Ratings-Informed Probit for Predicting Substitution

64 Pages Posted: 22 Jun 2013 Last revised: 26 Jan 2024

See all articles by Jeffrey P. Dotson

Jeffrey P. Dotson

Brigham Young University

Elea McDonnell Feit

Drexel University - Department of Marketing

Mark A. Beltramo

GM Global Research & Development

Date Written: January 19, 2024

Abstract

Choice-based conjoint analysis is widely used in marketing research to predict consumer preferences. However, some product attributes, like brands, or product styling, are too complex to decompose into a small number of distinct components that can be included as variables in a linear utility function. We propose an approach to handle any such "complex attribute" within a conjoint study. Specifically, we supplement the conjoint questions with appeal ratings for each potential level of the complex attribute. We then estimate a heterogeneous multinomial probit model where the covariance structure is a function of the correlations in the appeal ratings. The proposed ratings-informed probit model is theoretically-motivated and requires only one additional parameter per respondent. The structure allows us to collect the choice and ratings data for different groups of respondents, and so scales easily to settings with a large number of concepts. Two empirical applications demonstrate that the ratings-informed probit makes substantially better predictions about which concepts will compete against each other compared to benchmarks. The approach provides a parsimonious way to incorporate complex attributes into choice models early in the product development process when evaluating many concepts.

Keywords: hoice Models, Conjoint, Product Line Design, Brands, Substitution, Styling, Hierarchical Bayes, Multinomial Probit Model

Suggested Citation

Dotson, Jeffrey P. and Feit, Elea McDonnell and Beltramo, Mark A., Ratings-Informed Probit for Predicting Substitution (January 19, 2024). Available at SSRN: https://ssrn.com/abstract=2282570 or http://dx.doi.org/10.2139/ssrn.2282570

Jeffrey P. Dotson

Brigham Young University ( email )

United States
8014221659 (Phone)

Elea McDonnell Feit (Contact Author)

Drexel University - Department of Marketing ( email )

United States

Mark A. Beltramo

GM Global Research & Development ( email )

Warren, MI 48090-9055
United States

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