以顧客價值為基礎之電子化顧客關係管理服務架構

網路的出現與普及造就龐大商機與另類服務提供方式，但資訊透明化與低轉換成本使消費者對於企業之忠誠度大幅下降，過去企業聚焦在刺激消費的行銷導向，不斷拓展客源卻忽視留住舊客戶的重要性，高顧客流失率為網路特性所衍生出的顧客現象，亦是企業必須解決與重視之問題。企業應有效率的準確掌握顧客需求，並將資源轉化成最適的服務提供給顧客以滿足之，方能留住顧客，因此本研究目的為(1)將顧客使用網路服務所欲獲得的價值重新分層，以利於未來提供客製化之顧客關係管理服務(2)建立以顧客價值為基礎之架構，並連結顧客需求和其相對應的顧客關係管理服務(3)預測顧客未來顧客價值的需求層級，並推薦最適顧客關係管理E化服務組合來滿足顧客的需求。本研究依文獻顧客價值分層概念、融入CRM管理思維演進建構出顧客價值模型，並運用馬可夫鏈來預測顧客價值和貝式定理來推薦最適服務組合，並選定Apple-iTunes來進行個案模擬與驗證。研究結果顯示，當紀錄顧客兩個月(52筆)使用資料來預測顧客價值會有最高峰之準確度，一般學生和沉迷上班族達70.6%，沉迷型學生60.4%、一般上班族50%，因此資料筆數與顧客類型的差異會影響到方法的準確度。此外，貝氏定理預測最適服務組合之能力並不受顧客類型影響，且無論樣本數多寡，適切度都高達80%至90%，代表依據顧客過去的消費習慣和紀錄來推薦適切服務給顧客是很穩定且精確的。本研究提出之模型降低推薦錯誤及不適服務之風險，並提供企業在實務上預測和掌握顧客需求上參考依據。

關鍵字

顧客價值；顧客關係管理；馬可夫鏈；貝氏定理

並列摘要

Due to the emergence and popularity of the internet, many business opportunities and special services are created. However, the information transparency and the low transformation cost result in the decrement of customer loyalty. In the past, firms focus on stimulating consumption and acquiring customers; nevertheless, neglecting the significance of customer retention. Furthermore, Internet is also the major problem for e-CRM which may result in high customer defecting rate. Hence, it is important to obtain customer needs accurately and efficiently and deliver appropriate CRM e-service combination to customers.Consequently, the purposes of this research are (1) separating customer perceived value into different levels based on the customer needs effectively, (2) providing a value-based model which can relate the customer perceived value and the corresponding CRM services and utilizes the least resources, and (3) forecasting customer need and recommending appropriate and correlated combination of CRM e-services. This study delaminates customer value based on literature and blends in the evolution of CRM concept to build the customer value model. Meanwhile, this work applies Markov Chain and Bayesian theorem to forecast and recommend appropriate CRM e-services to customers. This study uses Apple iTunes as the case to verify the performance from simulation. The findings reveal that the precision is highest for 52 samples of customer behavior. The precision of typical student and addicted worker can reach 70.6%, 60.4% for addicted student, 50% for typical worker. The results also show that the number of samples and customer type are critical factors to affect the validity of Markov Chain. Additionally, the performance of Bayesian theorem to forecast and recommend appropriate CRM e-services is insignificantly influenced by the customer type and sample. The adequacy is as high as 80% to 90%. The findings reveal Bayesian approach provides stable and precise prediction. The proposed model not only diminishes the risk for recommending inappropriate CRM e-services but also avoids to waste resources based on customer needs.

並列關鍵字

Customer Value ； Customer Relationship Management ； Markov Chain ； Bayesian theorem

參考文獻

Grandon, E. E., Ranganathan, C. (2001), “The impact of content and design of web sites on online sales,” Proceedings of the 7th Americas Conference on Information Systems, pp. 920-926.

Aase, K. K. (2001), “A Markov model for the pricing of catastrophe insurance futures and spreads,” Journal of Risk and Insurance, Vol. 68, pp. 25-49.

Bhatia, A. (1999), “Customer Relationship Management 1th edition, toolbox Portal for CRM.

Boerner, R. E. J. (1996), “Markov Models of inertia and Dynamism on Two Contiguous Ohio Landscapes, Geographical, Analysisp ,” Vol. 28(1) , pp. 56-66.

Brown, S. A. ( 2000), “Customer Relationship Management: A Strategic Imperativein the World of e-Business, ” John Wiley & Sons.

國際替代計量

以顧客價值為基礎之電子化顧客關係管理服務架構

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