Statistical Benefits of Choices from Subsets

Abstract

Marketers often analyze multinomial choice from a set of branded products to learn about demand. Given a set of brands to study, the authors analyze three reasons why choices from strict subsets of the brands can contain more statistical information about demand than choices from all the brands in the study: First, making choices from smaller subsets is easier, so it is possible to use more choice tasks when the choice data come from a choice-based conjoint survey. Second, choices from subsets of brands better identify and more accurately estimate the covariance structure of unobserved utility shocks associated with brands. Third, subsets automatically balance the brand shares when some of the brands are less popular than others. The authors demonstrate these three benefits of subsets using a mixture of analytical results and numerical simulations and provide implications for the design of choice-based conjoint analyses. They find that the optimal subset size depends on the model, the number of brands in the study and the designer's resource constraint. In addition to showing that subsets can be beneficial, this article provides a simulation methodology that helps designers pick the best subset size for their setting.