Browsing by Author "Chiu, Yi-Ling"

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Churn prediction and social neighbour influences for different types of user groups in virtual worlds
(2019-06-01)
Liu, Duen-Ren
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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
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.
Customer Churn Prediction in Virtual Worlds
(2015)
Liao, Hsiu-Yu
;
Chen, Luan-Yu
;
Liu, Duen-Ren
;
Chiu, Yi-Ling
;
資訊管理與財務金融系註：原資管所+財金所
With the emerging of social network websites, more and more social network online games are booming. Players have more alternatives of VWs games, while platform providers suffer from the problems of high customer turnover rate and low-customer-loyalty. Therefore, building a churn prediction model to facilitate subsequent churn management and customer retention is the best core marketing strategy. In this paper, we put emphasis on modeling a hybrid classification, which takes monetary cost, user behavior and social neighbor features into consideration. The experimental results show that the proposed hybrid model is well-suited for this problem in virtual worlds comparing to the existing churn prediction methods applied in the traditional retail, and financial industry.