Game analytics - maximizing the value of player data

During the years of the Information Age, technological advances in the computers, satellites, data transfer, optics, and digital storage has led to the collection of an immense mass of data on everything from business to astronomy, counting on the power of digital computing to sort through the amalgam of information and generate meaning from the data. Initially, in the 1970s and 1980s of the previous century, data were stored on disparate structures and very rapidly became overwhelming. The initial chaos led to the creation of structured databases and database management systems to assist with the management of large corpuses of data, and notably, the effective and efficient retrieval of information from databases. The rise of the database management system increased the already rapid pace of information gathering.peer-reviewe

Extended RFM logit model for churn prediction in the mobile gaming market

As markets are becoming increasingly saturated, many businesses are shifting their focus to customer retention. In their need to understand and predict future customer behavior, businesses across sectors are adopting data-driven business intelligence to deal with churn prediction. A good example of this approach to retention management is the mobile game industry. This business sector usually relies on a considerable amount of behavioral telemetry data that allows them to understand how users interact with games. This high-resolution information enables game companies to develop and adopt accurate models for detecting customers with a high attrition propensity. This paper focuses on building a churn prediction model for the mobile gaming market by utilizing logistic regression analysis in the extended recency, frequency and monetary (RFM) framework. The model relies on a large set of raw telemetry data that was transformed into interpretable game-independent features. Robust statistical measures and dominance analysis were applied in order to assess feature importance. Established features are used to develop a logistic model for churn prediction and to classify potential churners in a population of users, regardless of their lifetime

Animal-Computer Interaction: a Manifesto (2011) and sections from Towards an Animal-Centred Ethics for Animal-Computer Interaction (2016)

Reprint of journal article "Animal-Computer Interaction: a Manifesto" (2011) and of sections of journal article "Towards an Animal-Centred Ethics for Animal-Computer Interaction" (2016

Analyzing Player Networks in Destiny

Destiny is a hybrid online shooter sharing features with Massively Multi-Player Online Games and first-person shooters and is the to date the most expensive digital game produced. It has attracted millions of players to compete or collaborate within a persistent online environment. In multiplayer online games, the interaction between the players and the social community that forms in persistent games forms a crucial element in retaining and entertaining players. Social networks in games have thus been a focus of research, but the relationships between player behavior, performance, engagement and the networks forming as a result of interactions, are not well understood. In this paper, a large-scale study of social networks in hybrid online games/shooters is presented. In a network of over 3 million players, the connections formed via direct competitive play are explored and analyzed to answer five main research question focusing on the patterns of players who play with the same people and those who play with random groups, and how differences in this behavior influence performance and engagement metrics. Results show that players with stronger social relationships have a higher performance based on win/loss ratio and kill/death ratio, as well as a tendency to play more and longer

Exploring the Unprecedented Privacy Risks of the Metaverse

Thirty study participants playtested an innocent-looking "escape room" game in virtual reality (VR). Behind the scenes, an adversarial program had accurately inferred over 25 personal data attributes, from anthropometrics like height and wingspan to demographics like age and gender, within just a few minutes of gameplay. As notoriously data-hungry companies become increasingly involved in VR development, this experimental scenario may soon represent a typical VR user experience. While virtual telepresence applications (and the so-called "metaverse") have recently received increased attention and investment from major tech firms, these environments remain relatively under-studied from a security and privacy standpoint. In this work, we illustrate how VR attackers can covertly ascertain dozens of personal data attributes from seemingly-anonymous users of popular metaverse applications like VRChat. These attackers can be as simple as other VR users without special privilege, and the potential scale and scope of this data collection far exceed what is feasible within traditional mobile and web applications. We aim to shed light on the unique privacy risks of the metaverse, and provide the first holistic framework for understanding intrusive data harvesting attacks in these emerging VR ecosystems

Inferring Private Personal Attributes of Virtual Reality Users from Head and Hand Motion Data

Motion tracking "telemetry" data lies at the core of nearly all modern virtual reality (VR) and metaverse experiences. While generally presumed innocuous, recent studies have demonstrated that motion data actually has the potential to uniquely identify VR users. In this study, we go a step further, showing that a variety of private user information can be inferred just by analyzing motion data recorded by VR devices. We conducted a large-scale survey of VR users (N=1,006) with dozens of questions ranging from background and demographics to behavioral patterns and health information. We then collected VR motion samples of each user playing the game ``Beat Saber,'' and attempted to infer their survey responses using just their head and hand motion patterns. Using simple machine learning models, many of these attributes could accurately and consistently be inferred from VR motion data alone, highlighting the pressing need for privacy-preserving mechanisms in multi-user VR applications