Webinars
Assessing Marketing AI: What Practitioners Need to Know
Generative AI is quickly changing how marketing teams work and how they think. This webinar highlights new research showing that leading large language models can reliably access core marketing knowledge across areas like segmentation, consumer behavior, pricing, branding, and strategy.
Analyzing more than 30,000 textbook-based questions, research finds that today’s AI performs at a high level across the marketing canon, often outperforming unprepared humans, while still falling short on complex reasoning, numerical tasks, and context-driven judgment.
For practitioners, the takeaway is clear: AI is becoming an always-on tool for learning and decision support. It can help teams refresh core frameworks, pressure-test ideas, and apply proven principles more consistently. MSI members will leave with a practical view of where AI adds value now and where human insight remains critical.
speakers
Ray is the E.W. Kelley Professor at Indiana University’s Kelley School of Business, and founding director of the School’s Customer Interface Laboratory, a state-of-the-art facility for investigating how customers interact with new retail environments and technologies. His research focuses on understanding the influence of point-of-purchase factors on consumer shopping behavior. His articles have appeared in the Harvard Business Review, Journal of Consumer Research, Journal of Marketing, Journal of Marketing Research, and Marketing Science. Ray teaches the MBA Applied Marketing Research and PhD Advanced Shopper Research courses and was named by Poets & Quants as one of the world’s best b-school professors. Prior to joining IU, he served on the faculties of the Harvard Business School and Wharton. He has consulted for several leading companies in consumer goods and service industries, and his virtual shopping technology has been used by market research firms around the world.
Max Matthe is an Assistant Professor of Marketing at Indiana University’s Kelley School of Business, where he is also affiliated with the Advanced Business Technologies Department. His research explores the intersection of quantitative marketing and modern data science, with a primary focus on market structure, competitive positioning, and the evolution of brand strategies over time. Methodologically, Max leverages machine learning, natural language processing, and causal inference to tackle complex managerial challenges in segmentation, targeting, and positioning, while also examining the practical implications of novel technologies, such as large language models (LLMs), on the marketing function. His empirical work has been published in premier academic journals including Marketing Science. A dedicated educator and recent recipient of the Kelley School's Trustees Teaching Award, Max holds a Ph.D. in Marketing from Goethe University Frankfurt.