How product managers can leverage AI to analyze and act on customer feedback
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TLDR
In this episode, I’m interviewing John Mitchell, President of Applied Marketing Science (AMS), on the evolving role of AI in Voice of the Customer (VOC) research. We discuss how AI is transforming the way companies capture customer needs, the importance of thoughtful customer research design, common pitfalls, and the critical balance between leveraging human insight and machine learning. John gives practical advice on asking better questions, the value of storytelling, and real-world examples demonstrate both the power and current limitations of AI-driven VOC.
Introduction
Today we’re talking about how we understand customers’ unmet needs and problems. There’s a lot of information these days in reviews, social media posts, and other online mechanisms, and we’re probably missing a lot of it. There are some tools out there that could help us do a better job understanding what’s going on. We’ll dive into just what some of the options are for us and also how AI is impacting this.
Our guest is John Mitchell. He is the president of Applied Marketing Science (AMS). John has spent over 20 years helping Fortune 500 companies and startups alike understand their customers better. He has trained over a thousand people on Voice of the Customer (VOC) approaches and led customer-insight workshops at McKinsey, Innosight, and Vistaprint before coming back to lead AMS. Whether you’re pioneering VOC research in your organization or just getting into how AI can help us with this, AMS has a deep history with this that John has been part of.
This episode was recorded live at the Ignite Innovation Conference, the Product Development and Management Association’s annual conference. To find out more about PDMA and how it can help your career, go to PDMA.org.
Summary of Concepts Discussed for Product Managers
The Evolution of VOC & AI’s Role:
John Mitchell traces VOC from face-to-face interviews and observational research to today’s use of AI and machine learning, highlighting how AI accelerates insights, uncovers overlooked data, and helps companies extract more value from existing information.
Common VOC Mistakes:
Many teams underprepare for VOC research, talk only to “A-list” or friendly customers, or simply ask customers what they want (which often leads to generic answers). Deep customer understanding and competitive advantage come from well-structured research and targeting a broader audience.
Asking Better Questions:
Instead of generic questions, John Mitchell recommends getting customers to tell detailed stories about their experiences, which reveal both functional and emotional needs.
AI Tools in VOC:
There are three main AI approaches for VOC work: open tools (with noted security risks), secure commercial data analysis platforms, and purpose-built tools trained to articulate customer needs from large data sources. AI extends and enhances human analysis, often finding emotional unmet needs that people might miss.
Synthetic Customers:
While not yet widely adopted by AMS, synthetic personas have potential for rapid concept evaluation—though John Mitchell cautions against over-reliance, emphasizing that real human feedback remains irreplaceable.
Real-World Example:
In the paint and stains category, AMS compared traditional human VOC analysis to an AI model, finding that a combined approach surfaced the most comprehensive set of customer needs.
Voice Recorder Tip:
John recommends that every product researcher should record their customer interviews (using a dedicated device), ensuring nothing is missed and allowing for high-quality AI or human analysis downstream.
Useful Links
Innovation Quote
“ If I had asked customers what they want, they would have said a faster horse.” – attributed to Henry Ford
Application Questions
- How could you leverage AI tools to analyze existing customer feedback more efficiently without compromising on the depth and quality of insights?
- What steps can you take to ensure your VOC research covers a representative sample of your market, not just your biggest or friendliest customers?
- Reflect on a recent market research effort: did the team fall into any of the common traps discussed (e.g., insufficient preparation, asking customers for solutions)?
- How might you incorporate storytelling into your customer interviews to uncover both functional and emotional needs?
- What balance do you see between AI-driven analysis and human interpretation when making high-stakes product decisions, and where do you feel each adds the most value?
Bio
John has over 25 years’ experience in marketing strategy, market research, and innovation. He specializes in research to support new product development and customer experience design, and has led engagements in the U.S., Latin America, Europe and Asia. John has also trained and coached hundreds of AMS clients to develop their in-house insight capabilities.
He is a RIVA-trained focus group moderator and was formerly a Customer Insights Expert in the Marketing & Sales practice of McKinsey & Company and North America Customer Insights Director at Vistaprint. His clients include leading medical device, technology, and consumer services companies.
Thanks!
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Source
Product Mastery Now for Product Managers, Leaders, and Innovators 