  Visual design is critical to product success, and the subject of intensive
marketing research effort. Yet visual elements, due to their holistic and
interactive nature, do not lend themselves well to optimization using extant
decompositional methods for preference elicitation. Here we present a
systematic methodology to incorporate interactive, 3D-rendered product
configurations into a conjoint-like framework. The method relies on rapid,
scalable machine learning algorithms to adaptively update product designs along
with standard information-oriented product attributes. At its heart is a
parametric account of a product's geometry, along with a novel, adaptive
"bi-level" query task that can estimate individuals' visual design form
preferences and their trade-offs against such traditional elements as price and
product features. We illustrate the method's performance through extensive
simulations and robustness checks, a formal proof of the bi-level query
methodology's domain of superiority, and a field test for the design of a
mid-priced sedan, using real-time 3D rendering for an online panel. Results
indicate not only substantially enhanced predictive accuracy, but two
quantities beyond the reach of standard conjoint methods: trade-offs between
form 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 or
analyst-supplied consumer groupings, as well as their sensitivities to form and
functional elements.
