Abstract
This paper contains results of a research project aiming at the application and evaluation of modern data analysis techniques in the field of marketing. The investigated techniques are: genetic programming, rough data analysis, CHAID and logistic regression analysis. All four techniques are applied independently to the problem of customer retention modelling, using a database of a financial company. Models created by these techniques are used to gain insights into factors influencing customer behaviour and to make predictions on ending the relationship with the company in question. Comparing the predictive power of the obtained models shows that the genetic technology offers the highest performance.
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References
A.E. Eiben, T.J. Euverman, W. Kowalczyk, E. Peelen, F. Slisser and J.A.M. Wesseling. Comparing Adaptive and Traditional Techniques for Direct Marketing, in H.-J. Zimmermann (ed.), Proceedings of the 4th European Congress on Intelligent Techniques and Soft Computing, Verlag Mainz, Aachen, pp. 434–437, 1996.
D. Haughton and S. Oulida, Direct marketing modeling with CART and CHAID, in Journal of direct marketing, volume 7, number 3, 1993.
D.W. Hosmer and L. Lemeshow. Applied logistic regression, New York, Wiley, 1989.
J. Koza, Genetic Programming, MIT Press, 1992.
W. Kowalczyk, Analyzing temporal patterns with rough sets, in H.-J. Zimmermann (ed.), Proceedings of the 4th European Congress on Intelligent Technologies and Soft Computing, Verlag der Augustinus Buchhandlung, pp. 139–143, 1996.
M. Magidson, Improved statistical techniques for response modeling, in Journal of direct marketing, volume 2, number 4, 1988.
F.F. Reichheld, Learning from Customer Defections, in Harvard Business Review, march–april 1996.
R. Walker, D. Barrow, M. Gerrets and E. Haasdijk, Genetic Algorithms in Business, in J. Stender, E. Hillebrand and J. Kingdon (eds.), Genetic Algorithms in Optimisation, Simulation and Modelling, IOS Press, 1994.
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© 1998 Springer-Verlag Berlin Heidelberg
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Eiben, A.E., Koudijs, A.E., Slisser, F. (1998). Genetic modelling of customer retention. In: Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T.C. (eds) Genetic Programming. EuroGP 1998. Lecture Notes in Computer Science, vol 1391. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0055937
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DOI: https://doi.org/10.1007/BFb0055937
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