Abstract
We study the construction of probabilistic fuzzy target selection models for direct marketing by using an approach based on fuzzy clustering. Since fuzzy clustering is an unsupervised algorithm, the class labels are not used during the clustering step. However, this may lead to inefficient partitioning of the data space, as the clusters identified need not be well-separated, i.e. homogeneous, in terms of the class labels. Furthermore, the regions of the data space with more high-prospect customers should be explored in larger detail. In this paper, we propose to use a weighting approach to deal with these problems. Additional parameters, i.e. weight factors, associated with data points and the clusters are introduced into the fuzzy clustering algorithm. We derive the optimal update equations for the weighted fuzzy c-means algorithm. A heuristic method for estimating suitable values of the weight factors is also proposed. The benefits of our approach for the target selection models are illustrated by using data from the target selection campaigns of a large charity organization.
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References
Morwitz, V.G., Schmittlein, D.C.: Testing new direct marketing offerings: the interplay of management judgment and statistical models. Management Science 44 (1998) 610–628
Bult, J.R.: Target Selection for Direct marketing. Ph.D. thesis, Rijksuniversiteit Groningen, Groningen, the Netherlands (1993)
Bult, J.R., Wansbeek, T.J.: Optimal selection for direct mail. Marketing Science 14 (1995) 378–394
Potharst, R., Kaymak, U., Pijls, W.: Neural networks for target selection in direct marketing. In Smith, K., Gupta, J., eds.: Neural Networks in Business: techniques and applications. Idea Group Publishing, London (2002) 89–110
Zahavi, J., Levin, N.: Applying neural computing to target marketing. Journal of Direct Marketing 11 (1997) 5–22
Haughton, D., Oulabi, S.: Direct marketing modeling with CART and CHAID. Journal of Direct Marketing 7 (1993) 16–26
Pijls, W., Potharst, R., Kaymak, U.: Pattern-based target selection applied to fund raising. In Gersten, W., Vanhoof, K., eds.: DataMining for Marketing Applications, ECML/PKDD-2001 Workshop, Freiburg, Germany (2001) 15–24
Setnes, M., Kaymak, U.: Fuzzy modeling of client preference from large data sets: an application to target selection in direct marketing. IEEE Transactions on Fuzzy Systems 9 (2001) 153–163
Kaymak, U.: Fuzzy target selection using RFMvariables. In: Proceedings of Joint 9th IFSA World Congress and 20th NAFIPS International Conference, Vancouver, Canada (2001) 1038–1043
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function. Plenum Press, New York (1981)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modelling and control. IEEE Transactions on Systems, Man and Cybernetics 15 (1985) 116–132
Zadeh, L.A.: Probability measures of fuzzy events. J.Math. Anal. Appl. 23 (1968) 421–427
van den Berg, J., Kaymak, U., van den Bergh, W.M.: Fuzzy classification using probability-based rule weighting. In: Proceedings of 2002 IEEE International Conference on Fuzzy Systems, Honolulu, Hawaii (2002) 991–996
Höppner, F., Klawonn, F., Kruse, R., Runkler, T.: Fuzzy Cluster Analysis: methods for classification, data analysis and image recognition. Wiley, New York (1999)
van den Bergh, W.M., van den Berg, J., Kaymak, U.: Detecting noise trading using fuzzy exception learning. In: Proceedings of Joint 9th IFSA World Congress and 20th NAFIPS International Conference, Vancouver, Canada (2001) 946–951
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Kaymak, U. (2003). Data and Cluster Weighting in Target Selection Based on Fuzzy Clustering. In: Bilgiç, T., De Baets, B., Kaynak, O. (eds) Fuzzy Sets and Systems — IFSA 2003. IFSA 2003. Lecture Notes in Computer Science, vol 2715. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44967-1_68
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DOI: https://doi.org/10.1007/3-540-44967-1_68
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