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Predicting purchase decisions in mobile free-to-play games

: Sifa, Rafet; Hadiji, Fabian; Runge, Julian; Drachen, Anders; Kersting, Kristian; Bauckhage, Christian

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Jhala, A. ; Association for the Advancement of Artificial Intelligence -AAAI-:
Eleventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2015. Proceedings : 14-18 November 2015, University of California, Santa Cruz, CA, USA
Menlo Park: AAAI Press, 2015
ISBN: 978-1-57735-740-7
Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) <11, 2015, Santa Cruz/Calif.>
Conference Paper, Electronic Publication
Fraunhofer IAIS ()
behavioral analytics; behavioral profiling; statistical data mining; forecasting player behavior

Mobile digital games are dominantly released under the freemium business model, but only a small fraction of the players makes any purchases. The ability to predict who will make a purchase enables optimization of marketing efforts, and tailoring customer relationship management to the specific user's profile. Here this challenge is addressed via two models for predicting purchasing players, using a 100,000 player dataset: 1) A classification model focused on predicting whether a purchase will occur or not. 2) a regression model focused on predicting the number of purchases a user will make. Both models are presented within a decision and regression tree framework for building rules that are actionable by companies. To the best of our knowledge, this is the first study investigating purchase decisions in freemium mobile products from a user behavior perspective and adopting behavior-driven learning approaches to this problem.