Abstract
This chapter describes how rough sets can be used for response modeling in database marketing. We use real-world data from one of the largest European mail-order companies. Past transaction data of customers, personal characteristics and their response behavior are used to determine whether these clients are good mailing prospects during the next period.
We provide a comparison of statistical techniques, machine learning, mathematical programming, rough sets and neural networks in a classification task, and show that rough sets can also be successfully used for response modeling in database marketing.
The performance of alternative techniques is judged on the percentage of correct classifications in the validation sample, and on gains chart analysis. The results indicate that on a dataset with only categorical information, the predictive performance of statistical techniques, machine learning techniques and neural networks on a validation dataset is very similar Still the observed differences are significant.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aldrich, J. H., Nelson, F. D.: Linear probability, logit, and probit models. Sage Publications, Beverly Hills CA (1991)
Berry J.: Database marketing. Business Week, September 5 (1994) 56–62
Brieman, L., Friedman, J. H., Olshen, R. A., Stone, C. J.: Classification and regression trees. Wadsworth & Brooks, Monterey CA (1984)
Burgess, N.: How neural networks can improve database marketing. Journal of Database Marketing 2/4 (1995) 312–327
Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: an overview. In: Advances in knowledge discovery and data mining, AAAI Press (1996)
Furness, P.: New pattern analysis methods for database marketing. Journal of Database Marketing 1/3 (1994) 220–232
Gochet, W., Stam, A., Chen, S., Srinivasan, V.: Multi-group discriminant analysis using linear programming. Operations Research 45/2 (1997) 213–225
Grzymala-Busse, J. W.: Managing uncertainty in machine learning from examples. In: Proceedings of Workshop Intelligent Information Systems III, Wigry, Poland, June 6–10 (1994) 70–84
Hanssens, D. M., Parsons, L. J., Schultz, R. L.: Market response models: econometric and time series analysis. Kluwer Academic Publishers, Boston (1992)
Klecka, W. R.: Discriminant analysis. Sage Publications, Beverly Hills CA (1990)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection (working paper). Computer Science Department, Stanford University (1995)
Lenarcik, A., Piasta, Z.: An invariant method of rough classifier construction. Proceedings of the poster session of ISMIS ‘86, Oak Ridge Laboratory (1996) 146–156
Magidson, J.: Improved statistical techniques for response modeling. Journal of Direct Marketing 2/4 (1988) 6–18
McLachlan, G. J.: Discriminant analysis and statistical pattern recognition. John Wiley, New York (1992)
Michie, D., Spiegelhalter, D. J., Taylor C. C.: Machine learning, neural and statistical classification. Ellis Horwood series in artificial intelligence. Prentice Hall, Englewood Cliffs NJ (1994)
Pawlak, Z.: Rough sets. International Journal of Information and Computer Science 11 (1982) 341–356
Pawlak, Z., Grzymala-Busse, J. et al.: Rough sets. Communications of the ACM 38 (1995) 89–95
Petrison, L. A., Blattberg, R. C., Wang P.: Database marketing: past, present, and future. Journal of Direct Marketing 7 (1993) 27–43
Piasta, Z., Lenarcik, A.: Rule induction with probabilistic rough classifiers. ICS Research Report 24/96, Institute of Computer Science, Warsaw University of Technology (1996)
Quinlan, J. R.: C4.5 programs for machine learning. Morgan Kaufmann, San Mateo CA (1993)
Ragsdale, C. T., Stam, A.: Mathematical programming formulations for the discriminant problem: an old dog does new tricks. Decision Sciences 22 (1991) 296–306
Roberts, M. L., Berger P. D.: Direct marketing management. Prentice Hall, Englewood Cliffs NJ (1989)
Thompson, J.: Targeting for response value and profit. Journal of Targeting, Measurement and Analysis for Marketing 3 (1994) 133–146
Van den Poel, D.: Cross-selling with neural nets. Proceedings of the NCDM’96 conference (1996) 831–840
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Van den Poel, D. (1998). Rough Sets for Database Marketing. In: Polkowski, L., Skowron, A. (eds) Rough Sets in Knowledge Discovery 2. Studies in Fuzziness and Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1883-3_17
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1883-3_17
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2459-9
Online ISBN: 978-3-7908-1883-3
eBook Packages: Springer Book Archive