction model,and derive the inference procedure. Finally,we evaluate both data model and prediction model on real-world data. The experimental results show that the temporal usage data model can effectively capture the unique characteristics of mobile log data,and the hazard based prediction model is thus much more effective than traditional classification methods. Furthermore,the hazard model is explainable,that is,it can easily show how the replacement of smart phones relate to mobile app usage over time.
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