Harnessing Generative AI and Market Research: New Frontiers in Advertising, Product Design, and Consumer Insights
(A2025-125743)

AUTHORS

Martin Reisenbichler, Vienna University of Economics and Business; Malik Stromberg, WU Vienna; Maximilian Konrad, Technical University Munich; Jan Ole Krugmann, Technical University Munich (TUM)

ABSTRACT

Generative AI is a transformative force reshaping the landscape of the marketing industry across various domains, notably in online advertising, product design and market research. However, despite its disruptive potential, numerous critical questions linger within the industry. These unresolved issues span diverse areas, including (1) the applicability and boundary conditions of using generative AI for market research, as well as (2) applying generative AI to estimate consumers’ viewing patterns on visual ads (replacing conventional eye-tracking methods), (3) supporting product search in online stores, and (4) aligning visual advertising with subjective perceptions of consumer segments. This special session assembles an array of working papers designed to address and navigate these intricate challenges. The underlying theme driving all four working papers in this session is to combine market research potential with the power of generative models. Over the course of the session, these papers contribute valuable insights that hold significant relevance for both practitioners and scholars alike. The first paper, “Blind Spots in Broad Strokes: Caveats for the Use of LLMs in Marketing Research” by Malik Stromberg, Wendy W. Moe, Thomas Reutterer, and David A. Schweidel assesses the efficacy and boundary conditions when using LLMs for market research. The second paper, “Artificial Attention: Simulating Top-Down Attention Processes Using Multimodal Large Language Models” by Maximilian Konrad and Jochen Hartmann, explores the use of multimodal large language models to simulate top-down attention in consumer behavior, potentially replacing traditional eye-tracking experiments with synthetic gaze path simulations for market research. The third paper, “From Prompt to Product: Reimagining Visual Search with Generative AI” by Jan Ole Krugmann and Jochen Hartmann explores whether generative AI can be harnessed as a tool to support consumer search for products with desired attributes like a specific shape, size, or color. The fourth paper, “Automated Alignment: Guiding Visual Generative AI for Brand Building and Customer Engagement” by Tijmen Jansen, Mark Heitmann, Martin Reisenbichler and David A. Schweidel, explores the integration of text-to-image generative AI with market research modules for balancing long-term branding objectives as well as short term sales goals for the prime marketing application of online display (visual) advertising. Bridging the gap between theoretical insights and practical applications throughout these papers, this session brings high quality academic and practitioner-oriented research to the table. While this session on generative AI is about training and evaluating models for business and research applications, other special sessions submitted to EMAC are about how consumers and employees interact with generative AI.