On churn and social contagion

Borbora, Zoheb; Chandra, Arpita; Kumaraguru, Ponnurangam; Srivastava, Jaideep

doi:10.1145/3341161.3344378

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Unsupervised approaches have also been developed, including segmentation of players (Fu et al 2017), difference in social neighbour influence in different user groups (Liu et al 2019), and distinct behavioral profiles associated with churners and active players (Borbora and Srivastava 2012). The sole research we found that used social networks to predict churn, included information from ego-nets showing that loners are much more likely to churn than the socializers, and that the propensity to churn increases with decreased socialization (Borbora et al 2019).…”

Section: Churn Prediction In Online and Mobile Gamingmentioning

confidence: 99%

Social networks for enhanced player churn prediction in mobile free-to-play games

et al. 2022

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Social networks have been shown to enhance player experience in online games and to be important for the players, who often build complex communities. In online and mobile games, the behavior of players is bursty as they tend to play intensively at first for a short time and then quit playing altogether. Such players are known as churners. In the literature, several attempts have been made at predicting player churn in online and mobile games using behavioral features from the games’ player logs as input in supervised machine learning models. Previous research shows that information from social networks provides alternative and significant information when predicting churn, and yet the importance of networks has not been fully researched in mobile gaming. In this research, we study player churn in a mobile free-to-play game with one-versus-one matches. We build two types of networks based on how two players are matched. We train churn prediction models with features extracted from the networks to evaluate their predictive performance in terms of churn. Furthermore, we predict churn using the players’ behavioral features during their first day of game playing. According to our results, the network features greatly increase the predictive performance of the models, indicating that they carry alternative information about intention to churn. In addition, the first-day features are quite predictive, which means that first day activity is sufficient to predict churn of players quite accurately, validating the bursty behavior. Our research gives an indication of which aspects of game playing are associated with churn and allow us to study influence and social factors in mobile games.

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Section: Churn Prediction In Online and Mobile Gamingmentioning

confidence: 99%