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Prediction in Marketing Using the Support Vector Machine

Author

Listed:
  • Dapeng Cui

    (Ipsos Insight, North America, 111 North Canal, Suite 405, Chicago, Illinois 60606)

  • David Curry

    (College of Business Administration, University of Cincinnati, Cincinnati, Ohio 45221-0145)

Abstract

Many marketing problems require accurately predicting the outcome of a process or the future state of a system. In this paper, we investigate the ability of the support vector machine to predict outcomes in emerging environments in marketing, such as automated modeling, mass-produced models, intelligent software agents, and data mining. The support vector machine (SVM) is a semiparametric technique with origins in the machine-learning literature of computer science. Its approach to prediction differs markedly from that of standard parametric models. We explore these differences and benchmark the SVM's prediction hit-rates against those from the multinomial logit model. Because there are few applications of the SVM in marketing, we develop a framework to position it against current modeling techniques and to assess its weaknesses as well as its strengths.

Suggested Citation

  • Dapeng Cui & David Curry, 2005. "Prediction in Marketing Using the Support Vector Machine," Marketing Science, INFORMS, vol. 24(4), pages 595-615, January.
    • Handle: RePEc:inm:ormksc:v:24:y:2005:i:4:p:595-615
    DOI: 10.1287/mksc.1050.0123
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    References listed on IDEAS

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