Efficient Experimental Designs for Hyper-parameter Estimation: Studying the Level-Effect in Conjoint Analysis

Research in marketing, and business in general, involves understanding when effect-sizes are expected to be large and when they are expected to be small. An example is the level-effect in marketing, where the effect of product attributes on utility is positively related to the number of levels present among choice alternatives. Knowing when consumers are sensitive to the levels of attributes is an important aspect of merchandising, selling and promotion. In this paper, we propose efficient methods of learning about contextual factors that influence consumer preference and sensitivities within the context of a hierarchical Bayes model. A design criterion is developed for hierarchical linear models, and validated in a study of the "level-effect" in conjoint analysis using a national sample of respondents. Extensions to other model structures are discussed.