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Evolutionary Computational Techniques in Marketing

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Encyclopedia of Machine Learning

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Definition

Evolutionary Computation (EC) in marketing is a field that uses evolutionary techniques to extract and gather useful patterns with the objective of designing marketing strategies and discovering products and services of superior value which satisfy the customers’ necessities. Due to the fierce competition by some companies for attracting more customers and the necessity of innovation, it is common to find numerous marketing problems being approached by EC techniques.

Motivation and Background

The objective of marketing is to identify the customers’ needs and desires in order to guide the entire organization to serve best by designing products, services, and programs which satisfy customers (Kotler & Armstrong, 1996). Nowadays, the market competition is very strong, since customers can choose from several alternatives. For that reason, marketing teams are facing the necessity of creating intelligent business strategies. Thus, new artificial intelligent approaches for...

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Recommended Reading

  • Abraham, A., & Ramos, V. (2003). Web Usage mining using artificial ant colony clustering and linear genetic programming, Genetic programming, Congress on Evolutionary Computation (CEC), IEEE, 2003, 1384–1391.

    Google Scholar 

  • Balakrishnan, P. V. S., & Jacob, V. S. (1996). Genetic algorithms for product design. Management Science, 42(8), 1105–1117.

    Article  MATH  Google Scholar 

  • Bhattacharyya, S. (2000). Evolutionary algorithms in data mining: Multi-objective performance modeling for direct marketing. In KDD ’00: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 465–473). New York: ACM.

    Chapter  Google Scholar 

  • Fruchter, G.E., Fligler, A., Winer, R. S. (2006). Optimal product line design: A genetic algorithm approach to mitigate cannibalization. Journal of Optimization Theory and Applications, 131(2), (pp. 227-244), Springer Netherlands.

    Google Scholar 

  • Kotler, P., & Armstrong, G. (1996), Principles of marketing, 7 ed., Prentice Hall, Englewood Cliffs NJ.

    Google Scholar 

  • Kwon, Y.-K., & Moon, B.-R. (2001). Personalized email marketing with a genetic programming circuit model. In L. Spector, E. D. Goodman, A. Wu, W. B. Langdon, H.-M. Voigt, M. Gen (Eds.), Proceedings of the genetic and evolutionary computation conference (GECCO-2001) (pp. 1352–1358). San Francisco: Morgan Kaufmann.

    Google Scholar 

  • Liu, H.-H., & Ong, C.-S. (2008). Variable selection in clustering for marketing segmentation using genetic algorithms. Expert Systems Applications, 34(1), 502–510.

    Article  Google Scholar 

  • Naik, P. A., Mantrala, M. K., & Sawyer, A. G. (1998). Planning media schedules in the presence of dynamic advertising quality. Marketing Science, 17(3), 214–235.

    Article  Google Scholar 

  • Scanlon, Jessie. “Staples’ Evolution.” BusinessWeek 29 Dec. 2008: 1-2. Web, http://www.businessweek.com/innovate/content/dec2008/id20081229_162381.htm}}

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Author information

Authors and Affiliations

    Authors
    1. Alma Lilia García-Almanza
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    2. Biliana Alexandrova-Kabadjova
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    3. Serafín Martínez-Jaramillo
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    Editor information

    Editors and Affiliations

    1. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia, 2052

      Claude Sammut

    2. Faculty of Information Technology, Clayton School of Information Technology, Monash University, P.O. Box 63, Victoria, Australia, 3800

      Geoffrey I. Webb

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    © 2011 Springer Science+Business Media, LLC

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    García-Almanza, A.L., Alexandrova-Kabadjova, B., Martínez-Jaramillo, S. (2011). Evolutionary Computational Techniques in Marketing. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_275

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