Journal
MARKETING SCIENCE
Volume 24, Issue 4, Pages 595-615Publisher
INFORMS
DOI: 10.1287/mksc.1050.0123
Keywords
automated modeling; choice models; kernel transformations; multinomial logit model; predictive models; support vector machine
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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.
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