虛擬世界高價值客戶流失預測

Abstract

隨著社群網路的盛行及發展，許多線上遊戲逐漸問世。虛擬世界市場更藉著這波浪潮得到許多關注，近年來，虛擬世界相關遊戲不斷增加，玩家選擇變得更多，導致虛擬遊戲公司面臨顧客的高流失率及低忠誠度，又根據帕雷托80/20法則 — 公司百分之八十的利潤來自於前百分之二十的高價值客戶，失去一位高價值客戶比普通客戶對公司的影響相較的大。 因上述原因，建立一顧客流失預測模型有助於顧客關係管理，更成為角逐虛擬遊戲市場的重要策略，本研究將著重於預測模型的整合分類運用，綜合考量使用者金錢消費量、遊戲行為以及社群鄰居影響力等因素，其中採用RFM模型協助進行顧客流失預測，運用以上機制加強虛擬世界中的社群影響以提升預測準確度。 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，並利用許多常見的分類方法進行預測，考慮上述虛擬世界特色的方法比傳統只考量使用者行為的結果更好。

With the emerging of social network websites, more and more social network online games are produced. Also, the trend of playing online games with friends encourages the flourishing VWs market. Nowadays, users own more selections of VWs games while companies consequently suffer from the problems of high customer turnover rate and low-customer-loyalty. Moreover, according to the Pareto principle, 80% of a company's profits come from 20% of its customers (the high value segment). Losing a high-value customer will naturally be more damaging than the loss of a low-value one. 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. Through the perspective of RFM model, we can predict more precisely. Our research applies the dataset from Roomi and several common classification methods to conduct the prediction. The experimental results show that the proposed hybrid model is more thoughtful and well-suited for this problem compare to the traditional way in each classification method.