Publications
Publications
- 2023
- HBS Working Paper Series
Using GPT for Market Research
By: James Brand, Ayelet Israeli and Donald Ngwe
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. We focus on the distributional
nature of LLM responses, and query the Generative Pre-trained Transformer 3.5 (GPT-3.5)
model 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 GPT-3.5, a widelyused
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-3.5 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-3.5 for marketing
purposes and discuss potential limitations.
Keywords
Large Language Model; Research; AI and Machine Learning; Analysis; Customers; Consumer Behavior; Technology Industry; Information Technology Industry
Citation
Brand, James, Ayelet Israeli, and Donald Ngwe. "Using GPT for Market Research." Harvard Business School Working Paper, No. 23-062, April 2023. (Revised July 2023.)