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Adventures in Demand Analysis Using AI

Author

Listed:
  • Philipp Bach
  • Victor Chernozhukov
  • Sven Klaassen
  • Martin Spindler
  • Jan Teichert-Kluge
  • Suhas Vijaykumar

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.

Suggested Citation

  • Philipp Bach & Victor Chernozhukov & Sven Klaassen & Martin Spindler & Jan Teichert-Kluge & Suhas Vijaykumar, 2024. "Adventures in Demand Analysis Using AI," Papers 2501.00382, arXiv.org.
    • Handle: RePEc:arx:papers:2501.00382
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    References listed on IDEAS

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