Challenges and Opportunities in High-Dimensional Choice Data Analyses

Marketing Letters, Vol. 19, No. 3/4, Seventh Tri-Annual Choice Symposium (December 2008), pp. 201-213

14 Pages Posted: 24 Jun 2014

See all articles by Prasad A. Naik

Prasad A. Naik

University of California, Davis

Michel Wedel

University of Maryland - Robert H. Smith School of Business, Marketing Department; University of Groningen - Faculty of Economics and Business

L. Bacon

Polimetrix Inc.

Anand V. Bodapati

University of California, Los Angeles (UCLA) - Anderson School of Management

Eric Bradlow

University of Pennsylvania - Marketing Department

Wagner A. Kamakura

Rice University

Jeffrey Kreulen

IBM Research - Almaden Research Center

Peter Lenk

University of Michigan, Stephen M. Ross School of Business

David Madigan

Columbia University - Department of Statistics

Alan Montgomery

Carnegie Mellon University - Tepper School of Business

Date Written: May 1, 2008

Abstract

Modern businesses routinely capture data on millions of observations across subjects, brand SKUs, time periods, predictor variables, and store locations, thereby generating massive high-dimensional datasets. For example, Netflix has choice data on billions of movies selected, user ratings, and geodemographic characteristics. Similar datasets emerge in retailing with potential use of RFIDs, online auctions (e.g., eBay), social networking sites (e.g., mySpace), product reviews (e.g., ePinion), customer relationship marketing, internet commerce, and mobile marketing. We envision massive databases as four-way VAST matrix arrays of Variables x Alternatives x Subjects x Time where at least one dimension is very large. Predictive choice modeling of such massive databases poses novel computational and modeling issues, and the negligence of academic research to address them will result in a disconnect from the marketing practice and an impoverishment of marketing theory. To address these issues, we discuss and identify the challenges and opportunities for both practicing and academic marketers. Thus, we offer an impetus for advancing research in this nascent area and fostering collaboration across scientific disciplines to improve the practice of marketing in information-rich environment.

Suggested Citation

Naik, Prasad A. and Wedel, Michel and Bacon, L. and Bodapati, Anand V. and Bradlow, Eric and Kamakura, Wagner A. and Kreulen, Jeffrey and Lenk, Peter and Madigan, David and Montgomery, Alan, Challenges and Opportunities in High-Dimensional Choice Data Analyses (May 1, 2008). Marketing Letters, Vol. 19, No. 3/4, Seventh Tri-Annual Choice Symposium (December 2008), pp. 201-213. Available at SSRN: https://ssrn.com/abstract=2392642

Prasad A. Naik

University of California, Davis ( email )

One Shields Avenue
Davis, CA 95616
United States

Michel Wedel (Contact Author)

University of Maryland - Robert H. Smith School of Business, Marketing Department ( email )

College Park, MD 20742
United States
301.405.2162 (Phone)
301.405.0146 (Fax)

HOME PAGE: http://www.rhsmith.umd.edu/marketing/faculty/wedel.html

University of Groningen - Faculty of Economics and Business ( email )

Postbus 72
9700 AB Groningen
Netherlands

L. Bacon

Polimetrix Inc. ( email )

285 Hamilton Avenue
Suite 200
Palo Alto, CA 94301
United States

Anand V. Bodapati

University of California, Los Angeles (UCLA) - Anderson School of Management ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States

Eric Bradlow

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States
215-898-8255 (Phone)

Wagner A. Kamakura

Rice University ( email )

6100 South Main Street
P.O. Box 1892
Houston, TX 77005-1892
United States
(713) 348-6307 (Phone)

Jeffrey Kreulen

IBM Research - Almaden Research Center

San Jose, CA 95120
United States

Peter Lenk

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States

David Madigan

Columbia University - Department of Statistics ( email )

Mail Code 4403
New York, NY 10027
United States

Alan Montgomery

Carnegie Mellon University - Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

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