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

Researchers propose a novel statistical framework for integrating Large Language Model-generated data with real human data in conjoint analysis, addressing the bias gap between synthetic and authentic consumer responses. The approach delivers 24.9-79.8% cost and data savings while maintaining statistical robustness, validating that LLM data serves as a complement rather than substitute for human market research.

The research addresses a critical limitation in applying LLMs to market research: while these models excel at generating human-like text, their synthetic responses diverge from authentic consumer behavior in systematic ways. This gap between LLM-generated and real data creates bias when researchers naively substitute one for the other, undermining the validity of market preference analysis. The proposed data augmentation methodology solves this by establishing a statistical framework that weighs and combines both data sources to produce unbiased estimators with proven asymptotic properties.

This work emerges from the broader economic pressure to reduce market research costs while maintaining scientific validity. Traditional survey-based conjoint analysis requires extensive real respondents, making it expensive and time-consuming. LLMs offered an apparent shortcut, but earlier findings revealed that direct substitution amplified rather than reduced estimation error. The new framework transforms this limitation into an opportunity by positioning LLM-generated data as a legitimate input when properly calibrated.

For businesses conducting market research and survey platforms, this research validates a cost-reduction pathway without sacrificing reliability. Companies can deploy LLM data for augmentation rather than replacement, achieving measurable savings while preserving statistical confidence. The empirical validation across distinct domains—COVID-19 vaccine preferences and sports car choices—demonstrates generalizability beyond narrow use cases.

The framework's practical impact hinges on adoption by market researchers and analytics firms. As organizations scale consumer preference research, this methodology provides a principled approach to leverage AI-generated data. Future work likely extends this approach to other domains requiring synthetic data augmentation, establishing LLMs as cost-effective complements to traditional research methodologies rather than unreliable replacements.

• →LLM-generated data introduces systematic bias when directly substituted for human survey responses in market research.
• →A novel statistical augmentation framework achieves 24.9-79.8% cost and data savings while maintaining estimator robustness.
• →LLM data functions optimally as a complement to real data within rigorous statistical frameworks, not as a standalone replacement.
• →The methodology produces asymptotically normal estimators with finite-sample performance bounds, ensuring statistical validity.
• →Validation across vaccine preference and automotive choice studies demonstrates the framework's generalizability across domains.