資料採礦分類模式之應用－以人工類神經網路建構中興健身俱樂部之顧客流失模型為例

The Application of Classification Model in Data Mining-The Empirical Result of Churn Model in SUPERSHAPE HEALTH CLUB Using Artificial Neural Networks

10.6338/JDA.200606_1(3).0003

陳麒文(Chi-Wen Chen)

顧客流失 ； 資料採礦 ； 人工類神經網路 ； 健身俱樂部 ； churn ； data mining ； artificial neural networks ； health club

Journal of Data Analysis

1卷3期（2006 / 06 / 01）

47 - 62

繁體中文

本研究的主要目的在於建立與評估人工類神經網路應用於健身俱樂部之顧客流失模型，資料來源由中興健身俱樂部所提供，共計1,287筆，在剔除資料不全與資料內容不合理的資料後，共有1,152筆。而為了驗證模型的適用性，本研究之研究架構分為模型訓練階段與模型測試階段進行，並以80：20的比例隨機抽出訓練模型的樣本及測試模型的樣本個數；在第一階段之模型訓練階段，主要在建立人工類神經之顧客流失模型；而第二階段之模型測試階段，主要是利用先前建立好之模型，來預測未來可能流失顧客之正確率。研究分析工具為Vesta公司所出版的Qnet軟體，結果發現，利用人工類神經網路所建構之顧客流失模型，其整體正確判別率為84.78%，而流失顧客之重要特徵為女性、年齡介於30～35歲、會齡為1年以上～2年、入會金額為0元、月費金額為月繳2,500元、居住在臺北市內、且以現金付款方式繳費的會員。

The purpose of this study was to establish and evaluate the artificial neural networks applying to churn model of health clubs. The database, totally 1,287 records, was provided by SUPERSHAPE HEALTH CLUB. After deleting the missing data and unreasonable data, there were 1,152 records totally. In order to verify the applicability of churn model, the research construction of this study was broken down as two stages of model training and model testing, and the samples of the training model and testing model were extracted randomly with the proportion of 80:20. In the model training stage of first, the churn model was built by artificial neural networks primarily. In the model testing stage of second, the correct rate of future churn customers was predicted by the model built previously. The analysis tool was Qnet published by Vesta Corp. The result found that whole correct classification rate was 84.78% in this churn model using artificial neural networks, and the significant characteristics of churn customer were the members of female, the age between 30-year-old and 35-year-old, the participation year of 1 year to 2 years, the entrance fee of 0 dollar, the monthly fee of 2,500 dollars, living in Taipei city, and the method of payment of cash.

1. (2001).The Technology Review Ten.MIT Technology Review
2. Berry, M. J. A.,Linoff, G. S.(1997).Data mining techniques: For marketing, sales, and customer support.New York:Wiley Computer.
3. Berson, A.,Smith, S.,Thearling, K.(2000).Building data mining applications for CRM.New York:McGraw-Hill.
4. Cabena, P.,Hadjinaian, P.,Stadler, R.,Verhees, J.,Zanasi, A.(1997).Discovering data mining from concept to implementation.New Jersey:Prentice Hall PTR.
5. Cios, K.,Pedrycz, W.,Swiniarski, R.(1998).Data mining methods for knowledge discovery.Boston:Kluwer Academic Publishers.
6. Fayyad, U. M.,Piatetsky-Shapiro, G.,,Smyth, P.(1996).From data mining to knowledge discovery in databases.AI Mag.,17,37-54.
7. Fish, K. E.,Barnes, J. H.,Aiken, M. W.(1995).Artificial neural networks: A new methodology for industrial market segmentation.Industrial Marketing Management,24,431-438.
8. Freeman, J. A.,Skapura, D. M.(1992).Neural networks algorithms, applications, and programming techniques.MA:Addison-Wesley Publishing Company.
9. Guape, F. H.,Owrang, M. M.(1995).Database mining discovering new knowledge and cooperative advantage.Information Systems Management,12,26-31.
10. Khirallah, K.(1999).Should retail banks race toward the one-to-one future?.Bank Technology News,12(4),41-43.
11. Lee, H.,Jo, H.,Han, I.(1997).Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis.Expert Systems With Applications,13,97-108.
12. Matheus, C. J.,Chan, P. K.,Piatetsky-Shapiro, G.(2000).Systems for knowledge discovery in databases.IEEE trans. On Knowledge Discovery and Data Engineering,5(6),903-913.
13. Newell, F.(1993).Loyalty.com: Customer relationship management in the new era of internet marketing.New York:McGraw-Hill.
14. Peppers, D.,Rogers, M.(1993).The future of one to one: Building business relationships one customer at a time.New York:Currency Doubleday.
15. Piatetsky-Shapiro, G.(1993).An overview of knowledge discovery in database: Recent progress and challenges.Rough Sets, Fuzzy Sets and Knowledge Discovery-Proceedings of the International Workshop on Rough Sets and Knowledge Discovery
16. Stotlar, D. K.(1999)。關係行銷法在體適能產業的運用。1999健康體能與有氧運動大會，臺北:
17. Vellido, A.,Lisboa, P. J. G.,Vaughan, J.(1999).Neural networks in business: A survey of applications (1992-1998).Expert Systems With Applications,17,51-70.
18. Vesta Services(1998).Qnet97-neural network modeling for windows 95/98/NT.Winnetka, IL:Vesta Services, Inc..
19. Walters, S.(1994).Customer service: A how-to-do-it manual for librarians.New York:Neal-Schuman Publishing.
20. Werbos, P. J.(1974).MA,Harvard University.
21. Zhang, G.,Patuwo, B. E.,Hu, M. Y.(1998).Forecasting with artificial neural networks: The state of the art.International Journal of Forecasting,14,35-62.
22. 王信勝(2001)。碩士論文(碩士論文)。輔仁大學資訊管理學系碩士班。
23. 江世傑(2001)。碩士論文(碩士論文)。國立成功大學工業管理學系碩士班。
24. 吳國禎(2000)。碩士論文(碩士論文)。中原大學醫學工程學系碩士班。
25. 呂奇傑(2001)。碩士論文(碩士論文)。輔仁大學應用統計學研究所。
26. 周慶華(2001)。碩士論文(碩士論文)。輔仁大學金融研究所。
27. 邱志洲、李天行、周宇超、呂奇傑(2002)。整合鑑別分析與類神經網路在資料探勘上之應用。工業工程學刊，19(2)，9-22。
28. 夏載(2001)。剖析資料採礦在顧客關係管理中的應用。電子化企業經理人報告，20，71-75。
29. 張政一(2001)。碩士論文(碩士論文)。中國文化大學國際企業管理研究所。
30. 許峻源(2001)。碩士論文(碩士論文)。輔仁大學應用統計研究所。
31. 陳麒文、陳鴻雁(2001)。Data Mining於運動行銷上之應用。「2001年體育科技與資訊」研討會，臺北:
32. 彭慧雯(2001)。碩士論文(碩士論文)。國立臺北科技大學生產系統工程與管理研究所。
33. 黃雅蘭(1992)。碩士論文(碩士論文)。國立臺灣科技大學資訊管理系碩士班。
34. 葉怡成(2001)。類神經網路模式應用與實作。臺北:儒林書局。
35. 劉嘉鴻(2001)。碩士論文(碩士論文)。輔仁大學金融研究所。
36. 數博網(2001)。歹年冬資料採礦度小月。動腦雜誌，304，44-46。
37. 蔡裕春(2001)。碩士論文(碩士論文)。輔仁大學應用統計學研究所。
38. 魯振華(2001)。碩士論文(碩士論文)。逢甲大學保險學系碩士班。
39. 賴欣儀(2000)。碩士論文(碩士論文)。國立中山大學企業管理學系研究所。
40. 謝邦昌(2001)。資料採礦入門及應用─從統計技術看資料採礦。臺北:資商訊息顧問股份有限公司。
41. 羅家德(2001)。網際網路關係行銷。臺北:聯經出版社。
42. 蘇文娟(2000)。碩士論文(碩士論文)。國立東華大學國際經濟研究所。