Leveraging the Power of Generative AI for Branding and (Online) Marketing
(A2024-119574)

AUTHORS

Martin Reisenbichler, Vienna University of Economics and Business; Tijmen Jansen, University of Hamburg; Yannick Exner, Technical University of Munich; Maximilian Konrad, Technical University Munich

ABSTRACT

Generative AI is a transformative force reshaping the landscape of the marketing industry across various domains, notably in online advertising, branding and marketing analytics. However, despite its disruptive potential, numerous critical questions linger within the industry. These unresolved issues span diverse areas, including the selection of appropriate AI models, tackling specific optimization challenges such as balancing long-term branding objectives versus short-term sales goals in visual ad generation, and addressing concerns related to content optimization and the equilibrium of campaign running costs in textual search engine advertising. This special session assembles an array of high-tier working papers designed to address and navigate these intricate challenges. Over the course of the session, these papers contribute valuable insights that hold significant relevance for both practitioners and scholars alike. The first paper, “One Model Fits All? Explore the Zero-Shot Capabilities of Multimodal Large Language Models for Automated Marketing Image Analytics” by Maximilian Konrad and Jochen Hartmann, is the prime publication to assess the efficacy of the novel area of multimodal Large Language Models (spanning image and text data) in automated marketing analytics. The second paper, “The Power of Generative Marketing: Can Generative AI Reach Human-Level Visual Marketing Content?” by Yannick Exner, Jochen Hartmann and Samual Domdey, provides principal insights into generative AI model design and architectures for marketing optima exemplified on text-to-image diffusion models in social media and online advertisement. The third 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 for balancing long term branding objectives as well as short term sales goals for the prime marketing application of online display (visual) advertising. The fourth paper, “Applying Large Language Models to Sponsored Search Advertising” by Martin Reisenbichler, Thomas Reutterer, and David A. Schweidel explores the possibility to adapt Large Language Models to Search Engine Advertising (SEA) as billion dollar industry and prime marketing channel – adapting the generative model’s architecture to SEA needs, showcasing performance implications of content and cost optimization tradeoffs, and exploring boundary conditions of gen AI applications in SEA. 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.