Consider-Then-Choose Models in Decision-Based Design Optimization Available to Purchase

This article describes an advance in design optimization that includes consumer purchasing decisions. Decision-Based Design optimization commonly relies on Discrete Choice Analysis (DCA) to forecast sales and revenues for different product variants. Conventional DCA, which represents consumer choice as a compensatory process through maximization of a smooth utility function, has proven to be reasonably accurate at predicting choice and interfaces easily with engineering models. However the marketing literature has documented significant improvement in modeling choice with the use of models that incorporate non-compensatory (descriptive) and compensatory (predictive) components. The non-compensatory component can, for example, model a “consider-then-choose” process in which potential customers first narrow their decisions to a small set of products using heuristic screening rules and then employ a compensatory evaluation to select from this set. This article demonstrates that ignoring consider-then-choose behavior can lead to sub-optimal designs, and that optimality cannot be “recovered” by changing marketing variables alone. A new computational approach is proposed for solving optimal design problems with consider-then-choose models whose screening rules are based on conjunctive (logical “and”) rules. Computational results are provided using three state-of-the-art commercial solvers (matlab, KNITRO, and SNOPT).