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| Title: | 應用資料採礦技術於信用卡使用行為及市場需求
Applications of Data Mining Techniques to the Behavior of Using Credit Cards and Market Demand |
| Authors: | 游涵茵 |
| Contributors: | 鄭宇庭
游涵茵 |
| Keywords: | 信用卡
資料採礦
C5.0
CHAID
類神經網路
Credit Card
Data Mining
C5.0
CHAID
Neural Net |
| Date: | 2009 |
| Issue Date: | 2010-12-08 01:53:51 (UTC+8) |
| Abstract: | 隨著金融自由化、國際化的趨勢，加上國民所得提高、電子化的普及，使得信用卡市場蓬勃發展，國內各大銀行紛紛積極投入信用卡發卡行列。台灣的信用卡市場競爭的程度，從各發卡銀行所提供消費者的各項附加服務，如辦卡送禮、持卡免年費、失卡零風險、購物優惠…等，幾乎都已是每一張信用卡的基本配備。
隨著卡債、卡奴的事件爆發，銀行業者舊有的信用卡行銷策略已經宣告失敗，但信用卡市場背後帶來的經濟效益，仍然是不容忽視，如今，要如何增加信用卡市場的佔有率已不是銀行業者的行銷重點，高佔有率並不一定就能帶來高經濟效益。銀行業者的行銷策略應該是做好信用卡市場區隔，找出不同特性的消費族群，依消費族者選擇信用卡的考量因素擬定行銷策略，進而提升市場競爭地位。
本研究選用四種模型建置方式，分別為羅吉斯迴歸、C5.0、CHAID以及類神經網路，經由分類矩陣評估比較四種模型，其中C5.0不論是在整體預測正確率、反查率或準確度，皆是高於其它三個模型，故最後選擇C5.0此一模型。
透過C5.0共獲得七項影響「是否有使用信用卡」之相關變數，其中「是否有出國旅行」、「經濟來源是否為自己」、「性別」、「是否畢業後找工作」、「是否有使用網路消費」、「認同環保意識」、「是否有投資或買保險」，此七項變數對使用信用卡消費具較大影響力，最後本研究會針對這些變數再給與發卡銀行建議。
【關鍵字】信用卡、資料採礦、C5.0、CHAID、類神經網路
As the trend of financial liberalization and globalization and also the popularization of electronic business and the increase of domestic income, the credit card market has bloomed vigorously then ever, banks are urging on developing credit card markets. All those additional service of every bank could be seen as a clue to know the competitiveness in Taiwan, such as free gift, free annual fee, zero risk of losing cards, shopping discount…etc., and those service almost become a basic equipment of every credit card.
With credit debt and credit card slaves increasing, bank’s former marketing strategies have failed. The economic benefits of credit card market still are not ignored. Today, how to increase market share of credit card is not the key point of bank’s marketing strategy. There is not necessary that high market share can bring high economic benefits. In order to follow this trend, the study aims to discover the corn factors of possessing credit cards through the application of Clementine 12.0 software.
Since Decision Tree-C5.0 is excellent in the forecast accuracy and validity as compared to Logistic Regression, Decision Tree-CHAID and Neural Net were adopted in this research. Through using Decision Tree-C5.0, this study identified seven factors that have greater impact on using credit cards and they are”Whether respondent travel abroad”,“Is the source of income making by yourself”,“Gender”,“Do respondent look for jobs after graduating from school”,“Do respondent buy something on the internet”,“Approve the environmental awareness”.This research will chiefly use these seven factors to provide the marketing portfolio strategy recommendations for banks.
Keywords：Credit Card, Data Mining, C5.0, CHAID, Neural Net |
| Reference: | 中文參考文獻
1.王嘉祺(2004)，「使用信用卡循環信用之持卡人特性之實證研究」，國立雲林科技大學財務金融系碩士論文。
2.王濟川、郭志剛(2003)，「Logistic迴歸模型－方法及應用」，五南書局。
3.甘齡珺(2009)，「應用資料採礦於自行車產業之行銷組合策略分析」，國立政治大學企業管理硏究所碩士論文。
4.吳明隆(2003)，「SPSS統計應用學習實務」，知誠數位科技。
5.吳豐志(1994)，「信用卡市場區隔與定位之研究」，東海大學企業管理研究所碩士論文。
6.呂春榮(1993)，「信用卡使用動機之研究」，國立成功大學企業管理研究所碩士論文。
7.李銘博(1998)，「臺灣信用卡外國發卡機構進入策略之探討」，國立政治大學企業管理硏究所碩士論文。
8.周紹賢(1995)，「利用信用卡刷出財富」，歐瑞文化事業有限公司。
9.周紹賢(1998)，「信用卡行銷爭霸」，遠流出版社。
10.陳宜芝，大成報，「一張晶片卡食衣住行全包」，第14版，1998.10.12。
11.張玉琳(1997)，「消費市場之關係行銷硏究 : 以信用卡為例」，國立政治大學企業管理硏究所碩士論文。
12.葉玉梅(1983)，「金融機構信用卡消費行為之硏究」，國立政治大學企業管理硏究碩士論文。
13.管理雜誌，第287期，1998.09，p46-48。
14.廖仁傑(2005)，「信用卡業務信用評分制度與模型之有效性研究」，國立中央大學財務金融研究所碩士論文。
15.蔡政良(2006)，「銀行信用卡競爭地位之分析」，國立成功大學統計學研究所碩士論文。
16.謝邦昌、鄭宇庭與蘇志雄(2009)，「Data Mining概述」，中華資料採礦協會。
17.蕭潮聲(1974)，「消費者對銀行信用卡態度之研究」，國立政治大學企業管理硏究所碩士論文。
英文參考文獻
1.Breiman L., J.H. Friedman, R.A. Olshen, and C.J. Stone (1984), “Classification and Regression Tree.” Wadsworth, California.
2.Demby(1974), “Psychographics and Form Where It Comes in Lifestyle and Psychographics.” Ed. William D.Wells, Chicago: AMA.
3.Engel, J. F., Blackwell R. D., and Miniard P. W. (1995),Consumer Behavior,8th ed. New York: Dryden Press.
4.Hunt, E.B. (1962). “Concept learning: An information processing problem.” New York: Wiley.
5.Hunt, E.B., J. Marin, and P.J. Stone, (1966). “Experiments in induction.”New York: Academic Press.
6.Kotler, P. (1992), ”Marketing Management, Analysis, Planning, Implementation and Control, 3th ed.”, Prentice-Hall, Inc, New York, NY.
7.Lazer, W. (1963),”Toward Scientific Marking.”Chicago: AMA.
8.Loh, Wei-Yin Yu-Shan Shih, “Split selection methods for classification trees.” Statistica Sinica, Vol. 7, 1997, pp.815-840.
9.MacQueen, J.B. (1967). “Some Methods for classification and Analysis of Multivariate Observations”, Berkeley, University of California Press, pp.281-297.
10.Perreault, W. D. and H. C. Barksdale, (1980) “A model-free approach for analysis of complex contingency data in survey research”, Journal of Marketing Research, Vol. 17 (November), pp. 503-515.
11.Plummer, Joseph T.(1974)”The Concept and Application of Life Style Segmentation.”Journal of Marketing ,pp.25-67.
12.Quinlan, J.R. (1979), “Discovering rules from large collections of examples: a case study.” In Michie, D., editor, Expert Systems in the Microelectronic Age. Edinburgh University Press, Edinburgh Scotland.
13.Steenackers , A. and M.J. Goovaerts(1989), “ A Credit Scoring Model for Personal Loans.” Insurance Mathematics Economics, pp.31-34. |
| Description: | 碩士
國立政治大學
統計研究所
97354009
98 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0097354009 |
| Data Type: | thesis |
| Appears in Collections: | [統計學系] 學位論文
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