A recent Ideas Made to Matter article from MIT Sloan highlights new research from MIT and Northwestern showing that an AI model purpose-built for Voice of the Customer (VOC) analysis, and co-developed with Applied Marketing Science (AMS), can match the accuracy of trained human analysts in identifying and classifying customer needs. The AMS team played a key role: supporting the study’s design, providing VOC expertise, and validating the results.
The problem: organizations often overlook that they are sitting on a goldmine of existing customer data. This includes publicly available sources such as social media, reviews and online forums, as well as proprietary data like interview transcripts and call center records. With the explosion of information available, the process of sorting through mountains of data and identifying customer needs (even the most nuanced) can be time-consuming and overwhelming.
The solution: adopting generative AI, specifically, a model that is custom-built for VOC research, enables organizations to efficiently process customer feedback into detailed, actionable insights. Our supervised, finetuned large language model (LLM) was trained with data from decades of professional VOC studies in a similar manner to professional analysts, using iterative feedback and expert guidance. This collaboration supports AMS’s award-winning methodology that blends the best of AI for VOC innovation, decades of customer insights expertise, and academic rigor with practical business impact.
Ready to learn how AI-VOC can help you tackle your most pressing customer insights challenges? Schedule a free consultation with our experts today.