Optimal Product Design by Sequential Experiments in High Dimensions
针对产品属性及其交互作用导致的设计空间高维问题，提出一种基于市场份额期望改进的序贯实验准则，结合随机搜索变量选择方法，高效筛选高需求潜力的产品配置。
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, during which 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 with alternative methods. This paper was accepted by Eric Anderson, marketing.