Abstract
In this work we have studied the problem of finding potential customers from large marketing databases using a genetic algorithm. The problem is that it is far from clear, in advance, what constitutes a good potential customer in the database in question, even if criteria for how a customer should be graded can be formulated. The genetic algorithm approach uses this grading as the basis for a fitness function, crucial to the genetic algorithm, and effectively applies the genetic algorithm to classify the database accordingly. As a consequence, the result directly tells us both how well the grading succeeds in finding good customers and gives us a set of found optimal customers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Goldberg, David E. Genetic Algorithms in Search, Optimization and Machine Learning Addison-Wesley, Reading 1989
Sandqvist, Sam On Finding Optimal Customers from a Large Marketing Database STEP-92 New Directions in Artificial Intelligence, Proc. of Finnish Int. AI Conf., Vol 3, p. 36ff
Siegelmann, Hava T. and Frieder, Ophir Document Allocation in Multiprocessor Information Retrieval Systems Tech. Report IA-92-1, George Mason University, Fairfax, 1992
Galarce, Carlos E. Adaptive Systems and the Search for Optimal Index Selection M.Sc. Thesis, Wayne State University, Detroit, 1990
Ioannides, YE. and Chang, Y. C. Randomized Algorithms for Optimizing Large Join Queries Proc. 1990 ACM-SIGMOD Conf. on Management of Data, ACM Press, 1990
Bennett, Kristin; Ferris, Michael C. and Ioannides, Yannis E. A Genetic Algorithm for Database Query OptimizationProc. Fourth Int. Conf. on Genetic Algorithms, Morgan Kaufmann Pub, 1991 p. 400-407
Davis, Lawrence (ed) Handbook of Genetic Algorithms Van Nostrand Reinhold, New York, 1991
Greene, David Perry and Smith, Stephen F. GBML System Learns Rules Describing Customer Preferences Proc. Second Int. Conf. on Genetic Algorithms, Morgan Kaufman Pub., 1987 p. 217-223
SoftC Ltd. SoftC Database Access Library documentation Electronic media, with the software. 1990
de Pelsmacher, Patrick What are your customers looking for? — An illustration of conjoint analysis shows how to elicit customer preferences and price perceptions for different product characteristics Operational Research Insight, Vol. 5, issue 2 (March–June 1992), p. 16–21
Grefenstette, John J; Davis, Lawrence, and Cerys, Daniel Genesis & OOGA: Two Genetic Algorithm Systems TSP, P.O. Box 991, Melrose, MA 02176, 1991
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1993 Springer-Verlag/Wien
About this paper
Cite this paper
Sandqvist, S. (1993). On Finding Optimal Potential Customers from a Large Marketing Database — a Genetic Algorithm Approach. In: Albrecht, R.F., Reeves, C.R., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7533-0_77
Download citation
DOI: https://doi.org/10.1007/978-3-7091-7533-0_77
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82459-7
Online ISBN: 978-3-7091-7533-0
eBook Packages: Springer Book Archive