  In some markets, the visual appearance of a product matters a lot. This paper
investigates consumer transactions from a major fashion retailer, focusing on
consumer aesthetics. Pretrained multimodal models convert images and text
descriptions into high-dimensional embeddings. The value of these embeddings is
verified both empirically and by their ability to segment the product space. A
discrete choice model is used to decompose the distinct drivers of consumer
choice: price, visual aesthetics, descriptive details, and seasonal variations.
Consumers are allowed to differ in their preferences over these factors, both
through observed variation in demographics and allowing for unobserved types.
Estimation and inference employ automatic differentiation and GPUs, making it
scalable and portable. The model reveals significant differences in price
sensitivity and aesthetic preferences across consumers. The model is validated
by its ability to predict the relative success of new designs and purchase
patterns.
