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.
Customers nowadays consider the driver’s seat, specifically its comfort and aesthetic form, during the automobile purchase decision. As a result, much research has been recently conducted into seat comfort and the influence of the visual appearance of the seat on the perception of comfort. However, the cost of the seat remains an important contributor to overall vehicle cost, and the visual appearance of a seat may influence a customer’s willingness to pay. We conducted an experiment measuring this tradeoff using hierarchical Bayesian conjoint analysis, a marketing method that elicits customer preferences and willingness-to-pay at the individual customer level. Utility models are statistically inferred for three brand segmentations using a dataset obtained through an online interactive web application. Results indicate that in a heterogeneous market, willingness-to-pay is affected by seat form and comfort rating, with particularly significant tradeoffs for the luxury automotive brand segment.