529: Is this the best AI-powered market research approach? – with Carmel Dibner

How AI captures customer needs that human product managers miss Watch on YouTube TLDR In my recent conversation with Carmel Dibner from Applied Marketing Science, we explored how artificial intelligence is transforming Voice of the Customer (VOC) research for product teams. The collaboration between AMS and MIT researchers has yielded impressive results, with AI tools not only matching human analysts in identifying customer needs but often exceeding them—especially for emotional needs that humans might overlook. Rather than replacing human researchers, AI serves as a copilot, helping product teams uncover twice as many unique needs while reducing analysis time and eliminating bias. This hybrid approach offers tremendous potential for innovation, particularly in the early stages of product development. Key Topics * AI can now match or exceed human analysts in identifying customer needs from research data * Large Language Models (LLMs) are surprisingly effective at capturing emotional needs that humans often miss * AI finds twice as many unique customer needs compared to human analysts alone * The most effective approach is using AI as a “copilot” alongside human researchers * AI tools significantly speed up data analysis and can process multiple data sources simultaneously * These tools can find niche needs that create innovation opportunities * AI still has limitations in prioritizing needs and assessing the validity of different data sources Introduction Voice of the Customer research has been a cornerstone of product management for decades. But it is changing, with AI tools that are transforming how we uncover and analyze customer needs. While some fear AI might miss the human element of customer research, recent advancements show it can actually help us capture more nuanced emotional needs while eliminating human bias. Joining us is returning guest, Carmel Dibner, who is a principal and co-owner at Applied Marketing Science (AMS), where she has helped companies uncover critical customer insights to improve products, services, and customer experiences. Before moving to consulting she was in brand management at Unilever. More recently, she has collaborated with AI researchers at MIT to improve VOC outcomes. I regard Applied Marketing Science, Carmel’s company, as the thought leaders in VOC research, and it was the first organization to formalize the VOC interview process. In this discussion, we’ll explore how LLMs are revolutionizing Voice of the Customer analysis. Carmel will share results of experiments where AI not only matched human analysts in extracting customer insights but excelled at finding hidden needs – unmet needs that could unlock your next innovation opportunity and create competitive advantage. Whether you’re skeptical about AI in customer research or eager to embrace it, this discussion will challenge your assumptions about the future of Voice of the Customer analysis. The AI Revolution in Voice of the Customer Research Early AI Experiments (2017-2018) AMS began experimenting with artificial intelligence for customer research around 2017-2018. Their initial focus was on developing algorithms that could effectively analyze textual data and extract meaningful customer insights. However, these early efforts faced significant limitations. The AI could identify potentially useful information,