From Non-Paying to Premium: Predicting User Conversion in Video Games with Ensemble Learning

Retaining premium players is key to the success of free-to-play games, but most of them do not start purchasing right after joining the game. By exploiting the exceptionally rich datasets recorded by modern video games--which provide information on the individual behavior of each and every player--survival analysis techniques can be used to predict what players are more likely to become paying (or even premium) users and when, both in terms of time and game level, the conversion will take place. Here we show that a traditional semi-parametric model (Cox regression), a random survival forest (RSF) technique and a method based on conditional inference survival ensembles all yield very promising results. However, the last approach has the advantage of being able to correct the inherent bias in RSF models by dividing the procedure into two steps: first selecting the best predictor to perform the splitting and then the best split point for that covariate. The proposed conditional inference survival ensembles method could be readily used in operational environments for early identification of premium players and the parts of the game that may prompt them to become paying users. Such knowledge would allow developers to induce their conversion and, more generally, to better understand the needs of their players and provide them with a personalized experience, thereby increasing their engagement and paving the way to higher monetization.Comment: social games, conversion prediction, ensemble methods, survival analysis, online games, user behavio

Customer Lifetime Value Prediction in Non-Contractual Freemium Settings: Chasing High-Value Users Using Deep Neural Networks and SMOTE

In non-contractual freemium and sharing economy settings, a small share of users often drives the largest part of revenue for firms and co-finances the free provision of the product or service to a large number of users. Successfully retaining and upselling such high-value users can be crucial to firms\u27 survival. Predictions of customers\u27 Lifetime Value (LTV) are a much used tool to identify high-value users and inform marketing initiatives. This paper frames the related prediction problem and applies a number of common machine learning methods for the prediction of individual-level LTV. As only a small subset of users ever makes a purchase, data are highly imbalanced. The study therefore combines said methods with synthetic minority oversampling (SMOTE) in an attempt to achieve better prediction performance. Results indicate that data augmentation with SMOTE improves prediction performance for premium and high-value users, especially when used in combination with deep neural networks

Discovering the Pedagogy and Secrets of Gamification and Game-Based Learning Applied to the Music Theory Classroom

This research project aims to establish the credibility of gamification and game-based learning (GBL) in higher education and online education, specifically for applying digital game-based learning (DGBL) to the twenty-first-century music theory classroom. This research project aims to address the current Education Engagement Crisis, the historical need of engaging students, and adapting the music curriculum to the current technological age. This research project will propose an original digital game concept and framework for teaching music theory core skills and other areas of music-related study in higher education as its contribution to the field and research of music education and digital game-based learning. The proposed game, the Universe of Music Theory: Music Masters (UoMT), will be an immersive, engaging, fun, and interactive, online learning-centered game created for the music theory core curricula and designed to address the preferred learning methods of digital natives. This framework may work alongside any music-core program or course as a MIDI lab activity, course-facilitated, or independent supplemental teaching and learning tool. The UoMT will facilitate unique opportunities to teach, reinforce, and assess music theory concepts in a praxial manner that will enable students to practice music-core skills (Music Theory, Keyboard Skills, and Aural Skills) and explore interconnected music-related disciplines (music academia, natural and scientific sound and music phenomena, and psychology of music). What the student learns in class will increase their in-game efficiency and what the student reviews in the game will increase their in-class efficiency

A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction

Companies across the globe are keen on targeting potential high-value customers in an attempt to expand revenue and this could be achieved only by understanding the customers more. Customer Lifetime Value (CLV) is the total monetary value of transactions/purchases made by a customer with the business over an intended period of time and is used as means to estimate future customer interactions. CLV finds application in a number of distinct business domains such as Banking, Insurance, Online-entertainment, Gaming, and E-Commerce. The existing distribution-based and basic (recency, frequency & monetary) based models face a limitation in terms of handling a wide variety of input features. Moreover, the more advanced Deep learning approaches could be superfluous and add an undesirable element of complexity in certain application areas. We, therefore, propose a system which is able to qualify both as effective, and comprehensive yet simple and interpretable. With that in mind, we develop a meta-learning-based stacked regression model which combines the predictions from bagging and boosting models that each is found to perform well individually. Empirical tests have been carried out on an openly available Online Retail dataset to evaluate various models and show the efficacy of the proposed approach.Comment: 11 pages, 7 figure

Dynamic In-game Advertising in 3D Digital Games. A threat or a possibility

Lately, digital games have developed concerning their use as a marketing medium. The present article is part of a study aimed at building a theoretical model for measuring and analyzing dynamic in-game advertising in 3D digital games. The study is explorative in nature, because it intends to build a new model of a real phenomenon based on one or more existing theories. Dynamic in-game advertising can be implemented in a 3D digital game without harming the gameplay experience, while still being effective from the marketer’s point of view. An optimized dynamic in-game advertisement is realistically and repeatedly, but subtly placed and interactive advertisement of a low-involvement product.© 2012 the Author. Published by Nordicom. All works published by Nordicom are licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public licence (CC BY-NC-ND 4.0) and are Open Access.fi=vertaisarvioitu|en=peerReviewed

Video game technology and learning in the music classroom

Game-based learning, or the process of adapting an educational concept into a game-based structure, has been studied by researchers for nearly a century. Over the last several decades, new technologies have allowed digital media to create a multibillion- dollar entertainment industry commonly known as video games. Video games have become a tool for many educators who have the potential to engage and motivate students to learn in various subjects and disciplines. The purpose of this study was to determine the effectiveness of digital game-based learning in comparison to other teaching methods as related to music education and to explore the perspectives of young students regarding video games both in school and in their personal lives. Ninety-two (n = 92) fifth and sixth grade students in a northeastern U.S. elementary school completed a mixed-method experimental study consisting of a pretest/posttest control group, surveys, and in-depth interviews. Results showed that students who had access to educational video games combined with the assistance of an instructor achieved higher mean scores compared with students who had access to either video games without instruction or instruction without video games. Survey and interview data suggested that students enjoyed playing video games on a regular basis for reasons such as enjoyment, socialization, and distraction. The majority of respondents believed that video games can and should be used in educational practices, including music education, but current educational games are inadequate because they do not possess the qualities of entertainment that are inherent in commercially designed games. These findings suggested that educational video games may be potentially used as an effective tool in the music classroom to teach musical concepts and skills. In addition, benefits may also include increased student motivation, engagement, and a hands-on approach to learning that is based on the students’ individual needs. However, it may be necessary for video games to be used in combination with a qualified teacher to prevent confusion, distraction, and possible frustration. Pairing quality instruction with engaging technology that is relevant in children’s lives may be highly beneficial for the continued development of music education