Omnichannels & Customer Journey
Presentation: MSI Webinar: 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.
Presentation: MSI Webinar: When AR Backfires: Consumer Trust After the Purchase
Augmented reality (AR) is transforming online shopping, helping brands drive engagement and purchase confidence. But what happens when the product disappoints after the sale?
This webinar explores new research showing that consumers who use AR before purchase are more likely to feel manipulated by brands when post-purchase outcomes are negative, reducing future patronage and trust. Drawing on findings from seven studies, the session highlights the hidden risks of immersive retail technologies and identifies practical strategies firms can use to mitigate backlash, including disclosure design, interface choices, and consumer guidance.
Attendees will gain actionable insights into how to design AR experiences that build customer confidence without undermining long-term brand relationships.
Summary: MSI Webinar: 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.
Generative Personalization with Textual and Non-textual Data
Develops GPT2 (Generative Personalized Targeting with Generative Pre-trained Transformers) which infers user preferences from both textual content and non-textual interaction data for personalized content generation at scale.
The Key to Brand Growth? In Mind, In Stock--and In GenAI Responses
Rather than starting with brands, shoppers' decisions are increasingly mediated by GenAI responses to their inquiries. By recommending a single choice rather than a consideration set, AI mediation can undercut established brands, especially for low-involvement categories. In addition to mental and physical availability, brand growth now depends on AI availability.