Adventures in Demand Analysis Using AI

TL;DR

This paper demonstrates that AI-derived multimodal product embeddings significantly enhance demand prediction and causal inference accuracy in empirical analysis, revealing heterogeneity in price elasticity.

Contribution

It introduces a novel approach combining AI-based multimodal embeddings with demand analysis, improving predictive and causal inference capabilities.

Findings

Embeddings improve sales rank and price prediction accuracy.

AI representations lead to more credible causal estimates.

Reveals heterogeneity in price elasticity across products.

Abstract

This paper advances empirical demand analysis by integrating multimodal product representations derived from artificial intelligence (AI). Using a detailed dataset of toy cars on textit{Amazon.com}, we combine text descriptions, images, and tabular covariates to represent each product using transformer-based embedding models. These embeddings capture nuanced attributes, such as quality, branding, and visual characteristics, that traditional methods often struggle to summarize. Moreover, we fine-tune these embeddings for causal inference tasks. We show that the resulting embeddings substantially improve the predictive accuracy of sales ranks and prices and that they lead to more credible causal estimates of price elasticity. Notably, we uncover strong heterogeneity in price elasticity driven by these product-specific features. Our findings illustrate that AI-driven representations can enrich and modernize empirical demand analysis. The insights generated may also prove valuable for applied causal inference more broadly.