Modeling Preferences for Common Attributes in Multicategory Brand Choice
Published 5/1/2005
Author: Vishal P. Singh, Karsten T. Hansen, and Sachin Gupta
View this content
Executive Summary
Characteristic- based discrete choice models of demand have been used extensively in both economics and marketing. The basic approach in these models is to view products as bundles of characteristics, with consumer preferences defined over the characteristics’ space. Besides the advantage of parsimony, this modeling approach has important applications, such as the prediction of demand for new products. In this article, the authors propose a multicategory brand choice model that is based on the conceptualization that the intrinsic utility for a brand is a function of underlying attributes or characteristics, some of which are common across categories.
The general premise is that household preferences for attributes that are common across categories are likely to be correlated. The authors project the unobserved component of preferences for attributes and sensitivities to marketing-mix variables to a lower dimensional space of unobserved factors. The factors are interpretable as unobservable household “traits” that explain similarity in choice behaviors across categories. Because the traits transcend categories, household-specific factor estimates derived from purchasing in existing categories can be used to predict preferences for attributes in new categories. The authors apply the proposed model to household panel data for three closely related snack categories and for two less related food categories. The authors find strong correlations in preferences for product attributes such as brand names and low fat or fat free. In two cross-category targeting applications, the authors demonstrate that such high correlations in product attribute preferences across categories imply that (1) the model estimates can be used to improve forecasts of preferences for an attribute in a new category and (2) potential targets for a new product in an existing category can be scored on the basis of prospects’ probability of choice.
Biography
Vishal P. Singh is Assistant Professor of Marketing at the Tepper School of Business, Carnegie Mellon University. He received his doctoral degree from Kellogg School of Management, Northwestern University. His research interests include retail competition, demand analysis, competitive pricing, private labels, and database marketing.
Karsten T. Hansen is Assistant Professor at Kellogg School of Management, Marketing Department, Northwestern University. His research interests are in the areas of database marketing, empirical models of household behavior, and econometrics. He has recently published in Journal of Econometrics and International Economic Review.
Sachin Gupta is Associate Professor of Marketing at the Johnson Graduate School of Management, Cornell University. His areas of research interest and expertise include marketing research, sales promotions, and competitive strategy. His research has been published in Journal of Marketing Research, Marketing Science, and Management Science, and he is a member of the editorial boards of Journal of Marketing Research, Marketing Science, and Quantitative Marketing and Economics.
J Marketing Research, Volume 42, Number 2, May 2005
View Table of Contents.