Optimal Product Design by Sequential Experiments in High Dimensions

Abstract

The identification of optimal product and package designs is challenged when attributes and their levels interact. Firms recognize this by testing trial products and designs prior to launch where the effects of interactions are revealed. A difficulty in conducting analysis for product design is dealing with the high dimensionality of the design space and the selection of promising product configurations for testing. We propose an experimental criterion for efficiently testing product profiles with high demand potential in sequential experiments. The criterion is based on the expected improvement in market share of a design beyond the current best alternative. We also incorporate a stochastic search variable selection method to selectively estimate relevant interactions among the attributes. A validation experiment confirms that our proposed method leads to improved design concepts in a high-dimensional space compared to alternative methods.

Keywords: Design Criterion, Expected Improvement, Interaction Effects, Stochastic Search Variable Selection