Design optimization in market system research 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 both noncompensatory (descriptive) and compensatory (predictive) components. This noncompensatory 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 noncompensatory screening rules and then employ a compensatory evaluation to select from within this consideration set. This article presents solutions to a design optimization challenge that arises when demand is modeled with a consider-then-choose model: the choice probabilities are no longer continuous or continuously differentiable. We examine two different classes of methods to solve optimal design problems–genetic algorithms (GAs) and nonlinear programming (NLP) relaxations based on complementarity constraints–for consider-then-choose models whose screening rules are based on conjunctive (logical “and”) rules.

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).

Abstract. Consideration set formation using non-compensatory screening rules is a vital component of real purchasing decisions with decades of experimental validation. Marketers have recently developed statistical methods that can estimate quantitative choice models that include consideration set formation via non-compensatory screening rules. But is capturing consideration within models of choice important for design? This paper reports on a simulation study of a vehicle portfolio design when households screen over vehicle body style built to explore the importance of capturing consideration rules for optimal designers. We generate synthetic market share data, fit a variety of discrete choice models to the data, and then optimize design decisions using the estimated models. Model predictive power, design "error", and profitability relative to ideal profits are compared as the amount of market data available increases. We find that even when estimated compensatory models provide relatively good predictive accuracy, they can lead to sub-optimal design decisions when the population uses consideration behavior; convergence of compensatory models to non-compensatory behavior is likely to require unrealistic amounts of data; and modeling heterogeneity in non-compensatory screening is more valuable than heterogeneity in compensatory trade-offs. This supports the claim that designers should carefully identify consideration behaviors before optimizing product portfolios. We also find that higher model predictive power does not necessarily imply better design decisions; that is, different model forms can provide "descriptive" rather than "predictive" information that is useful for design.

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Consumer behavior can be modeled using a decision-making process termed “consideration” in which consumers form requirements, “consideration rules,” in order to narrow their options for further evaluation. One type of consideration rule is the conjunctive rule, where a consumer makes a list of requirements and a product must meet all of the requirements in order to be considered for purchase, such as “the vehicle must get 25 miles per gallon or more”; “it must be priced at $22,000 or less”; and “it must be a standard-sized sedan.” This paper offers a design framework for linking these consideration rules with design. We demonstrate the use of our framework with a case study, namely the Volkswagen (VW) “clean diesel” scandal, which investigates the design strategies used in response to the scandal by capturing considerations within the marketing product planning subproblem and assuring engineering feasibility within the engineering design subproblem.