Automatically Discovering Visual Product Characteristics

51 Pages Posted: 13 Jul 2022

See all articles by Ankit Sisodia

Ankit Sisodia

Yale School of Management

Alex Burnap

Yale School of Management

Vineet Kumar

Yale School of Management

Date Written: June 30, 2022

Abstract

Marketing models typically focus on how structured product characteristics impact consumer preferences. However, visual characteristics of products present in unstructured image data play an important role in impacting preferences for many categories. We seek to automatically discover and quantify visual characteristics (attributes) from image data using a disentanglement-based approach. While the deep learning literature has shown that supervision is required to obtain unique disentangled representations, ground truth visual characteristics are typically unknown. We develop a method that does not require such supervision, and instead uses readily available structured product characteristics as supervisory signals to enable disentanglement. Our method does not need prior knowledge of characteristics, yet we are able to discover semantically interpretable and statistically independent characteristics. Moreover, the method quantifies the levels of each discovered product characteristic, necessary for managerial tasks such as demand modeling and conjoint analysis. We apply this method to automatically discover visual product characteristics of watches, and discover 6 semantically interpretable visual characteristics providing a disentangled representation. Our results find the supervisory signal `brand' best promotes disentanglement relative to an unsupervised approach. We lastly demonstrate how consumers preferences may be assessed over these discovered visual characteristics using a choice-based conjoint analysis.

Keywords: discovery of product characteristics, deep learning, disentanglement

Suggested Citation

Sisodia, Ankit and Burnap, Alex and Kumar, Vineet, Automatically Discovering Visual Product Characteristics (June 30, 2022). Available at SSRN: https://ssrn.com/abstract=4151019 or http://dx.doi.org/10.2139/ssrn.4151019

Ankit Sisodia (Contact Author)

Yale School of Management ( email )

165 Whitney Ave
New Haven, CT 06511

Alex Burnap

Yale School of Management ( email )

165 Whitney Avenue
New Haven, CT 06511
United States

Vineet Kumar

Yale School of Management ( email )

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States

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