Stylized facts for mobile game analytics

There are numerous widely disseminated beliefs in the rapidly growing domain of Mobile Game Analytics, notably within the context of the Free-to-Play model. However, the field remains in its infancy, as there is limited conclusive empirical knowledge available across industry and academia, to provide evidence for these beliefs. Additionally, the current knowledge base is highly fragmented. For Mobile Game Analytics to mature, empirical frameworks are needed. In this paper the concept of stylized facts is presented as a means to develop an initial framework for a common understanding of key hypotheses and concepts in the field, as well as organizing the available empirical knowledge. A focus on stylized facts research will not only facilitate communication but also, more importantly, improve the quality and actionability of insights. Unified terminology and a comprehensive collection of stylized facts can be the building blocks for a conceptually well-founded understanding of mobile gaming.

[1]  Lesley Chiou,et al.  Paywalls and the demand for news , 2013, Inf. Econ. Policy.

[2]  Julian Togelius,et al.  Game Data Mining , 2013, Game Analytics, Maximizing the Value of Player Data.

[3]  P. Geroski.  What do we know about entry , 1995 .

[4]  Christian Bauckhage,et al.  k-Maxoids Clustering , 2015, LWA.

[5]  Alain Pinsonneault,et al.  For a Fee: The Impact of Information Pricing Strategy on the Pattern and Effectiveness of Word-of-Mouth via Social Media , 2013, ICIS.

[6]  Christian Bauckhage,et al.  Clustering Game Behavior Data , 2015, IEEE Transactions on Computational Intelligence and AI in Games.

[7]  Christian Bauckhage,et al.  Archetypal Game Recommender Systems , 2014, LWA.

[8]  Christian Bauckhage,et al.  Predicting player churn in the wild , 2014, 2014 IEEE Conference on Computational Intelligence and Games.

[9]  E. Prescott,et al.  Chapter 14 The equity premium in retrospect , 2003 .

[10]  Noor Shaker,et al.  Alone We Can Do So Little, Together We Can Do So Much: A Combinatorial Approach for Generating Game Content , 2014, AIIDE.

[11]  Tim Fields,et al.  Social Game Design: Monetization Methods and Mechanics , 2011 .

[12]  Erkki Sutinen,et al.  Editorial: E-learning and Human-Computer Interaction: Exploring Design Synergies for more Effective Learning Experiences , 2006, J. Educ. Technol. Soc..

[13]  Nachiappan Nagappan,et al.  What Drives People: Creating Engagement Profiles of Players from Game Log Data , 2015, CHI PLAY.

[14]  Christian Bauckhage,et al.  Behavior evolution in Tomb Raider Underworld , 2013, 2013 IEEE Conference on Computational Inteligence in Games (CIG).

[15]  D. C. Hague,et al.  The theory of capital : proceedings of a conference held by the International Economic Association , 1961 .

[16]  R. Cont.  Empirical properties of asset returns: stylized facts and statistical issues , 2001 .

[17]  Boi Faltings,et al.  Hidden Markov models for churn prediction , 2015, 2015 SAI Intelligent Systems Conference (IntelliSys).

[18]  Lea Skorin-Kapov,et al.  The impact of user, system, and context factors on gaming QoE: A case study involving MMORPGs , 2013, 2013 12th Annual Workshop on Network and Systems Support for Games (NetGames).

[19]  Christian Bauckhage,et al.  Predicting Purchase Decisions in Mobile Free-to-Play Games , 2015, AIIDE.

[20]  Peter J. Klenow,et al.  Economic growth: A review essay , 1997 .

[21]  Georgios N. Yannakakis.  Game AI revisited , 2012, CF '12.

[22]  George M. Constantinides,et al.  Handbook of the Economics of Finance , 2013 .

[23]  C. I. Jones,et al.  The New Kaldor Facts: Ideas, Institutions, Population, and Human Capital , 2009 .

[24]  Christian Bauckhage,et al.  The Playtime Principle: Large-scale cross-games interest modeling , 2014, 2014 IEEE Conference on Computational Intelligence and Games.

[25]  John Bohannon,et al.  IEEE International Conference on Computational Intelligence and Games. Game-miners grapple with massive data. , 2010, Science.

[26]  D. Coyle.  GDP: A Brief but Affectionate History , 2014 .

[27]  Christian Bauckhage,et al.  User Churn Migration Analysis with DEDICOM , 2015, RecSys.

[28]  Georgios N. Yannakakis,et al.  Player modeling using self-organization in Tomb Raider: Underworld , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

[29]  Dilip Soman,et al.  Effects of Payment Mechanism on Spending Behavior: The Role of Rehearsal and Immediacy of Payments , 2001 .

[30]  Albert Ali Salah,et al.  Automatic Classification of Player Complaints in Social Games , 2017, IEEE Transactions on Computational Intelligence and AI in Games.

[31]  E. Prescott,et al.  The Equity Premium in Retrospect , 2003 .

[32]  Sam Devlin,et al.  Predicting player disengagement and first purchase with event-frequency based data representation , 2015, 2015 IEEE Conference on Computational Intelligence and Games (CIG).

[33]  John T. Gourville,et al.  The potential downside of bundling: how packaging services can hurt consumption. , 2001 .

[34]  Julian Togelius,et al.  A Panorama of Artificial and Computational Intelligence in Games , 2015, IEEE Transactions on Computational Intelligence and AI in Games.

[35]  Peng Gao,et al.  Churn prediction for high-value players in casual social games , 2014, 2014 IEEE Conference on Computational Intelligence and Games.

[36]  N. Kaldor.  Capital Accumulation and Economic Growth , 1961 .

[37]  Debanjan Saha,et al.  A long-term study of a popular MMORPG , 2007, NetGames '07.

[38]  Robert J. Moore,et al.  "Alone together?": exploring the social dynamics of massively multiplayer online games , 2006, CHI.

[39]  Sam Devlin,et al.  Player Preference and Style in a Leading Mobile Card Game , 2015, IEEE Transactions on Computational Intelligence and AI in Games.

[40]  John T. Gourville,et al.  Transaction Decoupling: How Price Bundling Affects the Decision to Consume , 2001 .

[41]  Christian Bauckhage,et al.  Guns, swords and data: Clustering of player behavior in computer games in the wild , 2012, 2012 IEEE Conference on Computational Intelligence and Games (CIG).

[42]  R. Thaler,et al.  Invest Now, Drink Later, Spend Never: The Mental Accounting of Delayed Consumption , 2006 .

[43]  Kenneth S Bordens,et al.  Research design and methods : a process approach , 2004 .

[44]  John T. Gourville,et al.  How Packaging Services Can Hurt Consumption , 2001 .

[45]  Guenter Wallner.  Sequential Analysis of Player Behavior , 2015, CHI PLAY.

[46]  F. Selto,et al.  Inference from Empirical Research , 2005 .

[47]  Julian Togelius,et al.  Experience-Driven Procedural Content Generation , 2011, IEEE Transactions on Affective Computing.