Demand Estimation with Text and Image Data
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911452516843520 |
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| author | Compiani, Giovanni Morozov, Ilya Seiler, Stephan |
| author_facet | Compiani, Giovanni Morozov, Ilya Seiler, Stephan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20711 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Demand Estimation with Text and Image Data Compiani, Giovanni Morozov, Ilya Seiler, Stephan General Economics Economics Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Demand Estimation with Text and Image Data |
| topic | General Economics Economics Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2503.20711 |