Prediction in Marketing Using the Support Vector Machine

Dapeng Cui () and David Curry ()
Additional contact information
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

Marketing Science, 2005, vol. 24, issue 4, 595-615

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.

Keywords: automated modeling; choice models; kernel transformations; multinomial logit model; predictive models; support vector machine (search for similar items in EconPapers)
Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (47)

Downloads: (external link)
http://dx.doi.org/10.1287/mksc.1050.0123 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:24:y:2005:i:4:p:595-615

Access Statistics for this article

More articles in Marketing Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().