虛擬世界之客戶流失預測

Abstract

隨著網路科技的蓬勃發展，線上遊戲也變得更加熱門。原先在遊戲軟體已佔有一席之地的虛擬世界(Virtual World)，更藉著這波浪潮得到許多關注。虛擬世界相關的遊戲在近幾年不斷的增加，高成長的虛擬世界市場吸引了許多廠商投入資源競爭。而激烈的競爭導致顧客流失，使得利潤逐漸的減少。而且，不滿的顧客會對虛擬世界公司做出負面的評論，對公司會有負面的影響。因此，預測虛擬世界的流失使用者，並且使他們更加滿意而不想流失，已經變成了一個重要的問題。 目前虛擬世界客戶流失預測的相關文獻並不多。客戶流失預測相關文獻，大多著重在電信產業、零售業、金融業等現實世界的客戶流失預測，並未考量客戶的社交互動行為。而目前虛擬世界客戶流失預測的相關文獻很少，著重在以遊戲資料進行分析，設計考量遊戲互動影響的客戶流失預測方法，並未考慮到虛擬世界使用者的虛擬生活行為變化，以及虛擬社交互動行為變化，包含對話、交友、拜訪等社交互動，以及生活圈鄰居的影響力之影響。 本研究以虛擬世界的三種特色：虛擬生活行為變化、虛擬社交互動行為變化以及生活圈鄰居的影響力之影響進行，提出了創新的虛擬世界客戶流失方法。所提的預測方法主要以決策樹中的隨機森林分類法，以及類神經網路這兩種分類方法預測流失使用者。最後本研究以台灣的虛擬世界網站Roomi作為實驗評估的資料來源，實驗結果顯示，考慮上述虛擬世界三種特色的結果比僅考慮部分特色的結果更好，並且，決策樹在虛擬世界的客戶流失預測中比類神經網路有更好的結果。

With the rapid development of internet websites, more and more online games are produced. Virtual Worlds (VWs) are getting more attention because of the booming trend of on-line games. The highly growth market of VWs attract many companies to join the contest. But the fierce competitions result in a high customer turnover and shortage of profit. Moreover, the unsatisfied customer may spread negative word-of-mouth effect to the company. Therefore, how to predict the churner in the virtual worlds and satisfy them has becoming an important issue. Even though customers churn prediction has been studying in the telecom, financial and retail industry to reduce customer turnover rate, but has not been applied in the virtual worlds to solve the customers’ turnover problem. The objective of this research is to develop a novel virtual world customer churn prediction method. This study analyzes the relationship between customer churn and three kinds of user behaviors in virtual world. The behaviors include virtual life behaviors, social contact behavior, and social influences of social circle neighbors. Our proposed model use random forest and neural network to classify the customer churn in virtual worlds by the three user behaviors mentioned above. The results shows our propose model considering both user’s activity energy and social circle neighbors’ social influence will have better performance. Also, the result shows the performance of decision tree is better than neural network for customer churn prediction in virtual worlds.