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Summary
The study in this MSI working paper evaluates whether large language models (LLMs) can serve as a reliable source of consumer preference data—potentially transforming how market research is conducted. Using conjoint-style survey questions, the researchers compared LLM-generated choices with human responses to estimate willingness-to-pay (WTP) for a variety of product attributes. They find that LLMs often approximate human preferences surprisingly well, especially when fine-tuned with prior survey data, though important limitations remain. For marketers, the research highlights both the promise and the boundaries of using AI-generated insights to accelerate testing, concept screening and early-stage innovation work
