Although eliciting consumer preference is a core part in design, capturing preferences on form (styling) is still a challenge. In particular, we have been examining the trade-off between forms and functions in a vehicle. We elicit consumers’ preferences on product design using state-of-the-art Machine Learning and parameterized 3D vehicle shapes. This research estimates willingness-to-pay for specific design 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.