Journal of Information Management (정보관리연구)

Korea Institute of Science and Technology Information (한국과학기술정보연구원 과학기술정보센터)
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The Utilization of Customer Information in Korean Retail Bank

  • Kwak, Soo-Hwan (Kyungpook National University, School of Business Administration)
  • Published : 2008.06.30
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Abstract

The combination of information and technology makes dramatically increase both information quality and quantity. Almost of company utilize customer information for the purpose of increasing sales amount and profitability. The purpose of this paper is to discover customer information's utilization practices in the Korean financial industry. The case of K Bank's information analysis in the inbound and outbound marketing is provided, The customer segmentation is used for the inbound marketing by using RFM analysis. And the loan card model is used for the outbound marketing by using logit analysis.

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References

  1. Alfansi, L. and Sargeant, A. 2000. “Market segmentation in the Indonesian banking sector: the relationship between demographics and desired customer benefits.” International Journal of Bank Marketing, 18(2): 64-74. https://doi.org/10.1108/02652320010322976
  2. Ansari, S., Kohavi, R., Mason, L., and Zheng, Z. 2001. “Integrating ecommerce and data mining: architecture and challenges.” Proceedings of IEEE International Conference on Data Mining, 27-34.
  3. Burnett, J. J. and Chonko, L. B. 1984. “A segmental approach to ‘packaging’ bank products.” Journal of Retail Banking, 6(1/2): 8-17.
  4. Dick, A. S. and Basu, K. 1994. “Customer loyalty: toward an integrated conceptual framework.” Journal of the Academy of Marketing Science, 22(2): 99-113. https://doi.org/10.1177/0092070394222001
  5. Hoffman, D. L. and Novak, T. P. 2000. “How to acquire customers on the web.” Harvard Business Review, 78(3): 179-188.
  6. Lejeune, M. A. P. M. 2001. “Measuring the impact of data mining on churn management.”Electronic Networking Applications and Policy, 11(5): 375-387. https://doi.org/10.1108/10662240110410183
  7. Lim, T. S., Loh, W. Y., and Shin, Y. S. 2000. “A Comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms.” Machine Learning, 40(3): 203-229. https://doi.org/10.1023/A:1007608224229
  8. Machauer, A. and Morgner, S. 2001. “Segmentation of bank customers by expected benefits and attitudes.” International Journal of Bank Marketing, 19(1): 6-17. https://doi.org/10.1108/02652320110366472
  9. McDougall, M. and Dibb, S. 1998. “Implementing marketing segmentation strategies in UK personal financial services: problems and progress.” The Service Industries Journal, 18(2): 45-63. https://doi.org/10.1080/02642069800000018
  10. Meidan, A. 1984. Bank Marketing Management. New York: Macmillan.
  11. Harrison, T. S. 1994. “Mapping customer segmentation for personal financial services.” International Journal of Bank Marketing, 12(8): 17-25. https://doi.org/10.1108/02652329410069010
  12. Smith, W. 1956. “Product differentiation and market segmentation as alternative marketing strategies.” Journal of Marketing, 21(July): 3-8. https://doi.org/10.2307/1247695