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.

翻译：本文提出一种利用非结构化数据推断替代模式的需求估计方法。我们采用预训练的深度学习模型，从产品图像与文本描述中提取嵌入特征，并将其整合至混合Logit需求模型中。该方法使得研究者即使在缺乏产品属性数据、或消费者重视难以量化的属性（如视觉设计）时，仍能进行需求估计。通过选择实验，我们证明该方法在第二选择的反事实预测方面显著优于基于属性的标准模型。我们还将该方法应用于亚马逊网站的40个产品类别，一致发现非结构化数据对替代模式具有显著信息价值。