Synthetic market research: perceptions, concerns and quality standards
(A2026-134088)

AUTHORS

Simone Griesser, Institute of Communication and Marketing, Lucerne School of Business; Carina Junker, HSLU - Lucerne University of Applied Sciences and Arts Business; Alicia Aregger, HSLU - Lucerne University of Applied Sciences and Arts Business; Nadja Carletti, HSLU - Lucerne University of Applied Sciences and Arts Business; Nicole Schmid, HSLU - Lucerne University of Applied Sciences and Arts Business; Arta Syla, HSLU - Lucerne University of Applied Sciences and Arts Business

ABSTRACT

Large Language Models (LLMs) create new possibilities for market research by generating synthetic data that mimics human responses. Little is known about how marketing professionals perceive this innovation. Understanding their perceptions and quality concerns is critical because adoption depends on trust in data quality. This study combines semi-structured interviews with marketing professionals and synthetic responses generated with Gemini to explore perceptions, usage and quality concerns. Findings show that speed and cost are key benefits while lack of customer interaction, unclear data origin, and risk of incorrect or biased outputs are major drawbacks. Transparency, standardisation, and certification are demanded. While some themes are unique to marketing professionals, others are unique to synthetic respondents. Moreover, answers from synthetic respondents tend to be more thematically similar than answers from marketing professionals. Overall, the results underscore opportunities for synthetic data in market research but emphasize the urgency of certification and trust-building measures.