Efficient Experimental Designs for Hyperparameter Estimation: Learning When Effect-Sizes are Large

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. Understanding the contexts in which consumers are sensitive to offers and variables such as price, 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.