臺鐵會員制再深化－利用資料探勘技術訂定忠誠計畫規則

Mining TRA's Transaction Data for Loyalty Program Rules

陳民祐(Min-Yu Chen)；王建富(Jiana-Fu Wang)

忠誠計畫 ； 資料探勘 ； 車票預約 ； 鐵路 ； 忠誠矩陣 ； Loyalty program ； Data mining ； Ticket reservation ； Railway ； Loyalty matrix

運輸計劃季刊

42卷3期（2013 / 09 / 30）

221 - 245

繁體中文

積點制的會員回饋計畫，或所謂的忠誠計畫，已廣泛地被許多行業用來維繫與顧客間的關係，甚至作為刺激顧客消費與公司獲利的工具。由於會員系統需要資源的持續投入，因此有必要針對會員的加入進行篩選，甚至分級提供不同程度的服務，以激勵及鎖定會員的消費。本研究利用臺鐵局電話及網路訂票的巨量資料庫，以RFM及延伸變數進行集群分析與建立決策樹模型，並輔以忠誠度矩陣加以評估，結果發現利用三個月訂票資料所建置的顧客價值分類準則即可達到94%的預測準確率，同時本研究並運用前述準則篩選出高價值旅客及具潛力的旅客作為臺鐵局邀請加入會員系統的對象，也建議藉由降低積點兌換門檻、提供多樣化的獎品以及採取層級式的會員架構等措施來強化會員積點制度的功效。

Point-based customer loyalty program has been extensively adopted in many industries to maintain customer relationships, even to stimulate repeat purchases from customers and to obtain more profits for companies. Due to the need to continuously invest resources in loyalty programs, companies should only allow profitable customers to join the programs. This study evaluates the ticket reservation data of Taiwan Railways Administration with RFM and extended variables using clustering and decision tree techniques and loyalty matrix concepts to identify customer values. Through this research, we are able to provide 94% classification accuracy on our decision tree model employing three-month ticker reservation data. Also, high-value and potential high-value customers are identified via the classification rules for member-recruiting. In the end, lowering the thresholds of redeeming points, offering diversified rewards and using tier membership structure are suggested to enhance the functions of the loyalty program.

1. 林祥生、劉益豪(2008)。應用資料採礦探討國際線航空旅客之線上購票行為。運輸計劃季刊，37(2)，197-235。
   連結：
2. Tyler, T., "Loyalty Programs: Making Sure You Invest in Only the Right Customers", www.genroe.com, 2010.
3. Porter, S.,“Big Data Changes the Marketing World Forever", http//www.businessreviewaustralia.com, 2012.
4. Oakes, G., "What Big Data Means to Marketers, and How to Handle It", http://www.marketingmagazine.co.uk., 2012.
5. 臺灣鐵路管理局，「臺鐵統計資訊」， http://www.railway.gov.tw/tw/CP.aspx?sn=7460&n=6886/，民國102年。
6. Dachis, J.,“Big Data is the Future of Marketing", http://www.businessinsider.com , 2012.
7. 臺灣鐵路管理局，「各站客貨運起迄量」，https://www.railway.gov.tw/. 民國102 年。
8. 臺灣鐵路管理局，「臺鐵會員服務系統」， https://members.railway.gov.tw/，民國102 年。
9. Aggelis, V.,Christodoulakis, D.(2005).Customer Clustering Using RFM Analysis.Proceedings of the 9th WSEAS International Conference on Computers,Athens, Greece:
10. Berman, B.(2006).Developing an Effective Customer Loyalty Program.California Management Review,49(1),123-148.
11. Dorotic, M.,Bijmolt, T. H. A.,Verhoef, P. C.(2012).Loyalty Programmes: Current Knowledge and Research Directions.International Journal of Management Reviews,14(3),217-237.
12. Dowling, G. R.(2002).Customer Relationship Management: In B2C Markets, Often Less Is More.California Management Review,44(3),87-104.
13. Ferguson, R.,Hlavinka, K.(2007).The COLLOQUY Loyalty Marketing Census: Sizing Up the US Loyalty Marketing Industry.Journal of Consumer Marketing,24(5),313-321.
14. Halkidi, M.,Batistakis, Y.,Vazirgiannis, M.(2001).On Clustering Validation Techniques.Journal of Intelligent Information Systems,17(2),107-145.
15. Hughes, A. M.(1994).Strategic Database Marketing: The Masterplan for starting and Managing a Profitable, Customer-based Marketing Program.Chicago:Probus Publishing.
16. Jain, A. K.(2010).Data Clustering: 50 Years Beyond K-means.Pattern Recognition Letters,31(8),651-666.
17. Kadar, M.,Kotanko, B.(2001).Designing Loyalty Programs to Enhance Value Growth.Mercer on Transport and Travel,8(2),28-33.
18. Kohonen, T.(1990).The Self-Organizing Map.Proceedings of the IEEE,78(9),1464-1480.
19. Kuo, R.,An, Y.,Wang, H.,Chung, W.(2006).Integration of Self-Organizing Feature Maps Neural Network and Genetic K-means Algorithm for Market Segmentation.Expert Systems with Applications,30(2),313-324.
20. Lawrence, R. L.,Wright, A.(2001).Rule-Based Classification Systems Using Classification and Regression Tree (CART) Analysis.Photogrammetric Engineering and Remote Sensing,67(10),1137-1142.
21. Liu, Y.(2007).The Long Term Impact of Loyalty Programs on Consumer Purchase Behavior and Loyalty.Journal of Marketing,71,19-35.
22. Mangiameli, P.,Chen, S. K.,West, D.(1996).A Comparison of SOM Neural Network and Hierarchical Clustering Methods.European Journal of Operational Research,93(2),402-417.
23. Marcus, C.(1999).A Practical yet Meaningful Approach to Customer Segmentation.Journal of Customer Marketing,15(5),494-504.
24. McCarty, J. A.,Hastak, M.(2007).Segmentation Approaches in Data-Mining: A Comparison of RFM, CHAID, and Logistic Regression.Journal of Business Research,60(6),656-662.
25. Mooi, E.,Sarstedt, M.(2011).A Concise Guide to Market Research.New York:Springer.
26. Nunes, J. C.,Drèze, X.(2006).Your Loyalty Program is Betraying You.Harvard Business Review,84(4),124-131.
27. Oracle(2005).Ensuring Customer Loyalty: Designing Next-Generation Loyalty Program.CA, USA:Oracle Corporation.
28. Reinartz, W.,Kumar, V.(2002).The Mismanagement of Customer Loyalty.Harvard Business Review,80(7),86-94.
29. Sharma, S.(1996).Applied Multivariate Techniques.New York:John Wiley & Sons, Inc..
30. Sharp, B.,Sbarp, A.(1997).Loyalty Programs and Their Impact on Repeat-Purchase Loyalty Patterns.International Journal of Research in Marketing,14,473-486.
31. Stauss, B.,Schmidt, M.,Schoele, A.(2005).Customer Frustration in Loyalty Programs.International Journal of Service Industry Management,16(3),229-252.
32. Steinley, D.(2006).K-means Clustering: A Half-Century Synthesis.British Journal of Mathematical and Statistical Psychology,59(1),1-34.
33. Van Laerhoven, K.(2001).Combining the Self-Organizing Map and K-means Clustering for On-line Classification of Sensor Data.Artificial Neural Networks
34. Vesanto, J.,Alhoniemi, E.(2000).Clustering of the Self-Organizing Map.IEEE Transactions on Neural Network,11(3),586-600.
35. 林繼國、陳佩棻(2002)。,臺北:交通部運輸研究所。
36. 廖述賢、溫志皓(2009)。資料採礦與商業智慧。臺北:雙葉書廊。