臺灣博碩士論文加值系統

隨著資訊產業的發展，企業與顧客之間的活動關係也日趨複雜且迅速，而企業行銷資源的配置與行銷顧客的抉擇，在激烈的競爭商場中愈形重要，因此如何將資源花在刀口上，並減少行銷預算浪費，顧客價值分析乃成為重要的課題之一。本篇論文結合模糊理論(Fuzzy Theory)、馬可夫鏈(Markov chain)、和RFM(Recency、Frequency、Monetary)模式，配合折現模式來計算顧客終身價值(customer lifetime values; CLV)，其中模糊理論及RFM模型是用來定義顧客之購買狀態，馬可夫鏈則是用來推算顧客在每期購買狀態改變的機率，然後推估出顧客在每期交易的轉換機率，再結合產品的收益與成本資料，便可以算出顧客在各期對公司的利潤貢獻，最後將各期的利潤貢獻折現加總，便可以計算出各種購買狀況下的顧客價值，利用此顧客價值就可指出哪些才是對企業是有利的顧客。本研究利用某家醫療藥品器材商之實際銷售資料，做模型的評估，利用三種不同的歸屬函數來實作，並與其他學者提出之顧客價值模型做比較，發現本模型之評估結果優於其他模型。

Because of progressive development of information technology, the relationship between enterprises and customers becomes more complicated. Therefore, it is an important issue for resource allocation among customers. To allocate resources efficiently and reduce costs for marketing budget, customer value analysis turns to be an important tool. In this thesis, fuzzy theory, Markov chain and RFM model are integrated to evaluate customer lifetime values (CLV). This approach calculates the profit contribution of customers in every purchasing situation. First of all, customer purchasing state is updated contiguously by fuzzy theory and RFM model with transition matrix which represents the probabilities among purchasing states. Then the profit contribution of each period is computed by using revenue and cost data. Finally, the profit contribution of each customer is accumulated through some discounting consideration. This will construct the final customer lifetime values. The proposed method has been verified by using sales records from a well known medical company in central Taiwan. The proposed model outperforms other methods and obtains a good accurate rate for estimating of customer lifetime values.

致謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 V
表目錄 VI
第一章　緒論 1
第一節 研究背景 1
第二節 研究動機與目的 2
第三節 本文架構 3
第二章　文獻探討 4
第一節 資料探勘(Data Mining) 4
第二節 顧客價值模型 5
第三節 RFM(Recency、Frequency、Monetary)模型 5
第四節 模糊理論(Fuzzy Theory) 8
第五節 馬可夫鏈模型(Markov chain model) 15
第三章　研究方法 23
第一節 定義顧客購買狀態及R、F、M之歸屬函數 23
第二節 建立馬可夫機率移轉矩陣 27
第三節 建立馬可夫利潤矩陣 29
第四節 顧客價值計算 30
第四章　實驗結果分析 32
第一節 定義顧客狀態 32
第二節 建立機率移轉矩陣及利潤矩陣 37
第三節 進行顧客價值的估計 39
第四節 結果比較 41
第五章　結論 47
第一節 結論 47
第二節 研究限制 47
參考文獻 49

[1] 丁一賢與陳牧言，2003，資料探勘，台中：滄海書局。
[2] 宋家寬，2002，應用貝氏模式與馬可夫鏈於顧客轉移模型之分析，臺灣大學國際企業學研究所碩士論文。
[3] 何敏、張洪偉與張波，2005『模糊ISODATA及在CRM中的應用』，計算機應用，第二十一卷．第六期：1455∼1458頁。
[4] 邱宏彬與蘇建源，2004『一個可彈性支援顧客關係管理與資料庫行銷之模糊RFM Model』，電子商務學報，第六卷．第二期：149∼173頁。
[5] 徐村和與林凌仲，2006『顧客價值為基礎的競爭策略模式－模糊品質機能展開之應用』，管理學報，第二十三卷．第五期：557∼579頁。
[6] 高孔廉與張緯良，2000，作業研究，台北：五南圖書出版公司。
[7] 陳坤茂，1998，作業研究，台北：華泰文化事業股份有限公司。
[8] 陳秋恭，2004，應用模糊類神經網路於穿刺結構之動態訊號分析，成功大學航空太空工程研究所碩士論文。
[9] 郭瑞祥、蔣明晃與陳宏毅，2004『顧客價值分析之隨機模型建立及實證』，管理學報，第二十一卷．第五期：675∼692頁。
[10] 曾憲雄、蔡秀滿、蘇東興、曾秋蓉與王慶堯，2004，資料探勘，台北：旗標出版股份有限公司。
[11] 閔庭祥，2001，顧客關係管理系統之價值模型建構，中央大學資訊管理所博士論文。
[12] 楊清潭，2003，應用資料探勘技術於顧客價值分析之研究，東吳大學資訊管理科學所碩士論文。
[13] 葉丁鴻、林義貴與吳炎崑譯，Richard Bronson and Gray Bronson 著，1996，管理數學，台中，滄海書局。
[14] 蘇木春與張孝德，2000，機器學習：類神經路、模糊系統以及基因演算法則，台北：全華科技圖書股份有限公司。
[15] 蘇育代，2004，行銷策略與消費者行為交互影響之研究－馬可夫鏈理論與數理模式建構之運用，臺北大學企業管理學系碩士論文。
[16] Berger, P. D., and Nasr, N. I. "Customer Lifetime Value: Marketing Models and Applications," Journal of Interactive Marketing (12:1) 1998, pp. 17-30.
[17] Colombo, R., and Jiang, W. "A Stochastic RFM Model," Journal of Interactive Marketing (13:3) 1999, pp. 2-12.
[18] Dwyer, R. F. "Customer Lifetime Valuation to Support Marketing Decision Making," Journal of Direct Marketing (11:4) 1997, pp. 6-13.
[19] Etzion, O., Fisher, A., and Wasserkrug, S. "E-CLV: A Modeling Approach for Customer Lifetime Evaluation in E-Commerce Domains, with an Application and Case Study for Online Auction," Information Systems Frontiers (7:4) 2005, pp. 421-434.
[20] Fukuda, T., Morimoto, Y., Morishita, S., and Tokuyama, T. "Mining Optimized Association Rules for Numeric Attributes," The ACM Sigact-Sigmod-Sigart Symposium on Principles of Database Systems 1996, pp. 182-191.
[21] Gupta, S., and Lehmann, D. R. "Customers as Assets," Journal of Interactive Marketing (17:1) 2003, pp. 9-24.
[22] Ha, S. H., and Park, S. C. "Application of Data Mining Tools to Hotel Data Mart on the Intrant for Database Marketing," Journal of Expert Systems with Applications (15) 1998, pp. 1-31.
[23] Ha, S. H., Bae, S. M., and Park, S. C. "Customer's Time-Variant Purchase Behavior and Corresponding Marking," Computers and Industrial Engineering (43) 2002, pp. 801-820.
[24] Hand, D. J., Blunt, G., Kelly, M. G., and M., A. N. "Data Mining for Fun and Profit," Statistical Science (15:2) 2000, pp. 111-131.
[25] Hsieh, N. "An Integrated Data Mining and Behavioral Scoring Model for Analyzing Bank Customers," Expert Systems with Applications (24) 2004, pp. 623-633.
[26] Hughes, A. M. Strategic Database Marketing: The Masterplan for Starting and Managing a Profitable, customer-based marketing program Probus Publishing Company, 1994.
[27] Hughes, A. M. "Boosting Response with RFM：Recency, Frequency, and Monetary Analysis Finds the Buyers in Your Database," American Demographics 1996, pp. 4-10.
[28] Hwang, H., Jung, T., and Suh, E. "An LTV Model and Customer Segmentation Based on Customer Value: a Case Study on the Wireless Telecommunication Industry," Expert Systems with Applications (26) 2004, pp. 181-188.
[29] Jain, D., and Singh, S. S. "Customer Lifetime Value Research in Marketing: a Review and Future Directions," Journal of Interactive Marketing (16:2) 2002, pp. 34-45.
[30] Kaymak, U. "Fuzzy Target Selection Using RFM Variables," IFSA World Congress and 20th NAFIPS International Conference (2) 2001, pp. 1038-1043.
[31] Kotler, P., Ang, S. H., Leong, S. M., and Tan, C. T. Marketing Management: An Asian Perspective, Prentice Hall, Singapore, 1999.
[32] Lee, J. H., and Park, C. "Intelligent Profitable Customers Segmentation System based on Business Intelligence Tools," Expert Systems with Applications (29) 2005, pp. 145-152.
[33] Lewis, C. D. Industrial and Business Forecasting Methods: A practical Guide to Exponential Smoothing and Curve Fitting, Butterworth Scientific, London, 1982.
[34] Liu, D., and Shih, Y. "Integrating AHP and Data Mining for Product Recommendation based on Customer Lifetime Value," Information and Management (42) 2005, pp. 387-400.
[35] Miglautsch, R. J., "Thoughts on RFM Scoring," Journal of Database Marketing (8:1) 2000, pp. 67-72.
[36] Pfeifer, P. E., and Carraway, R. L. "Modeling Customer Relationships as Markov Chains," Journal of Interactive Marketing (14:2) 2000, pp. 43-55.
[37] Sheth, J. N., Sisodia R. S., and Sharma, A. "The Antecedents and Consequences of Customer-Centric Marketing," Journal of the Academy of Marketing Science (28:1) 2000, pp. 55-66.
[38] Stone, B. "Successful Direct Marketing Methods", Lincolnwood, IL：NTC Business Books, 1995, pp. 37-57.
[39] Swami, S., Puterman, M. L., and Weinberg, C. B. "Play It Again, Sam? Optimal Replacement Policies for a Motion Picture Exhibitor," Manufacturing and Service Operations Management (3:4) 2001, pp. 369-386.
[40] Shin, H. W., and Sohn, S. Y. "Segmentation of Stock Trading Customers According to Potential Value," Expert Systems with Applications (27) 2004, pp. 27-33.
[41] Tsai, C. Y., and Chiu, C. C. "A Purchase-based Market Segmentation Methodology," Expert Systems with Applications (27) 2004, pp. 265-276.
[42] Wang, H., and Hong, W. "Managing Customer Profitability in a Competitive Market by Continuous Data Mining," Industrial Marketing Management (35) 2006, pp. 715-723.
[43] Yang, A. X. "How to Develop New Approaches to RFM Segmentation," Journal of Targeting, Measurement and Analysis for Marketing (13:1) 2004, pp. 50-60.
[44] Yen, J., and Reza, L., Fuzzy Logic Intelligence Control and Information, Prentice-Hall, Inc., 1999.