2023
Working Paper
HBS Working Paper Series

Using GPT for Market Research

By: James Brand, Ayelet Israeli and Donald Ngwe

Format:Electronic
| Language:English
| Pages:34

Abstract

Large language models (LLMs) have quickly become popular as labor-augmenting tools for programming, writing, and many other processes that benefit from quick text generation. In this paper we explore the uses and benefits of LLMs for researchers and practitioners who aim to understand consumer preferences. In contrast to prior work, we focus on the distributional nature of LLM responses, and query GPT to generate hundreds of survey responses to each prompt. We offer two sets of results to illustrate our approach and assess it. First, we show that the Generative Pre-trained Transformer 3 (GPT-3) model, a widely-used LLM, responds to sets of survey questions in ways that are consistent with economic theory and well-documented patterns of consumer behavior, including downward-sloping demand curves and state dependence. Second, we show that estimates of willingness-to-pay for products and features generated by GPT-3 are of realistic magnitudes and match estimates from a recent study that elicited preferences from human consumers. We also offer preliminary guidelines for how best to query information from GPT-3 for marketing purposes and discuss potential limitations.