Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach

Abstract

Aesthetics are critically important to market acceptance. In the automotive industry, an improved aesthetic design can boost sales by 30% or more. Firms invest heavily in designing and testing aesthetics. A single automotive “theme clinic” can cost over $100,000, and hundreds are conducted annually. We use machine learning to augment human judgment when designing and testing new product aesthetics. The model combines a probabilistic variational autoencoder (VAE) and adversarial components from generative adversarial networks (GAN), with modeling assumptions that address managerial requirements for firm adoption. We train and evaluate our model with industry data from an automotive partner—203 SUV images evaluated by targeted consumers and 180,000 high-quality unrated images. Our model predicts well the appeal of new aesthetic designs—43.9% improvement relative to a baseline and substantial improvement over both conventional machine learning models and pretrained deep learning models. New automotive designs are generated in a controllable manner. We empirically verify that automatically generated designs are (1) appealing to consumers, (2) identify designs introduced to the market in the future, and (3) spark creativity to help designers and management explore visual dimensions that affect aesthetic perception. The results suggest that machine learning offers significant opportunity to augment aesthetic design.

Keywords: Aesthetics, Generative Adversarial Networks, Generating New Products, Machine Learning, Prelaunch Forecasting, Product Development, Variational Autoencoders.