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

37 Pages Posted: 19 Jul 2019

Date Written: July 1, 2019

Abstract

Aesthetics are critically important to market acceptance in many product categories. In the automotive industry in particular, an improved aesthetic design can boost sales by 30% or more. Firms invest heavily in designing and testing new product aesthetics. A single automotive “theme clinic” costs between $100,000 and $1,000,000, 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), along with modeling assumptions that address managerial requirements for firm adoption. We train our model with data from an automotive partner — 7,000 images evaluated by targeted consumers and 180,000 high-quality unrated images. Our model predicts well the appeal of new aesthetic designs — 38% 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 for the design team to consider, which we also empirically verify are appealing to consumers. These results, combining human and machine inputs for practical managerial usage, 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.

Suggested Citation

Burnap, Alex and Hauser, John R. and Timoshenko, Artem, Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach (July 1, 2019). Available at SSRN: https://ssrn.com/abstract=3421771 or http://dx.doi.org/10.2139/ssrn.3421771

Alex Burnap (Contact Author)

MIT Sloan School of Management ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

John R. Hauser

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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