Abstract
Marketers often analyze multinomial choice from a set of branded products to learn about demand. Given a set of brands to study, we analyze three reasons why choices from strict of the brands can contain more statistical information about demand than choices from all the brands in the study. First, making choices from smaller is easier, so it is possible to use more choice-tasks when the choice data comes from a choice-based conjoint questionnaire. Second, choices from of brands better identify and more accurately estimate the covariance structure of unobserved utility shocks associated with brands. Third, automatically balance the brand-shares when some of the brands are less popular than others. We 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. We find that the optimal subset-size depends on the model, the number of brands in the study, and the designer's resource constraint. Besides showing that can be beneficial, we also provide a simulation methodology that helps designers pick the best subset-size for their setting.
Published Version
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