Demand Estimation with Text and Image Data

Giovanni Compiani; Ilya Morozov; Stephan Seiler

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arXiv · 2503.20711

Demand Estimation with Text and Image Data

Giovanni Compiani·Ilya Morozov·Stephan Seiler

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Abstract

We propose a demand estimation approach that leverages unstructured data to infer substitution patterns. Using pre-trained deep learning models, we extract embeddings from product images and textual descriptions and incorporate them into a mixed logit demand model. This approach enables demand estimation even when researchers lack data on product attributes or when consumers value hard-to-quantify attributes such as visual design. Using a choice experiment, we show this approach substantially outperforms standard attribute-based models at counterfactual predictions of second choices. We also apply it to 40 product categories offered on Amazon.com and consistently find that unstructured data are informative about substitution patterns.

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econ.GN cs.CV cs.LG q-fin.EC

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Giovanni Compiani, Ilya Morozov, Stephan Seiler. 2025-03-26. Demand Estimation with Text and Image Data. https://arxiv.org/abs/2503.20711

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