Demand Estimation with Text and Image Data
提出一种利用产品图片和文字描述等非结构化数据来推断消费者替代模式的需求估计方法，通过预训练深度学习模型提取特征并纳入混合Logit模型，在实验和亚马逊数据上表现优于传统方法。
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.