Large Language Models for Market Research: A Data-augmentation Approach

• URL: http://arxiv.org/abs/2412.19363v2
• Date: Mon, 06 Jan 2025 17:33:20 GMT
• Title: Large Language Models for Market Research: A Data-augmentation Approach
• Authors: Mengxin Wang, Dennis J. Zhang, Heng Zhang,
• Abstract summary: Large Language Models (LLMs) have transformed artificial intelligence by excelling in complex natural language processing tasks.<n>Recent studies highlight a significant gap between LLM-generated and human data, with biases introduced when substituting between the two.<n>We propose a novel statistical data augmentation approach that efficiently integrates LLM-generated data with real data in conjoint analysis.
• Score: 3.3199591445531453
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have transformed artificial intelligence by excelling in complex natural language processing tasks. Their ability to generate human-like text has opened new possibilities for market research, particularly in conjoint analysis, where understanding consumer preferences is essential but often resource-intensive. Traditional survey-based methods face limitations in scalability and cost, making LLM-generated data a promising alternative. However, while LLMs have the potential to simulate real consumer behavior, recent studies highlight a significant gap between LLM-generated and human data, with biases introduced when substituting between the two. In this paper, we address this gap by proposing a novel statistical data augmentation approach that efficiently integrates LLM-generated data with real data in conjoint analysis. Our method leverages transfer learning principles to debias the LLM-generated data using a small amount of human data. This results in statistically robust estimators with consistent and asymptotically normal properties, in contrast to naive approaches that simply substitute human data with LLM-generated data, which can exacerbate bias. We validate our framework through an empirical study on COVID-19 vaccine preferences, demonstrating its superior ability to reduce estimation error and save data and costs by 24.9% to 79.8%. In contrast, naive approaches fail to save data due to the inherent biases in LLM-generated data compared to human data. Another empirical study on sports car choices validates the robustness of our results. Our findings suggest that while LLM-generated data is not a direct substitute for human responses, it can serve as a valuable complement when used within a robust statistical framework.

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