Synthetic Consumer Insight Generation with Large Language Models
This research investigates the feasibility of using large language models (LLMs) to generate synthetic consumer data for projective techniques. By comparing LLM and human responses on city tourism perceptions across multiple tasks, the study finds substantial overlap in broad topics but significant differences in style, linguistic structure, and diversity generation. Recommendations are provided for optimal LLM use and recognition of limitations.
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[Submitted on 7 Jul 2026]
Title:Synthetic Consumer Insight Generation with Large Language Models
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Abstract:Modern data-driven marketing relies on large amounts of consumer data, yet collecting such data can be costly, time-consuming, and difficult to scale. This research examines whether large language models (LLMs) can be used to generate synthetic consumer data for projective techniques, a set of methods designed to elicit consumer associations, emotions, wants, and needs. We test LLM-generated responses across multiple projective tasks, LLMs, prompting strategies, and temperature settings, and compare them with human responses from a primary research study on perceptions of city tourism destinations. Human and LLM responses were analyzed using linguistic measures, diversity and concentration metrics, topic models, and top-term analyses. The results show substantial overlap between human and LLM responses in broad topics and associations, but also important differences in style, linguistic structure, and the way diversity is generated. Recommendations are given on how to best utilize LLMs for generating synthetic consumer data, how model and prompt choices shape response quality, and on recognizing the limitations of LLM synthetic consumer data generation.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.05761 [cs.AI]
(or arXiv:2607.05761v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.05761
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Stephen L. France [view email] [v1] Tue, 7 Jul 2026 02:38:41 UTC (985 KB)
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