Abstract
Customer management is one of the important aspects in retail business. It is vital for the retailers to adopt different methodologies by which high valued customers can be identified, in order to perform suitable target marketing effectively. In this paper, a novel model is proposed for classifying retail customers into different categories based on purchasing behavior of customers. A class label for each transaction is determined based upon customer profit value (CPV), and a classifier model is build for predicting different categories of customers. The classifier model is constructed using SPSS tool for market basket data. Finally, the classifier model is verified with test data set, and used for predicting customer category. The extracted information is helpful for planning customer retention and providing personalized customer services by understanding their needs, preferences and behavior.
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References
Kim, J., Suh, E., Hwang, H.: A model for evaluating the effectiveness of CRM using the balanced scorecard. Journal of Interactive Marketing 17(2), 5–19 (2003)
Duboff, R.S.: Marketing to maximize profitability. The Journal of Business Strategy 13(6), 10–13 (1992)
Gloy, B.A., Akridge, J.T., Preckel, P.V.: Customer lifetime value: An application in the rural petroleum market. Agribusiness 13(3), 335–347 (1997)
Rosset, S., Neumann, E., Eick, U., Vatnik, N., Idan, Y.: Customer lifetime value modeling and its use for customer retention planning. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 332–340 (2002)
Verhoef, P.C., Donkers, B.: Predicting customer potential value an application in the insurance industry. Decision Support Systems 32, 189–19 (2001)
Fredrick, F.R.: The loyalty effect: The hidden force behind growth, profits and lasting value. Harvard Business School Press, Boston (1996)
Kotler, P., Armstrong, G.: Principles of Marketing, 7th edn. Prentice Hill, Englewood Cliff (1996)
Dwyer, F.R.: Customer lifetime valuation to support marketing decision making. Journal Interactive Marketing 11(4), 6–13 (1997)
Hwang, H., Jung, T., Suh, E.: 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 (2004)
Kim, S., Jung, T., Suh, E., Hwang, H.: Customer segmentation and strategy development based on customer lifetime value: A case study. Expert Systems with Applications 31, 101–107 (2006)
Lao, G., Zhang, Z.: A three-dimensional customer classification model based on knowledge discovery and empirical study. In: Chang, K.C.-C., Wang, W., Chen, L., Ellis, C.A., Hsu, C.-H., Tsoi, A.C., Wang, H. (eds.) APWeb/WAIM 2007. LNCS, vol. 4537, pp. 510–515. Springer, Heidelberg (2007)
Jiaying, Q., Suh, H.: Assessing modeling and decision making of customer value, Beijing University of Posts and Telecommunication Press (2005)
Yao, H., Hamilton, H.J., Butz, C.J.: A Foundational approach to mining Itemset Utilities From Databases. In: Third SIAM International Conference on Data Mining, pp. 482–486 (2004)
Hunt, E.B., Marin, J., Stone, P.J.: Experiments in induction. Academic Press, New York (1966)
Breiman, L., Friedman, J., Olshen, L., Stone, J.: Classification and Regression trees. Wadsworth Statistics/Probability series. CRC Press, Boca Raton (1984)
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Ramaraju, C., Savarimuthu, N. (2011). A Classification Model for Customer Segmentation. In: Abraham, A., Lloret Mauri, J., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22709-7_64
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DOI: https://doi.org/10.1007/978-3-642-22709-7_64
Publisher Name: Springer, Berlin, Heidelberg
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