Product Aesthetic Design: A Machine Learning Augmentation

53 Pages Posted: 20 Oct 2022 Last revised: 15 Nov 2022

Date Written: November 1, 2022

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 propose a model to augment the commonly-used aesthetic design process by predicting aesthetic scores and generating innovative and appealing images. The model combines a probabilistic variational autoencoder (VAE), adversarial components from generative adversarial networks (GAN), and a supervised learning component. We train and evaluate the model with data from an automotive partner— images of 203 SUVs evaluated by targeted consumers and 180,000 high-quality unrated images. Our model predicts well the appeal of new aesthetic designs—43.5% improvement relative to a baseline and substantial improvement over conventional machine learning models and pretrained deep neural networks. New automotive designs are generated in a controllable manner for use by design teams. We empirically verify that automatically generated designs are (1) appealing to consumers and (2) resemble designs which were introduced to the market five years after our data were collected. We provide an additional proof-of-concept application using open-source images of dining room chairs.

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

Suggested Citation

Burnap, Alex and Hauser, John R. and Timoshenko, Artem, Product Aesthetic Design: A Machine Learning Augmentation (November 1, 2022). MIT Sloan Research Paper No. 5814-19, Available at SSRN: https://ssrn.com/abstract=4253967 or http://dx.doi.org/10.2139/ssrn.4253967

Alex Burnap

Yale School of Management ( email )

165 Whitney Avenue
New Haven, CT 06511
United States

John R. Hauser (Contact Author)

MIT Sloan School of Management ( email )

International Center for Research on the Mngmt Tech.
Cambridge, MA 02142
United States
617-253-2929 (Phone)
617-258-7597 (Fax)

Artem Timoshenko

Kellogg School of Management, Northwestern University ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

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