Webinars
From Reviews to Responses: Using AI and RAG to Improve Customer Q&A
Consumers increasingly rely on online Q&A when making purchase decisions, yet many platforms still struggle with slow responses, inconsistent participation, and low-quality answers. At the same time, customer reviews contain valuable real-world insights that are difficult to use effectively in traditional QA systems.
In this webinar, we present an AI-enhanced QA framework that combines Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to generate more accurate, context-rich answers using customer reviews. Using a large-scale Amazon dataset, we show how the approach significantly improves answer quality, particularly for subjective and experience-based questions, while helping organizations scale trustworthy customer support and product discovery.
Attendees will gain practical insights into how AI-powered QA systems can leverage existing customer-generated content to improve engagement, customer trust, and purchase decision-making.
speakers
Neeraj Arora is Arthur C. Nielsen, Jr. Chair in Marketing Research and Education at the University of Wisconsin-Madison, where he also serves as the academic director of the Marketing Leadership Institute. He currently serves as a policy board member for the Journal of Consumer Research and has served as an associate editor for Journal of Marketing Research and Journal of Marketing. He has an undergraduate degree in engineering from Delhi University, and an MBA and Ph.D. from The Ohio State University. Professor Arora’s current research interests in marketing are tech-centered and include large language models, digital marketing effectiveness, and artificial intelligence. His past research focused on statistical models of consumer choice. Typical contexts for this research include brand choices made by individuals and groups. His research has appeared in the Journal of Marketing Research, Marketing Science, Journal of Marketing, Journal of Consumer Research, Journal of Retailing, International Journal of Research in Marketing, Customer Needs and Solutions, and Marketing Letters. Professor Arora teaches undergraduate, MBA, MSBA, and PhD students. His teaching interests include marketing analytics and marketing research. He has served as a business consultant in a variety of areas that include marketing analytics, research methodologies and legal matters.
Ishita Chakraborty is an Assistant Professor and the Thomas and Charlene Landsberg Smith Faculty Fellow in the Wisconsin School of Business in UW Madison. Her research interests are in the applications of unstructured data and AI in marketing. Her research has been published in premier marketing journals like Journal of Marketing, Journal of Marketing Research, Marketing Science and International Journal of Marketing Research and has been awarded the AMA/MSI Paul Root Award (2026), AMA AI SIG Award (2024) and Donald R. Lehmann Award (2023). Ishita has a Ph.D. in Quantitative Marketing from Yale University and an MBA from the Indian Institute of Management. Prior to her doctoral studies at Yale, she worked in marketing, sales and consulting in the Asia Pac region for Accenture Management Consulting and Mondelez International.