The authors model product consideration as preceding choice in a segment-level conjoint model. They propose a latent-class tobit model to estimate cardinal, segment-level preference functions based on consumers’ preference ratings for product concepts considered worth adding to consumers’ self-explicated consideration sets. The probability with which the utility of a product profile exceeds an unobserved threshold corresponds to its consideration probability, which is assumed to be independent across product profiles and common to consumers in a segment. A market-share simulation compares the predictions of the proposed model with those obtained from an individual-level tobit model and from traditional ratings-based conjoint analysis. The authors also report simulations that assess the robustness of the proposed estimation procedure, which uses an E-M algorithm to obtain maximum likelihood parameter estimates.
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