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Visual design is critical to product success, and the subject of intensivemarketing research effort. Yet visual elements, due to their holistic andinteractive nature, do not lend themselves well to optimization using extantdecompositional methods for preference elicitation. Here we present asystematic methodology to incorporate interactive, 3D-rendered productconfigurations into a conjoint-like framework. The method relies on rapid,scalable machine learning algorithms to adaptively update product designs alongwith standard information-oriented product attributes. At its heart is aparametric account of a product's geometry, along with a novel, adaptive"bi-level" query task that can estimate individuals' visual design formpreferences and their trade-offs against such traditional elements as price andproduct features. We illustrate the method's performance through extensivesimulations and robustness checks, a formal proof of the bi-level querymethodology's domain of superiority, and a field test for the design of amid-priced sedan, using real-time 3D rendering for an online panel. Resultsindicate not only substantially enhanced predictive accuracy, but twoquantities beyond the reach of standard conjoint methods: trade-offs betweenform and function overall, and willingness-to-pay for specific design elements.Moreover -- and most critically for applications -- the method provides"optimal" visual designs for both individuals and model-derived oranalyst-supplied consumer groupings, as well as their sensitivities to form andfunctional elements.

Form + Function: Optimizing Aesthetic Product Design via Adaptive, Geometrized Preference Elicitation