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Predictive Modeling on Multiple Marketing Objectives Using Evolutionary Computation

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Marketing Intelligent Systems Using Soft Computing

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 258))

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Abstract

Predictive models find wide use in marketing for customer segmentation, targeting, etc. Models can be developed to different objectives, as defined through the dependent variable of interest. While standard modeling approaches embody single performance objectives, actual marketing decisions often need consideration of multiple performance criteria. Multiple objective problems typically characterize a range of solutions, none of which dominate the others with respect to the different objectives - these specify the Pareto-frontier of non-dominated solutions, each offering a different level of tradeoff. This chapter examines the use of evolutionary computation to obtain a set of such non-dominated models. An application using a real-life problem and data-set is presented, with results highlighting how such multi-objective models can yield advantages over traditional approaches.

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Authors and Affiliations

  1. Information and Decision Sciences, College of Business Administration, University of Illinois, Chicago, 312-996-8794

    Siddhartha Bhattacharyya

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  1. Siddhartha Bhattacharyya
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Editors and Affiliations

  1. University of Granada, Spain

    Jorge Casillas  & Francisco J. Martínez-López  & 

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Bhattacharyya, S. (2010). Predictive Modeling on Multiple Marketing Objectives Using Evolutionary Computation. In: Casillas, J., Martínez-López, F.J. (eds) Marketing Intelligent Systems Using Soft Computing. Studies in Fuzziness and Soft Computing, vol 258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15606-9_13

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  • DOI: https://doi.org/10.1007/978-3-642-15606-9_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15605-2

  • Online ISBN: 978-3-642-15606-9

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