Models for heterogeneous variable selection

Marketing managers are interested in knowing how consumers will react to different product configurations. The product manager can change physical attributes through the design of the product and the perception of psychological attributes through promotion strategies. Because consumers are heterogeneous in their tastes and preferences, aggregate level estimates of attribute importance are insufficient to describe the market. New research methods focus on obtaining individual level estimates of attribute importance from a representative sample of consumers.; Marketing researchers have procedural and statistical methods of obtaining measures of attribute importance for each respondent on each attribute. In laboratory or experimental choice settings, studies can be designed to help focus respondents' attention and processing of the product attributes. Bayesian methods of modeling heterogeneity shrink poorly measured individual level parameters to the overall or group level mean. However, it is erroneous to assume that consumers use all the product attributes in all brand choice situations. This thesis demonstrates that improved inference and predictive accuracy can be obtained by modeling which attributes are actually being used by consumers in different discrete choice situations.; This thesis contributes new models for determining, at the individual level, which product attributes are being used by a consumer in a brand choice decision. The heterogeneous variable selection model extends current aggregate level models of Bayesian variable selection. This model assumes a distribution of heterogeneity with mass concentrated at 0 and away from 0 for each parameter. The pooled variable selection model allows the set of attributes used by an individual to vary by choice context. Examples of separate contexts include partial and full profile choice experiments or choice experiments and actual market place transactions. A hybrid model combines the heterogeneous and pooled variable selection models. The threshold variable selection model incorporates insights from an extended model of choice and provides a behavioral explanation of why certain product attributes are used. Tractable algorithms are introduced for estimating the proposed variable selection models. In the two empirical studies presented, a variable selection model fits the data better than baseline models with no variable selection and conventional distributions of heterogeneity.