Product Aesthetic Design: A Machine Learning Augmentation

Alex Burnap (), John R. Hauser () and Artem Timoshenko ()
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Alex Burnap: Department of of Marketing, Yale School of Management, Yale University, New Haven, Connecticut 06511
John R. Hauser: Marketing Group, MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142
Artem Timoshenko: Department of Marketing, Kellogg School of Management, Northwestern University, Evanston, Illinois 60208

Marketing Science, 2023, vol. 42, issue 6, 1029-1056

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 more than $100,000, and hundreds are conducted annually. We propose a model to augment the commonly used aesthetic design process by predicting aesthetic scores and automatically generating innovative and appealing product designs. The model combines a probabilistic variational autoencoder (VAE) with 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 uniform 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 that 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 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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