Demand Estimation with Text and Image Data

2025-03-26

Social Science Research Network

AI Summary

Traditional demand estimation struggles to quantify implicit product attributes—such as visual design—leading to biased modeling of substitution relationships. Method: We propose a novel method that automatically infers consumer substitution preferences from multimodal unstructured data (images and text) by jointly embedding visual and textual features via CLIP and BERT, and directly integrating these embeddings into a random-coefficients logit model—eliminating the need for manual attribute specification and enabling end-to-end learning of latent preferences and substitution structures. We combine choice experiment data with a counterfactual prediction framework. Results: Our approach significantly improves prediction accuracy for second-best alternatives in experiments. Empirically applied across 40 Amazon product categories, it consistently identifies substitution sets better aligned with market intuition. The method establishes a scalable, interpretable paradigm for demand modeling in high-dimensional, unstructured environments.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences
• Computer Vision: Multi-modal Vision

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We propose a demand estimation method that leverages unstructured text and image 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 random coefficients logit model. This approach enables researchers to estimate demand even when they lack data on product attributes or when consumers value hard-to-quantify attributes, such as visual design or functional benefits. Using data from a choice experiment, we show that our approach outperforms standard attribute-based models in counterfactual predictions of consumers' second choices. We also apply it across 40 product categories on Amazon and consistently find that text and image data help identify close substitutes within each category.

Problem

• Estimating demand using text and image data
• Identifying product substitutes without attribute data
• Improving counterfactual predictions with deep learning

Innovation

• Leverages text and image data
• Uses pre-trained deep learning models
• Incorporates embeddings into logit model