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

From Theory to Behaviour: Towards a General Model of Engagement

Engagement is a fuzzy concept. In the present work we operationalize engagement mechanistically by linking it directly to human behaviour and show that the construct of engagement can be used for shaping and interpreting data-driven methods. First we outline a formal framework for engagement modelling. Second we expanded on our previous work on theory-inspired data-driven approaches to better model the engagement process by proposing a new modelling technique, the Melchoir Model. Third, we illustrate how, through model comparison and inspection, we can link machine-learned models and underlying theoretical frameworks. Finally we discuss our results in light of a theory-driven hypothesis and highlight potential application of our work in industry.Comment: In review for being included in the proceedings of "Conference on Games

Controlling the Crucible : A Novel PvP Recommender Systems Framework for Destiny

Compared to conventional retail games, today's Massively Multiplayer Online Games (MMOGs) have become progressively more complex and volatile, living in a highly competitive market. Consumable resources in such games are nearly unlimited, making decisions to improve levels of engagement more challenging. Intelligent information filtering methods here can help players make smarter decisions, thereby improving performance, increasing level of engagement, and reducing the likelihood of early departure. In this paper, a novel approach towards building a hybrid multi-profile based recommender system for player-versus-player (PvP) content in the MMOG Destiny is presented. The framework groups the players based on three distinct traced behavioral aspects: base stats, cooldown stats, and weapon playstyle. Different combinations of these profiles are considered to make behavioral recommendations. An online evaluation was performed to investigate the usefulness of the proposed recommender framework to players of Destiny

Assessing the effects of blockchains in video games:case IkuneRacers

Abstract. Blockchain based applications are emerging on many domains to revolutionize software practices. Blockchains utilize technologies such as distributed ledgers and consensus algorithms to provide peer-to-peer based solutions that fulfil benefits like transparency, traceability, and immutability. The purpose of this study was to assess if these beneficial effects could be harnessed in video games to solve issues like poor retention and engagement. Additionally, one topic of interest was to find out if blockchain would affect the way players value their video game assets. This study utilizes the design science research methodology to address the research problem. One of the steps of the methodology includes creating a design artefact that can fulfil the objectives defined to it. For this study, however, the early steps of the methodology including the creation of the artefact were already done and addressed in a previous paper. Therefore, the main goal of this study is to demonstrate the usage of the artefact with qualitative interviews and evaluate if the objectives have been met. As an additional research question, this study set out to provide suggestions for improving the artefact for a possible new iteration. The interviews suggested that there were some indications towards increased retention for people who were interested in asset generation or the implementation of blockchain. For engagement, there were signs that people who enjoyed certain kind of video games were engaged by the asset generation aspect of the artefact. These are initial results that should be studied further to get definitive results. For the way users value their asset, there were huge discrepancies that made it difficult to draw conclusions, but the answers provided valuable insight on the topic. The themes for improving the artefact were the role of authority, asset exchange systems, blockchain transparency, third-party involvement in video games, and trust on blockchain. The findings in this study can be helpful towards further research on any of those topics, but for the purposes of design science research, focusing on asset exchange systems or third-party involvement in video games was established to be most sensible. That is because both of those domains could be improved by new blockchain based designs solutions

온라인 게임에서 유저의 행태에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 : 경영대학 경영학과, 2018. 2. 유병준.This dissertation consists of two essays on user behavior in online games. In the first essay, I identified multi-botting cheaters and measured their impacts using basic information in database such as user ID, playtime and item purchase record. I addressed the data availability issue and proposed a method for companies with limited data and resources. I also avoided large-scale transaction processing or complex development, which are fairly common in existing cheating detection methods. With respect to identifying cheaters, we used algorithms named DTW (Dynamic Time Warping) and JWD (Jaro–Winkler distance). I also measured the effects of using hacking tool by employing DID (Difference in Differences). My analysis results show some counter-intuitive results. Overall, cheaters constitute a minute part of users in terms of numbers – only about 0.25%. However, they hold approximately 12% of revenue. Furthermore, the usage of hacking tools causes a 102% and 79% increase in playtime and purchase respectively right after users start to use hacking tools. According to additional analysis, it could be shown that the positive effects of hacking tools are not just short-term. My granger causality test also reveals that cheating users activity does not affect other users' purchases or playtime trend. In the second essay, I propose a methodology to deal with churn prediction that meets two major purposes in the mobile casual game context. First, reducing the cost of data preparation, which is growing its importance in the big-data environment. Second, coming up with an algorithm that shows favorable performance comparable to that of the state-of-the-art. As a result, we succeed in greatly lowering the cost of the data preparation process by employing the sequence structure of the log data as it is. In addition, our sequence classification model based on CNN-LSTM shows superior results compared to the models of previous studies.Essay 1. Is Cheating Always Bad? A study of cheating identification and measurement of the effect 1 1. Introduction 2 2. Literature Review 8 3. Data 16 4. Hypotheses 17 5. Methodology 20 5.1 Cheating Identification 20 5.2 Measurement of Cheating Tool Usage Effect 28 6. Result 33 6.1 Cheating Identification 33 6.2 Measurement of Cheating Tool Usage Effect 33 7. Additional Analysis 35 7.1 Lifespan of Cheating Users 35 7.2 Granger Causality Test 36 8. Discussion and Conclusion 37 9. References 48 Essay 2. Churn Prediction in Mobile Casual Game: A Deep Sequence Classification Approach 61 1. Introduction 62 2. Definition of Churn 64 3. Related Works 65 4. Data 66 5. Methodology 66 5.1 Data Preparation 66 5.2 Prediction Model 71 6. Result and Discussion 74 7. References 77Docto