Form + Function: Optimizing Aesthetic Product Design via Adaptive, Geometrized Preference Elicitation Namwoo Kang Department of Mechanical Systems Engineering Sookmyung Women’s University
[email protected] Yi Ren Department of Mechanical Engineering Arizona State University
[email protected] Fred M. Feinberg Ross School of Business and Department of Statistics University of Michigan
[email protected] Panos Y. Papalambros Department of Mechanical Engineering University of Michigan
[email protected] November 7, 2019 The authors gratefully acknowledge the support of University of Michigan Graham Sustainability Institute Fellowship, as well as Elea Feit and Jangwon Choi for their suggestions. The first two authors contributed equally. Under second review at Marketing Science; please do not quote or distribute. 1 Form + Function: Optimizing Aesthetic Product Design via Adaptive, Geometrized Preference Elicitation Abstract Visual design is critical to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.