Should Optimal Designers Worry About Consideration?

TL;DR

This study investigates whether incorporating consideration set formation models improves vehicle portfolio design decisions, revealing that models capturing non-compensatory behavior lead to better outcomes than traditional compensatory models, especially with limited data.

Contribution

The paper demonstrates through simulation that modeling consideration behavior enhances the quality of design decisions, highlighting the importance of identifying consideration rules in portfolio optimization.

Findings

Compensatory models can be inaccurate for non-compensatory consideration behaviors.

Higher predictive accuracy does not always lead to better design decisions.

Modeling heterogeneity in consideration behavior improves portfolio optimization.

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.