Browse > Article
http://dx.doi.org/10.7465/jkdi.2015.26.1.229

VIP-targeted CRM strategies in an open market  

Lee, Hanjun (Business School, Korea University)
Shim, Beomsoo (National Information Society Agency)
Suh, Yongmoo (Business School, Korea University)
Publication Information
Journal of the Korean Data and Information Science Society / v.26, no.1, 2015 , pp. 229-241 More about this Journal
Abstract
Nowadays, an open-market which provides sellers and consumers a cyber place for making a transaction over the Internet has emerged as a prevalent sales channel because of convenience and relatively low price it provides. However, there are few studies about CRM strategies based on VIP consumers for an open-market even though understanding VIP consumers' behaviors in open-markets is important to increase its revenue. Therefore, we propose CRM strategies targeted on VIP customers, obtained by analyzing the transaction data of VIP customers from an open-market using data mining techniques. To that end, we first defined the VIP customers in terms of recency, frequency and monetary (RFM) values. Then, we used data mining techniques to develop a model which best classifies and identifies infiluential factors customers into VIPs or non-VIPs. We also validate each of promotion types in the aspect of effectiveness and identify association rules among the types. Then, based on the findings from these experiments, we propose strategies from the perspectives of CRM dimensions for the open-market to thrive.
Keywords
Association rule; classification; CRM; data mining; open market; promotion mix; RFM; VIP;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Parvatiyar, A. and Sheth, J. N. (2001). Customer relationship management: Emerging practice, process, and discipline. Journal of Economic & Social Research, 3, 1-34.
2 Salchenberger, L. M., Cinar, E. M. and Lash, N. A. (1992). Neural networks: A new tool for predicting thrift failures. Decision Sciences, 23, 899-916.   DOI
3 Shim, B. S., Choi, K. H. and Suh, Y. M. (2012). CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns. Expert Systems with Applications, 39, 7736-7742.   DOI   ScienceOn
4 Su, C. T., Hsu, H. H. and Tsai, C. H. (2002). Knowledge mining from trained neural networks. Journal of Computer Information Systems, 42, 61-70.
5 Swift, R. S. (2001). Accelerating customer relationships: Using CRM and relationship technologies, Prentice Hall PTR, Upper Saddle River, NJ.
6 Tam, K. Y. and Kiang, M. Y. (1992). Managerial applications of neural networks: The case of bank failure predictions. Management Science, 38, 926-947.   DOI   ScienceOn
7 Yu, J. X., Ou, Y., Zhang, C. and Zhang, S. (2005). Identifying interesting visitors through web log classification. IEEE Intelligent Systems, 20, 55-59.   DOI   ScienceOn
8 Zhang, G., Hu MY. and Patuwo, B. E. (1999). Indro DC, artificial neural networks in bankruptcy prediction: General framework and cross validation analysis. European Journal of Operational Research, 116, 16-32.   DOI   ScienceOn
9 Adomavicius, G. and Tuzhilin, A. (2001). Expert-driven validation of rule-based user models in personalization applications. Data Mining and Knowledge Discovery, 5, 33-58.   DOI   ScienceOn
10 Aggarval, C. C. and Yu, P. S. (2002). Finding localized associations in market basket data. IEEE Transactions on Knowledge and Data Engineering, 14, 51-62.   DOI   ScienceOn
11 Agresti, A. (2012). Categorical data analysis, 3rd ed, John Wiley & Sons, New York.
12 Baesens, B., Verstraeten, G., Dirk, V. D. P., Michael, E. P., Kenhove, P. V. and Vanthienen, J. (2004). Bayesian network classifiers for identifying the slope of the customer-life cycle of long-life customers. European Journal of Operational Research, 156, 508-523.   DOI   ScienceOn
13 Cheng, C. H. and Chen, Y. S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert Systems with Applications, 36, 4176-4184.   DOI   ScienceOn
14 Bortiz, J. E. and Kennedy, D. B. (1995). Effectiveness of neural network types for prediction of business failure. Expert Systems with Applications, 9, 503-512.   DOI   ScienceOn
15 Bunkley, N. and Joseph, J. (2008) Pioneer in quality control, New York Times, http://www.nytimes.com/2008/03/03/business/03juran.html.
16 Changchien, S. W., Lee, C. F. and Hsu, Y. J. (2004). On-line personalized sales promotion in electronic commerce. Expert Systems with Applications, 27, 35-52.   DOI   ScienceOn
17 Dennis, C., Marsland D. and Cockett, T. (2001). Data mining for shopping centres customer knowledge management framework. Journal of Knowledge Management, 5, 368-374.   DOI   ScienceOn
18 Fletcher, D. and Goss, E. (1993). Forecasting with neural networks: An application using bankruptcy data. Information and Management, 3, 159-167
19 Hosseini, M., Anahita, M. and Mohammad, RG. (2010). Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications, 37, 5259-5264.   DOI   ScienceOn
20 Hughes, A. M. (1994). Strategic database marketing, Probus Publishing, Chicago.
21 Hwang, H., Jung T. and Suh, E. (2004). An LTV model and customer segmentation based on customer value: A case study on the wireless telecommunication industry. Expert Systems with Applications, 26, 181-188.   DOI   ScienceOn
22 Kim, Y. S. and Street, W. N. (2004). An intelligent system for customer targeting: A data mining approach. Decision Support Systems, 37, 215-28.   DOI   ScienceOn
23 Khajvanda, M. and Tarokhb, M. J. (2011) Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Procedia Computer Science, 3, 1327-1332.   DOI   ScienceOn
24 Kim, S. Y., Jung, T. S., Suh, E. H. and Hwang, H. S. (2006). Customer segmentation and strategy development based on customer lifetime value: A case study. Expert Systems with Applications, 31, 101-107.   DOI   ScienceOn
25 Kim, Y. S. (2006). Toward a successful CRM: Variable selection, sampling, and ensemble. Decision Support Systems, 41, 542-553.   DOI   ScienceOn
26 Kracklauer, A. H., Mills, D. Q. and Seifert, D. (2004). Customer management as the origin of collaborative customer relationship management. Collaborative Customer Relationship Management - Taking CRM to the Next Level, 3-6.
27 Kubat, M., Hafez A., Raghavan, VV., Lekkala, J. R. and Chen, W. K. (2003). Item set trees for targeted association querying. IEEE Transaction on Knowledge and Data Engineering, 15, 1522-1534.   DOI   ScienceOn
28 Langley, P. and Simon, H. A. (1995). Applications of machine learning and rule induction. Communication of the ACM, 38, 55-64.
29 Lau, H. C. W., Wong, C. W. Y., Hui, I. K. and Pun, K. F. (2003). Design and implementation of an integrated knowledge system. Knowledge-Based Systems, 16, 69-76.   DOI   ScienceOn
30 Newell, F. (1997). The new rules of marketing: How to use one-to-one relationship marketing to be the leader in your industry, McGraw-Hills, New York.