Predicting and improving player retention is crucial to the success of mobile Free-to-Play games. This paper explores the problem of rapid retention prediction in this context. Heuristic modeling approaches are introduced as a way of building simple rules for predicting short-term retention. Compared to common classification algorithms, our heuristic-based approach achieves reasonable and comparable performance using information from the first session, day, and week of player activity.

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In this article I'm going to be talking about Tomb Raider. Now why would I take a look at Underworld, ten years after its original release? Well, hidden behind this game is the story of one of the first major efforts at analysing player performance in a AAA video game -- a feat achieved through large scale data collection and a little bit of help from artificial intelligence. All in an effort to answer the question: do players actually play the games we make in the way we expect them to? Nowadays it's incredibly common for games to adopt some form of analytics: a process whereby data is collected on how well the game performs as well as how players behave within it.

The in-game economies of massively multi-player online games (MMOGs) are complex systems that have to be carefully designed and managed. This paper presents the results of an analysis of auction house data from the MMOG Glitch, across a 14 month time period, the entire lifetime of the game. The data comprise almost 3 million data points, over 20,000 unique players and more than 650 products. Furthermore, an interactive visualization, based on Sankey flow diagrams, is presented which shows the proportion of the different clusters across each time bin, as well as the flow of players between clusters. The diagram allows evaluation of migration of players between clusters as a function of time, as well as churn analysis. The presented work provides a template analysis and visualization model for progression-based or temporal-based analysis of player behavior broadly applicable to games. Keywords: virtual economy, massively multi-player online game, game analytics, auction house, longitudinal analysis 1. Introduction Online games form a major component of the games industry, and have expanded strongly in terms of market share, variety and market penetration in recent years, notably due to the increasing availability of mobile platforms and the introduction of Free-to-Play (F2P) business models by the interactive entertainment industry [15,29,50,51]. Of the wide variety of online games, the Massively Multi-Player Online Game (MMOG) format, and its derivatives, is unique in that these games see thousands or more players interacting within the same virtual environment [21,22,42,46,64]. The games can support complex virtual societies that include ingame economies [3,8].

Videogame avatars provide an expressive avenue for players to represent themselves virtually. Research has shown that these avatars, while virtual, can reveal aspects of players' identities, along with physical, social, and cultural values of the real-world. In this paper, we present an approach for modeling player values through their avatars using artificial intelligence (AI) clustering techniques. In a study with 191 participants who created avatars using our system, we provide a thorough comparison of the techniques across numerical, textual, and visual data. Our findings showed that these data structures can effectively reveal players' values and preferences, such as conforming to stereotypes of character roles using statistical attributes, modeling nuances in text descriptions of avatars, and identifying "best-example" (prototypical) avatar appearances that players can be quantitatively shown to conform to. Our findings suggest that AI clustering approaches can be used to model players to yield insight into implicitly held values in a data-driven manner through virtual avatars.

Behavioral game analytics has predominantly been confined to work on single games, which means that the cross-game applicability of current knowledge remains largely unknown. Here four experiments are presented focusing on the relationship between game ownership, time invested in playing games, and the players themselves, across more than 3000 games distributed by the Steam platform and over 6 million players, covering a total playtime of over 5 billion hours. Experiments are targeted at uncovering high-level patterns in the behavior of players focusing on playtime, using frequent itemset mining on game ownership, cluster analysis to develop playtime-dependent player profiles, correlation between user game rankings and, review scores, playtime and game ownership, as well as cluster analysis on Steam games. Within the context of playtime, the analyses presented provide unique insights into the behavior of game players as they occur across games, for example in how players distribute their time across games.

Clustering is an essential game analysis tool for understanding There are many clustering procedures that could be used player strengths and preferences. For example, clustering to group players based upon their play styles, with k-means techniques have been used to identify player preferences clustering being the most common method. Our use of for using vehicles over direct combat (Drachen et al. 2012), a model-based semi-parametric Bayesian clustering procedure for taking time to solve puzzles over running through content has two important advantages. First, the number of (Drachen, Canossa, and Yannakakis 2009), for understanding clusters (unique player styles) does not have to be prespecified.

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.