標題: Churn prediction and social neighbour influences for different types of user groups in virtual worlds
作者: Liu, Duen-Ren
Liao, Hsiu-Yu
Chen, Kuan-Yu
Chiu, Yi-Ling
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
關鍵字: churn prediction;data mining;social influence;user groups;virtual worlds
公開日期: 1-Jun-2019
摘要: 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.
URI: http://dx.doi.org/10.1111/exsy.12384
http://hdl.handle.net/11536/152283
ISSN: 0266-4720
DOI: 10.1111/exsy.12384
期刊: EXPERT SYSTEMS
Volume: 36
Issue: 3
起始頁: 0
結束頁: 0
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