Predictive Modeling on Multiple Marketing Objectives Using Evolutionary Computation

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


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.


Genetic Algorithm Evolutionary Computation Multiobjective Optimization Pareto Frontier Population Member 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Siddhartha Bhattacharyya
    • 1
  1. 1.Information and Decision Sciences, College of Business AdministrationUniversity of IllinoisChicago

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