Generative AI in Modern Marketing: Content, Branding, and Product Discovery
(A2026-134058)

AUTHORS

Malik Stromberg, WU Vienna; Xiongkai Tan, Technical University of Munich; Evgenii Gradusov, WU Vienna; Gabriela Makar, WU Vienna

ABSTRACT

Generative AI continues to rapidly redefine how firms understand consumers, craft communications, and compete in increasingly AI-driven digital marketplaces. Across marketing domains, AI tools are reshaping both strategic decision-making and operational practices. This special session assembles four cutting-edge working papers addressing these intricate challenges. All four papers share a unifying theme: leveraging generative AI to better understand and influence how consumers discover, evaluate, and engage with products and brands. The papers offer methodological innovations and empirical insights that explicate the evolving interplay between AI systems, market actors, and digital information flows holding significant insights for both practitioners and scholars alike. The first paper, “The Narrative Campaign Generator: Weaving Consumer Reviews into LLMs Using Graph RAG” by Madhav Arora, Malik Stromberg, Arnaud De Bruyn, and Thomas Reutterer proposes a novel Review Graph RAG framework that provides LLMs with context from consumer reviews, enriching general-purpose models with authentic product information enabling them to craft campaigns that resonate more deeply with target audiences. The second paper, “Personal Brand Transformation and Professional Success: Evidence from the Motion Picture Industry Using a Large Vision–Language Model” by Qiong Tang, Xiongkai Tan, and Lily Xuehui Gao examines how professionals leverage their existing personal brand to achieve various professional outcomes in the movie industry. Using a vision large language model and network analysis, the authors capture the dynamics of personal brand image and analyze how the strategic use of personal brand equity shapes these outcomes. The third paper, “When AI Chooses for You: How LLM-Based Shopping Assistants Shape Product Discovery” by Evgenii Gradusov, Martin Reisenbichler, Thomas Reutterer, and Xiao Liu examines how LLM-based shopping assistants shape product recommendations and the relationship between consumers, sellers, and platforms. Using multimodal analysis across product categories, they find a systematic bias toward already successful brands, limiting visibility for newcomers. LLM-listed products are few and strongly skewed towards higher prices, raising concerns about consumer choice, market access, and the evolving dynamics of conversational commerce. In the fourth paper, “The Battle for Visibility: Engineering Website Content That Captures Generative Search Engine Attention” Gabriela Makar, Martin Reisenbichler, Thomas Reutterer, and David Schweidel develop and test an automated workflow that generates and optimizes website content to rank in LLM-based search systems such as searchGPT or Bing Copilot. Their GEO (Generative Engine Optimization) framework identifies the content features LLMs rely on, then automatically produces and refines webpages to maximize visibility in LLM responses. Field experiments and large-scale evaluations show how targeted content alignment improves a brand’s presence in AI search results. Collectively, these studies develop our understanding of how AI systems increasingly mediate what consumers notice, evaluate, and ultimately choose in today’s marketplaces. The session provides actionable implications for managers dealing with AI-mediated touchpoints and offers a fertile ground fostering scholarly discussions about the modern marketing landscape in an increasingly AI-shaped environment.