Churn Prediction in Online Games Using Players’ Login Records: A Frequency Analysis Approach

Castro, Emiliano Gonçalves de; Tsuzuki, Marcos de Sales Guerra

doi:10.1109/tciaig.2015.2401979

Cited by 54 publications

(20 citation statements)

References 16 publications

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“…The problem of churn prediction has been tackled in many different domains such as telecommunication [10][11][12][13][14][15], banking [16,17], subscription services [18], game businesses [19], and retailing [20]. In general, most of the efforts in churn studies involve prediction with different definitions of the churn event [14,21], evaluating new data mining algorithms [15,22,23], and introducing ways to deal with large volumes of data [24].…”

Section: Introductionmentioning

confidence: 99%

Behavioral attributes and financial churn prediction

et al. 2018

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Customer retention is crucial in a variety of businesses as acquiring new customers is often more costly than keeping the current ones. As a consequence, churn prediction has attracted great attention from both the business and academic worlds. Traditional efforts in the financial domain mainly focus on domain specific variables such as product ownership or service usage aggregation, however, without considering dynamic behavioral patterns of customers' financial transactions. In this paper, we attempt to fill in this gap by investigating the spatio-temporal patterns and entropy of choices underlying the customers' financial decisions, and their relations to customer churning activities. Inspired by previous works in the emerging field of computational social science, we built a prediction model based on spatio-temporal and choice behavioral traits using individual transaction records. Our results show that proposed dynamic behavioral models could predict churn decisions significantly better than traditionally considered factors such as demographic-based features, and that this effect remains consistent across multiple data sets and various churn definitions. We further study the relative importance of the various behavioral features in churn prediction, and how the predictive power varies across different demographic groups. More generally, the proposed features can also be applied to churn prediction in other domains where spatio-temporal behavioral data are available.

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Section: Introductionmentioning

confidence: 99%

Behavioral attributes and financial churn prediction

et al. 2018

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“…Since different combinations of data have different analytical power, it is necessary to determine most suitable data for the analysis being performed. RFM features (Recency, Frequency and Monetary) proved to be a good source of customer behavior in general [3,12], but also in the F2P online gaming market [6,27]. Recency features are related to the time of the last usage of the service; frequency features are related to how often the service is used, while monetary features are related to the total money that the customer has spent on services over a certain period.…”

Section: Data Transformationmentioning

confidence: 99%

“…Free-to play (F2P) games and other service-based business models are dominant in the online gaming market today. Within the F2P business setting, instead of charging a single upfront fee for the game license, companies make money with subscriptions, advertising, or microtransactions from paying players throughout their lifetime as customers [6]. F2P games are free to download and the developer hopes to generate revenue via in-app purchases and/or advertising [20].…”

Section: Introductionmentioning

confidence: 99%

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

Perišić

Pahor

2020

Cro. Oper. Res. Rev.

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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.

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“…The results from the theory‐driven model significantly outperformed those from diffusion‐based churn prediction; the experimental results also showed that achievement and socialization motivations are key factors to churning. A frequency analysis approach was adopted for churn prediction in online games using players' log‐in records (Castro & Tsuzuki, ). Kawale, Pal, and Srivastava () proposed a modified diffusion model to propagate the influence vector in a player's network, which represents the social influence of the network on the player.…”

Section: Related Workmentioning

confidence: 99%

Churn prediction and social neighbour influences for different types of user groups in virtual worlds

et al. 2019

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In virtual worlds (VWs), users have more VW games alternatives, whereas VW companies consequently suffer from high customer turnover rate and low customer loyalty. Therefore, building a churn prediction model to facilitate subsequent churn management and customer retention is important. The churn behaviours and the impact of social neighbour influences to customer churn may be different for different types of users. Accordingly, we segment users into stable, unstable, and solitary user groups according to their social contact behaviours in VWs. Novel segmentation‐based churn prediction approaches are proposed for churn prediction in VWs by building prediction models for each type of user groups and considering the effect of social neighbour influences for different user groups. The proposed approaches are evaluated by conducting experiments with a dataset collected from a VW platform. The experimental results show different churn prediction performances under different user groups. The segmentation‐based churn prediction approaches perform better than do general approaches without considering user groups. Moreover, the results also reveal that social neighbour influences have a positive impact on stable and unstable users. The proposed work contributes to investigating the social neighbour influences on churn prediction for different types of user groups in VWs.

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Churn Prediction in Online Games Using Players’ Login Records: A Frequency Analysis Approach

Cited by 54 publications

References 16 publications

Behavioral attributes and financial churn prediction

Behavioral attributes and financial churn prediction

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

Churn prediction and social neighbour influences for different types of user groups in virtual worlds

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