Should Optimal Designers Worry About Consideration?

Abstract

Consideration set formation using noncompensatory 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 noncompensatory 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 and design 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 suboptimal design decisions when the population uses consideration behavior; convergence of compensatory models to noncompensatory behavior is likely to require unrealistic amounts of data; and modeling heterogeneity in noncompensatory screening is more valuable than heterogeneity in compensatory tradeoffs. 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 more profitable design decisions; different model forms can provide “descriptiveâ€‌ rather than “predictiveâ€‌ information that is useful for design.