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題名:應用資料探勘於顧客回應模式之研究 ─以國內A壽險公司為例
作者:高淑珍
作者(外文):Shu-Chen Kao
校院名稱:國立成功大學
系所名稱:企業管理學系碩博士班
指導教授:張海青
學位類別:博士
出版日期:2004
主題關鍵詞:顧客回應模式資料探勘目標行銷ID3customer response modeldata miningtarget marketingID3
原始連結:連回原系統網址new window
相關次數:
  • 被引用次數被引用次數:期刊(6) 博士論文(1) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:6
  • 共同引用共同引用:0
  • 點閱點閱:37
隨著全球性經濟的興起使得產業競爭更加劇烈,因此企業必須採用目標行銷(target marketing)以協助企業正確快速地鎖定行銷推廣的顧客族群,而客戶回應模式(customer response model)因為兼具鎖定以及預測顧客這兩項特性,因而逐漸在行銷推廣領域受到重視。因此本研究以擁有大量顧客交易資料且產業競爭激烈的壽險業為例,試著以資料探勘技術應用發展出壽險公司之顧客回應模式,希望能透過資料探勘萃取出隱性且具價值的壽險行銷知識。
整個研究在經過與A壽險公司的初步訪談,並蒐集188464筆該公司之投保交易資料,經資料間斷化之後以廣為使用的ID3 決策樹歸納演算法加以探勘,經篩選之後得到943項具代表性的顧客回應之預測性規則,並得到81%的預測準確率。此外,本研究將探勘所得到的規則再深入以兩種不同的角度加以分析歸納以得到更為精煉的知識,其中分析一主要是針對每項規則的交易記錄支持筆數和支持度,探索熱賣的險別分類之顧客特性;至於分析二則是以規則的屬性與險別分類施以交叉分析,以獲知同一屬性的分級差異對於壽險類別的分佈是否具有差異性,其目的都是希望能夠協助壽險業務人員更順利的進行目標行銷。
最後,本研究針對A壽險公司之高階主管進行第二次深度訪談,訪談結果除了驗證此回應模式所獲得的知識與實務經驗吻合之外,也發現此模式的確有助於公司內部累積與傳播壽險顧客的回應知識,而未來顧客回應模式在實務上應採漸進式的導入方式為佳。另外,在訪談的過程當中也發現透過此一顧客回應模式的討論,個案公司除了逐漸熟悉資料探勘的意義之外,此顧客回應模式也有助於該公司將決策型式逐漸從經驗導向轉為資訊導向。因此,應用資料探勘技術於顧客回應模式確實是壽險公司取得競爭優勢的另一項新選擇,而資料探勘也不只是技術層面的議題,其在應用層面上也是很有價值的。
It has been seen that the modern marketing paradigm has been rapidly shifting and business has used to apply target marketing to capture the right customers in promotion activity. However, the customer response model, regarded as the tool for targeting and prediction, is the most important task in marketing promotion. This research proposes a data mining (DM) based customer response model for insurance industry to help in finding unobvious but valuable promotion knowledge to support making marketing related decisions.
First, we visited a leading insurance company (denoted by A), one of the most popular insurance companies in Taiwan, to frame the research focus. There were 188464 transaction records provided by A company. Of the collected data, two to third was used as a dataset being mined while the remainder as a test dataset. The ID3 mining algorithm was utilized to derive decision rules and obtained 943 qualified rules in total. The accuracy of the proposed model was 81%. To capture the important implication of the knowledge, the research analyzed the obtained rules in two directions including the level of supports and degree of conditions. The former focused mainly on the amount of supports and degree of conditions for the obtained rules to analyze the product categories with respect to the customer characteristics. The latter carried primarily out the relationships between different degree of condition and product categories.
The research then conducted the second visit of A in order to validate the obtained knowledge in practice. The results indicated that the customer response model was able to aim at finding and diffusing the insurance marketing knowledge. It was also found that the proposed customer response model with the DM mechanism was decision-supportable based on the opinion of executive manager. Moreover, the proposed model would play a key role in changing the decision making style from experience-oriented to information-oriented. Other research findings were provided and managerial implications addressed in this research also.
中文部份:
陳乃菁,”21世紀-顧客導向的世紀首重CRM規畫”,寶來證券,2001 年2月
鄭國華,”中菲電腦訪談報告”,復華證券,2000年8月
“產業評析-分析型顧客關係管理解決方案介紹 (上)”,2300科技資訊網,2001年10月, http://www.2300.com.tw
林東清,”顧客關係管理(CRM)研究的一些相關理論模式與重要議題”,資訊管理學報,第九卷專刊,2002年new window
林維娟,”探討企業M化之延展暨精奇科技企業成功案例分享-壽險業/汽車業”,2001年6月4日,鉅享網
李昇暾,”以資料採礦深化顧客關係管理”,電子化企業經理人報告─企業電子化轉型與再造,第17期,頁37-42,2001
吳欣穎,企業導入顧客關係管理之研究,國立台北大學企業管理學系碩士論文,民89
史博言, 1999年度台灣「顧客關係管理」運用現狀調查報告, ARC遠擎管理顧問公司
英文部份:
Adriaans, P. & Zantinge, D. (1996), “Data mining”, Reading Mass.: Addison- Wesley.
Ahn, Jae-Hyeon & Ezawa, Kazuo J. (1997), “Decision support for real-time telemarketing operations through Bayesian network learning”, Decision Support Systems, Vol. 21, Issue: 1, pp.17-27
Alexandra, J. C. (2003), “Creating customer knowledge competence: managing customer relationship management programs strategically”, Industrial Marketing Management, 32(5), pp. 375-383
Baesens, B., Viaene, S., Poel, D. V, Vanthienen, J. & Dedeme, G. (2002), “Bayesian neural network learning for repeat purchase modeling in direct marketing”, European Journal of Operational Research, 138(1), pp. 191-211

Bansal, K., Vadhavkar, S. & Gupta, A. (1998), “Neural networks based forecasting techniques for inventory control applications”, Data Mining and Knowledge Discovery, 2, pp.97-102
Ben, A. M. & Lerman, S. R. (1985), “Discrete choice analysis”, Cambridge, MA: MIT Press
Bennett, R. & Gabriel, H. (1999), “Organizational factors and knowledge amanagement within large marketing departments: an empirical study”, Journal of Knowledge Management, Vol.3, No. 3, pp.212-225.
Berger, P. D. & Magliozzi, T. L. (1992), “The effect of sample size and proportion of buyers in the sample on the performance of list segmentation equations generated by regression analysis”, Journal of Direct Marketing, 6(1), pp.13-22
Berson, A., Smith, S. & Thearling, K. (2000), Building data mining applications for CRM, McGraw-Hill
Blattberg, R. C. & Vitale, D. (1986), “Bull’s eye targeting “, ZIP Target Marketing, February, pp.63-68
Brodley, C. E. & Friedl, M.a. (1996), “Identifying and eliminating mislabeled training instances”, Proceeding of Thirteenth National Conference on Artificial Intelligence, AAAI Press
Chae, Y. M., Ho, A. H., Cho, K. W., Lee, D. H. & Ji, S. H. (2001), “Data mining approach to policy analysis in a health insurance domain”, Intern. J. of Medical Informatics, 62(2-3), pp. 103-111
Changchien, S. W. & Lu, T. C. (2001), “Mining association rules procedure to support on-line recommendation by customers and products fragmentation”, Expert Systems with Applications, 20(4), pp. 325-335
Chiu, C. (2002), “A case-based customer classification approach for direct marketing”, Expert Systems with Applications, 22(2), pp.163-168
Civi, Emin (2000), “Knowledge management as a competitive asset: a review”, Marketing Intelligence and Planning, Vol. 18, No.4, pp.166-174.
Coenen, F., Swinnen, G., Vanhoof, K. & Wets, G. (2000), “The improvement of response modeling: combining rule-induction and case-based reasoning”, Expert Systems with Application, Vol. 18, Issue: 4, pp. 307-313
Delesie, L. & Croes, L. (2000), “Operations research and knowledge discovery: a data mining method applied to health care management”, Intern. Trans. In Op. Res., 7(2), pp. 159-170
Desmet, P. & Renaudin, V. (1998), “Estimation of product category sales responsiveness to allocated shelf space”, Intern. J. of Research in Marketing, 15(5), pp.443-457
Domingos, P. (1997), “Knowledge discovery vie multiple methods”, IDA, Amsterdam: Elsevier
Domingos, P. (1998), “Multimodal inductive reasoning: combining rule-based and case-based learning”, Multimodal reasoning, AAAI Press
Fayyad, U. M., & Uthurusamy, R., (1995), Proceedings of the First International Conference on Knowledge Discovery and Data Mining, Menlo Park, CA: AAAI Press.
Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P. & Uthurusamy, R., (1996), Advances in knowledge discovery and data mining, Menlo Park, CA: AAAI Press.
Forcht, K. A. & Cochran, K. (1999), “Using data mining and datawarehousing techniques”, Industrial Management & Data Systems, 99(5), pp.189-196
Fox, B. R. (2000), “Technology: the new weapon in the war on insurance fraud”, Defense Counsel Journal, April, pp.237-244
Ha, S. H. & 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
Hoffman, T. (1999), “Finding a rich niche”, Computerworld, February, pp. 44
Information Discovery, Inc. “A characterization of data mining technologies and processes”, Information Discovery Inc. White Paper.
Jiang, J., Berry, M. W., Donato, J. M., Ostrouchov, G. & Grady, N. (1999), “Mining consumer product data via latent semantic indexing”, Intelligent Data Analysis, 3(5), pp.377-398
Kahan, R. (1998), “Using database marketing techniques to enhance your one-to-one marketing initiatives”, Journal of Consumer Marketing, 15(5), pp.491-493
Kalakota, R. & Marcia, R. (1999), E-business: Roadmap for success, 1 th ed., U.S.A.: Mary T. O’Brien
Katharina, D. C. Stark & Dirk, U. Pfeiffer (1999), “The application of non-parametric techniques to solve classification problems in complex data sets in veterinary epidemiology- an example”, Intelligent Data Analyusis, 3(1), pp.23-35
Kauderer S. I. & Kuehl A. J. (2001), “Adding value with technology”, Best’s Review, October, pp. 130
Kotler, P. (1999), “Marketing management”, 10th edition, Prentice Hall
Kuo, R. J. (2001), “A sales forecasting system based on fuzzy neural networks with initial weights generated by genetic algorithm”, European Journal of Operational Research, 129(3), pp. 496-517
Lach, J. (1999), “Data mining digs in”, American Demographics, July, pp. 38-45

Lauterborn, R., (1990), “New marketing litancy: 4P’s passe; C-Words take over”, Advertising Age, October 1, pp.26
Lee, J. H. & Kim, Y. G. (2001),”A stage model of organizational knowledge management: a latent content analysis”, Expert Systems with Applications, Vol. 20, issue: , pp.299-311.
Levin, N., Zahavi, J & Olitsky, M. (1995),”AMOS-A probability-driven, customer-oriented decision support system for target marketing of solo mailing”, European Journal of Operational Research, 87(3), pp.708-721
Mak, B. & Munakata, T. (2002), “Rule extraction from expert heuristics: a comparative study of rough sets with neural networks and ID3”, European Journal of Operational Research, 136(1), pp.212-229
Magidson, J. (1988), “Improved statistical techniques for response modeling”, Journal of Direct Marketing, 2(3), pp.6-18
Nash, E. L. (1993), Database marketing-the ultimate marketing tool, New York: McGraw-Hill
Ohmann, C., Moustakis, V., Yang, Q. & Lang, K. (1996), “Evaluation of automatic knowledge acquisition techniques in the diagnosis of acute abdominal pain”, Artificial Intelligence in Medicine, 8(1), pp.23-36
Pearce, M. R. (1997), Succeeding with micro-marketing, Ivey Business Quarterly, Autumn, pp.69-72
Peppers, D. & Rogers, M. (1999), The one to one manager: real-world lessons in customer relationship management, Doubleday, New York, 1999
Pine, J. (1999), “Mass customization: the new frontier in business competition”, Harvard Business School Press, Boston
Pilot Software. An intoroduction to data mining: discovering hidden value in your data warehouse. http:// www3.shore.net/~kht/text/dmwhite/dmwhite.htm
Pitta, D. (1998), “Marketing on-to-one and its dependence on knowledge discovery in databases”, Journal of consumer marketing, Vol. 15, No.5, pp. 468-480.
Quinlan, J. R. (1993), C4.5: Programs for machine learning, San Mateo: Morgan Kaufmann
Ratner, B. (1998), “Direct marketing models using genetic algorithms”, Journal of Targeting, Measurement and Analysis for marketing, 6(4), 1998,pp.328-342
Ruquet M. E. (2000), “Data mining challenges cited”, National Underwriter, December, pp.3,16-17
Saporito, P. (2000), “Insurers must learn to better manage client relationship”, Property & Casualty/Risk & Benefits Management, October , pp. 27, 32
Shepard, D. (1995), The new direct marketing, 2, New York: McGraw-Hill
Silverstein, C., Brin, S. & Motwani, R. (1998), “Beyond market baskets: generalizing association rules to dependence rules”, Data Mining and Knowledge Discovery, 2, pp.39-68
Sodano, A. (2000), “Leveraging CRM to build better products”, Life & Health/Financial Services Edition, June , pp. 23, 27
Suh, E. H., Noh, K. C. & Suh, C. K. (1999), “Customer list segmentation using the combined response model”, Expert Systems with Applications, 17(2), pp.89-97
Sung, N. H. & Lee, J. K. (2000), “Knowledge assisted dynamic pricing, for large-scale retailers”, Decision Support Systems, 28(4), pp.347-363
Swift, R. S. (2001), Accelerating customer relationships using CRM and relationship technologies, Prentice-Hall
Turksen, I. B. & Willson, I. A. (1995), “ A fuzzy set model for market share and preference prediction”, European Journal of Operational Research, 82(1), pp. 39-52
Trubik, E. & Smith, M. (2000), “Developing a model of customer defection in the Australian banking industry”, Managerial Auditing Journal, 15(5), pp.199-208
Van den Poel, D. & Wets, G. (1996), “Data mining for database marketing: a mail-order company application”, Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets and Machine Discovery, RSFD’96, pp. 383-389
Verhoef, P. C., Spring, P. N., Hoekstra, J. C. & Leeflang, P. S. H. (2003), “The commercial use of segmentation and predictive modeling technique for database marketing in the Netherlands”, Decision Support Systems, 34(4), pp. 471-481
Wang, P. & Baker, J. R. (1996), “Procedures to improve the house list segment tests”, Journal of Direct Marketing, 10(2), pp.24-35
Webster, F. (1992), “The changing role of marketing”, Corporation Journal of Marketing, 56, pp.1-17.
Wei, C. P. & Chiu, I. T. (2002), “Turning telecommunication call details to churn prediction: a data mining approach”, Expert Systems with Applications, 23(2), pp. 103-112
Widrow, B., Rumelhart, D. E. & Lehr, M. A. (1994), “Neural networks: applications in industry business and science”, Communications of the ACM, 37(3), pp. 93-105
Yeo, A. C., Smith, K. A., Willis, R. J. & Brooks, M. (2002), “A mathematical programming approach to optimize insurance premium pricing with a data mining framework”, The Journal of the Operational Research Society, 53(11), pp. 1197-1203
Zahavi, J. & Levin, N. (1995), “Issues and problems in applying neural computing to target marketing”, Journal of Direct Marketing, 9(3), pp. 33-45
Zahavi, J. & Levin, N. (1997), “Applying neural computing to target marketing”, Journal of Direct Marketing, 11(4), pp. 76-93
 
 
 
 
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