(44.192.10.166) 您好!臺灣時間:2021/03/06 03:58
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:翁誌謙
研究生(外文):Chih-Chien Weng
論文名稱:應用資料探勘技術於顧客關係管理-以兒童牙醫診所為例
論文名稱(外文):Applying data mining technique in customer relationship management–taking a pediatric dental clinic as an example
指導教授:吳信宏吳信宏引用關係
指導教授(外文):Hsin-Hung Wu
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:企業管理學系
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:60
中文關鍵詞:顧客關係管理資料探勘LRFM模型兒童牙醫自組織映射圖網路K-Means分群法分類與回歸樹
外文關鍵詞:Customer Relationship ManagementData MiningLRFM modelpediatric dentalSelf-Organizing MapK-MeansCART
相關次數:
  • 被引用被引用:3
  • 點閱點閱:423
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
近年來國內生活水準提高,健保制度日漸成熟,國人對醫療服務品質的要求提高,因此醫療產業若能提高醫療品質與病患滿意度,改善與病患間的關係是本研究關切的議題。利用資料探勘技術將病患的資料轉換成資訊,找出重要的病患,並做消費行為預測,擬定適合的行銷策略來滿足不同的病患,達到降低成本與提升經營績效之目的。
本研究透過LRFM (Length, Recency, Frequency, and Monetary)模型針對某家兒童牙醫診所的病患資料進行資料轉換與分析,再透過自組織映射圖網路(Self-Organizing Maps)與K-Means兩階段分群法對病患進行分群,並對此分群後的12群病患做敘述性統計分析,了解各分群病患分別的特徵與消費行為,找出高價值病患與潛在重要病患,並藉由研究結果給予該診所醫師適合的行銷建議;另外以F為目標產出變數,性別、年齡、L、R與郵遞區號為投入變數,建立分類與回歸樹(Classification and Regression Tree, CART)決策樹預測模型,透過決策樹歸納出12條判斷規則,找出能為診所帶來獲利的重要顧客群以及流失顧客群的行為特徵,以提供該診所醫師在未來針對這兩類類群顧客擬訂適合的行銷策略與資源分配。

In recent years, the domestic living standards have been improved, and the national health insurance has become maturely such that the residents in Taiwan have paid much attention to enhance the quality requirements of the medical service. Thus, the purpose of this research is to improve patients’ relationship by increasing the quality of medical care and patient satisfaction. This research uses data mining technique to transform the raw data into the useful information, identify the important patients, design marketing strategies to satisfy a wide variety of patients, and then reduce the cost to improve the performance.
This study uses LRFM (Length, Recency, Frequency, and Monetary) model to analyze patients’ database of a pediatric dental clinic by deploying a two-stage clustering method, including SOM (Self-Organizing Map) and K-Means methods. The descriptive analyses and patients’ characteristics and behaviors of the twelve clusters are summarized. Moreover, frequency is chosen to be the target output variable, while gender, age, length, recency, and postal code are the input variables to establish the decision tree forecasting model of classification and regression tree (CART). Twelve rules generated by CART are depicted to identify the high value patients as well as the loss patients for this dental clinic. The findings can also provide this dental clinic to make applicable marketing strategies and allocate resource more effectively for these two patient groups.

目錄

摘要 I
ABSTRACT II
圖目錄 VI
表目錄 VII

第一章 緒論 1
第一節 研究動機與背景 1
第二節 研究目的 3
第三節 研究流程 3
第二章 文獻探討 5
第一節 顧客關係管理 5
第二節 顧客價值 6
第三節 顧客忠誠度 7
第四節 資料探勘 8
一、資料探勘的定義 8
二、資料探勘的流程 9
三、資料探勘的功能 9
四、RFM模型 10
五、自組織映射圖網路與K-Means演算法 12
第五節 決策樹 14
第三章 研究方法 17
第一節 研究架構 17
第二節 資料來源 17
第三節 分群法資料前置處理 17
第四節 分群法資料分析 18
第五節 決策樹資料前置處理 18
第六節 決策樹資料分析 20
第四章 研究結果 21
第一節 分群結果與分析 21
一、 敘述性統計分析 21
二、 LRFM資料分析 22
三、 SOM與K-Means分群分析 22
第二節 決策樹結果與分析 25
一、CART預測模型建置 25
二、預測準確率分析 26
三、重要性變數分析 28
四、決策樹規則分析 30
第五章 結論與建議 36
第一節 分群法結論與建議 36
第二節 決策樹分析結論與建議 36
第三節 分群法與決策樹分析整合建議 37
參考文獻 39

圖目錄
圖1.1 研究流程圖 4
圖4.1 SOM顧客分群圖 22
圖4.2 CART投入變數重要性 29
圖4.3 CART決策樹分類結果 31

表目錄
表3.1 實際資料與五等記分轉換表 19
表4.1 年齡與性別統計分析 21
表4.2 看診次數與性別統計分析 21
表4.3 LRF敘述統計 22
表4.4 SOM與K-Means顧客分群結果 23
表4.5 CART決策樹訓練組與測試組比例配置結果 26
表4.6 CART決策樹模型預測準確率 27
表4.7 CART決策樹訓練組與測試組預測準確率結果 27
表4.8 CART投入變數重要性 29
表4.9 CART決策樹分類規則與結果 33

一、中文部分
張心馨與蔡憲富 (2004),「以Data Mining技術結合SOM和K-Means的消費者分群法於顧客關係管理和績效獲利性評估之實證研究」,資訊管理學報,第十一卷,第四期,161-203頁。

曾憲雄、蔡秀滿、蘇東興、曾秋蓉與王慶堯 (2005),「資料探勘」,第一版,旗標出版股份有限公司,台北。

張云濤與龏玲 (2007),「資料探勘原理與技術」,初版,五南圖書出版股份有限公司,台北。

葉怡成 (2001),類神經網路模式應用與實作,第七版,儒林圖書有限公司。

二、英文部分
Anton, J. (1996), Customer Relationship Management, New York, NY: Prentice-Hall.

Anton, J. and Hoeck, M. (2002), E-Business Customer Service, Santa Monica, CA: The Anton Press.

Backman, S. J. and Cromptom, J.L. (1991), “Different between High, Spurious, Latent and Low Loyalty Participants in Two Leisure Activities,” Journal of Park and Recreation Administration, 9(2), pp. 1-14.

Baier, M., Ruf, K. M. and Chakraborty, G. (2002), Contemporary Database Marketing: Concepts and Applications, Evanston: Racom Communications.

Berry, M. J. A. and Linoff, G. S. (1997), Data Mining Technique: For Marketing, Sales, and Customer Relationship Management, New York: John Wiley and Sons.

Berry, M. J. A. and Linoff, G. S. (2004), Data Mining Techniques: for Marketing, Sales, and Customer Relationship Management, Second Editin, Indianapolis: Wiley Publishing.

Breiman, L., Friedman, J. H., Olshen, R. D. and Stone, C. J. (1984), Classification and Regression Trees, Belmont, CA: Wadsworth & Brooks.

Breuer, G. and Peyerl, H. (2006), “Shareholder Value as a Basis for Strategic Business Decision Marking in Family Farms,” Die Bodenkulter, 57(4), pp. 185-196.

Brown, S. A. (2000), Customer Relationship Management: A Strategic Imperative in the World of E-Business, Toronto: Wiley.

Burez, J. and Van den Poel, D. (2007), “CRM at Canal + Belgique: Reducing Customer Attrition through Targeted Marketing,” Expert Systems with Applications, 32(2), pp. 277-288.

Chen T. S., Lin C. C., Chiu Y. H. and Chen R. C. (2006),“Combined Density-Based and Constraint-Based Algorithm for Clustering,” Journal of Donghua University, 23(6), pp. 36-38.

Chiu, C. C., Tien, C. C. and Chou, Y. C. (2005), “Construction of Clustering and Classification Models by Integrating Fuzzy Art, Cart and Neural Network Approaches,” Journal of the Chinese Institute of Industrial Engineers, 22(2), pp. 171-188.

Chang, E. C., Huang, H. C. and Wu, H. H. (2010), “Using K-Means Method and Spectral Clustering Technique in An Outfitter’s Value Analysis,” Quality and Quantity, 44(4), pp. 807-815.

Chye, K. H. and Gerry, C. K. L. (2002), “Data Mining and Customer Relationship Marketing in the Banking Industry,” Singapore Management Review, 24(2), pp. 1-27.

Dhillon, I.S., Guan, Y., and Kulis, B. (2004), “Kernel k-Means, Spectral Clustering and Normalized Cuts,” in Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, Washington, USA, pp. 551-556.

Dick, A. S. and Basu, K. (1994), “Customer Loyalty: Toward an Integrated Conceptual Framework” Journal of the Academy of Marketing Science, 22(2), 99-113.

Fan, Z., Kabrick, J. M. and Shifley, S. R. (2006), “Classification and Regression Tree Based Survival Analysis in Oak-Dominated Forests of Missouri’s Ozark Highlands,” Canadian Journal of Forest Research, 36(7), pp. 1740-1748.

Fayyad, U. M. (1996), “Data Mining and Knowledge Discovery: Making Sense Out of Data,” IEEE Expert: Intelligent Systems and Their Application, 11(5), pp. 20-25.

Feinberg, R. and Kadam, R. (2002), “E-CRM Web Service Attributes as Determinants of Customer Satisfaction with Retail Web Sites,” International Journal of Service Industry Management, 13(5), pp. 432-451.

Frawley, W. J., Piatetsky-Shapiro, G., and Matheus, C. J. (1991), “Knowledge Discovery Databases: An Overview,” AI Magazine, 13(3), pp. 57-70.

Griffin, J. (1995), Customer Loyalty: How to Earn it, How to Keep it, New York: Lexington Books.

Han, J. and Kamber, M. (2006), Data Mining: Concepts and Techniques, Second Edition, San Francisco: Morgan Kaufmann Publishers.

Ha, S. H. and Park, S. C. (1998), “Application of Data Mining Tools to Hotel Data Mart on the Intranet for Database Marketing,” Expert Systems with Applications, 15(1), pp.1-31.

Holbrook, M. B. (1999), Customer Value: A Framework for Analysis and Research, New York: Routledge.

Hosseini, S. M., Maleki, A. and Gholamian, M. R. (2010), “Cluster Analysis Using Data Mining Approach to Develop CRM Methodology to Assess the Customer loyalty,” Expert Systems with Applications, 37(7), pp. 5259-5264.

Huang, M. J., Chen, M. Y. and Lee, S. C. (2007), “Integrating Data Mining with Case-Based Reasoning for Chronic Diseases Prognosis and Diagnosis,” Expert Systems with Applications, 32(3), pp. 856–867.

Huang, S. C., Chang, E. C. and Wu, H. H. (2009), “A Case Study of Applying Data Mining Techniques in An Outfitter’s Customer Value Analysis,” Expert Systems with Applications, 36(3), pp. 5909-5915.

Huang, M. L. and Chen, H. Y. (2005), “Development and Comparison of Automated Classifiers for Glaucoma Diagnosis Using Stratus Optical Coherence Tomography,” Investigative Ophthalmology and Visual Science, 46(11), pp. 4121-4129.

Hung, S. Y., Yen, D. C. and Wang, H. Y. (2006), “Applying Data Mining to Telecom Churn Management,” Expert Systems with Applications, 31(3), pp. 515–524.

Hughes, A. M. (1994), Strategic Database Marketing, Chicago: Probus Publishing Company.
Jain, A. K., and Dubes R. C. (1988), Algorithms for Clustering Data. New Jersey:Prentice Hall.

Jain, A.K., Murty, M.N., and Flynn, P.J. (1999), “Data Clustering: a Review,” ACM Computing Surveys, 31(3), pp. 264-323.

Joo, Y. G. and Sohn, S. Y. (2008), “Structural Equation Model for Effective CRM of Digital Content Industry,” Expert Systems with Applications, 34(1), 63-71.

Jones, T. O. and Sasser, Jr., W. E. (1995), “Why Satisfied Customer Defect,” Harvard Business Review, 73(6), 88-99.

Kahan, R. (1998), “Using Database Marketing Techniques to Enhance Your One-to-One Marketing Initiatives,” Journal of Consumer Marketing, 15(5), pp. 491-493.

Kannan, P. K. and Rao, H. R. (2001), “Introduction to the Special Issue: Decision Support Issues in Customer Relationship Management,” Decision Support Systems, 32(2), pp. 83-84.

Kass, G. (1980) , “An Exploratory Technique for Investigating Large Quantities of Categorical Data, ” Applied Statistics, 29(2), 119-127.

Kaymak, U., (2001), “Fuzzy Target Selection Using RFM Variables,” IFSA World Congress and 20th NAFIPS International Conference, pp. 25-28.
Koch, R. (2000), The 80/20 Principle: The Secret of Achieving More with Less, London: Nicholas Brealey Publishing Ltd.

Kohonen, T. (1982), “Self-Organizing Formation of Topologically Correct Feature Maps”. Biological Cybernetics. 43(1), pp 59-69.

Kohonen, T. (1990), “The Self-Organizing Map,” Proceedings of the IEEE, 78(9), pp.1464-1480.

Kolakota, R. and Robinson, M. (1999), e-Business: Roadmap for Success (1st ed.), New York, USA: Addison Wesley Longman.

Kolakota, R. and Robinson, M. (2001), e-Business 2.0: Roadmap for Success, Addison Wesley.

Kotler, P. (1994), Marketing Management: Analysis, Planning, Implementation, and Control, New Jersey: Prentice-Hall.

Kolter, P., Ang, S. H., Leong, S. M. and C. T. Tan (1998). Marketing Management: An Asian Perspective, New York: Prentice Hall.

Kumar, V. and Reinartz, W. J. (2006), Customer Relationship Management: A Database Approach, Hoboken, NJ: John Wily and Sons.

Kuo, R. J., Ho, L. M., and Hu, C. M. (2002), “Integration of Self-Organizing Feature Map and K-Means Algorithm for Market Segmentation,” Computers and Operations Research. 29(11), pp. 1475-1493.
Lee, J., Lee, J., and Feick (2001), “The Impact of Switching Costs on the Customer-Loyalty Link: Mobile Phone Service in France,” Journal of Service Marketing, 15(1), pp. 35-48.

Lee, W. I. and Shih, B. Y. (2009), “Application of Neural Networks to Recognize Profitable Customers for Dental Services Marketing – a Case of Dental Clinics in Taiwan,” Expert Systems with Applications, 36(1), pp. 199-208.

Lee, W. I., Shin B. Y., and Chung Y. S. (2008), “The Exploration of Consumers’ Behavior in Choosing Hospital by the Application of Neural Network,” Expert Systems with Applications, 34(2), pp. 806-816.

MacQueen, J. (1967), “Some Methods for Classification and Analysis of Multivariate Observations, ” In proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, pp. 281-197.

Mitra, S. and T. Acharya (2003), Data Ming: Multimedia, Soft Computing, and Bioinformatics, Hoboken: John Wiley &sons.

Mulhern, F. J., (1999) “Customer Profitability Analysis: Measurement, Concentration, and Research Directions,” Journal of Interactive Marketing, 13(1), pp. 25-40.

Oliver, R. L. (1999), “Whence Consumer Loyalty?”, Journal of Retailing, 64(1), pp. 33-44.

Parvatiyar, A. and Sheth, J. (2004), Conceptual framework of Customer Relationship Management. In: J. N. Sheth, A. Parvatiyar, and G. Shainesh (Eds), Customer Relationship Management: Emerging Concepts, Tools and Applications (5th ed.), New Delhi: Tata McGraw-Hill Publishing.

Peel, J. (2002), CRM: Redefining Customer Relationship Management, Woburn, MA: Digital Press.

Peppard, J. (2000), “Customer Relationship Management (CRM) in Financial Services,” European Management Journal, 18(3), pp. 312-327.

Peppers, D. and Rogers, M. (1996), The One to One Future: Building Relationships One Customer at a Time, NY: Doubleday.

Peppers, D. and Rogers, M. (1997), Enterprise One to One: Tools for Competing in the Interactive Age, New York: Doubleday.

Petrick, J. F. (2004), “The Roles of Quality, Value, and Satisfaction in Predicting Cruise Passengers’ Behavioral Intentions,” Journal of Travel Research, 42(4), pp. 397-407.

Pfeifer, P. E. and Carraway R. L. (2000), “Modeling Customer Relationships as Markov Chains,” Journal of Interactive Marketing, 14(2), pp. 43-55.

Reinartz, W. J. and Kumar, V. (2000), “On the Profitability of Long-Life Customer in a Noncontractual Setting: An Empirical Investigation and Implication for Marketing,” Journal of Marketing, 64(4), pp. 17-35.

Questier, F., Put, R., Coomans, D., Walczak, B., and Vander Heyden, Y. (2005). “The Use of CART and Multivariate Regression Trees for Supervised and Unsupervised Feature Selection,” Chemometrics and Intelligent Systems, 76(1), pp. 45-54.

Quinlan, J. R. (1986), Induction of Decision Trees, Machine Learning, 1(1), pp. 81-106.

Quinlan, J. R. (1993), C4.5: Programs for Machine Learning, Morgan Kaufman Publishers.

Razi, M. A. and Athappilly, K. (2005), “A Comparative Predictive Analysis of Neural Networks (NNs), Nonlinear Regression and Classification and Regression Tree (CART) Models,” Expert Systems with Applications, 29(1), pp. 65-74.

Rust, R. T. and Zahorik, A. J. (1993), “Customer Satisfaction, Customer Retention, and Market Share,” Journal of Retailing, 69(2), pp. 193–215.

Selle, B., Morgen, R. and Huwe, B. (2006), “Regionalising the Available Water Capacity from Readily Available Data,” Geoderma, 132, pp. 391-405.


Shaw, M. J., Subramaniam, C., Tan, G. W., and Welge, M. E. (2001), “Knowledge Management and Data Mining for Marketing,” Decision Support Systems, 31(1), pp. 127-137.

Sharma, S. (1996), Applied Multivariate Techniques, New York: John Wiley and Sons, Inc.

Shieh, J. I., Wu, H. H., and Huang, K. K. (2010), “A DEMATEL Method in Identifying Key Success Factors of Hospital Service Quality,” Knowledge-Based Systems, 23(3), pp. 277-282.

Simula, O., Vesanto, J. and Vasara, P. (1998), “Analysis of Industrial Systems using the Self-Organizing Map,” Proceeding of Knowledge-Based Intelligent Electronic Systems, Adelaide Australia, 1, pp. 21-23.

Solomon, S., Nguyen, N., Liebowitz, J. and Agresti, W. (2006), “Using Data Mining to Improve Traffic Safety Programs,” Industrial Management & Data Systems, 106(5), pp. 621-643.

Stone, B. (1984), Successful Direct Marketing Methods (3rd ed.), Lincolnwood, IL: NTC Publishing.

Stone, B. (1995), Successful Direct Marketing Methods, Lincolnwood, IL: NTC Business Books.

Swift, R.S. (2001), Accelerating Customer Relationship: Using CRM and Relationship Technologies, Publisher: Prentice-Hall.

Thompson, B. and Sims, D. (2002), “CRM Improving Demand Chain Intelligence for Competitive Advantage,” Business Week, 3804, pp. 75-82.

Tiwana, A. (2000), The Essential Guide to Knowledge Management: e-Business and CRM Applications, Upper Saddle River, NJ: Prentice Hall.

Vandermerwe, S. (2000), “How Increasing Value to Customer Improves Business Results,” Sloan Management Review, 42(1), pp. 27-37.

Vesanto, J. and Alhoniemi, E. (2000), “Clustering of the Self-Organizing Map,” IEEE Transaction on Neural Networks, 11(3), pp. 586-600.

Woodruf, R. B.(1997), “Customer Value: the Next Source for Competitive Advantage,” Journal of the Academy of Marketing Science, 25(2), pp. 139-153.

Zeithaml, V. A. (1998), “Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence,” Journal of Marketing, 52(3), pp. 2-22.

三、網路資源
Unite for Children (2011), Convention on the Rights of the Child。線上檢索日期:2011年7月11日。網址:http://www.unicef.org/crc/

行政院衛生署雙和醫院(2011)。雙和醫院兒童牙科簡介。線上檢索日期:2011年7月11日。網址:http://www.shh.org.tw/UI/B/B10210.aspx?id=4046

連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔