AI Just Beat Experts at Understanding Customer Needs
The one thing you need to know in AI today | AI Ready CMO
A fine-tuned LLM just identified 100% of customer needs from product reviews and interviews. Professional analysts, using the same data, caught 87.5%.
If you know anything about Voice-of-Customer studies, you know this shouldn’t be possible.
Understanding customer needs isn’t about parsing text—it’s about recognizing unstated desires. When someone complains their wood stain “goes on pink,” they’re not asking for a different color. They’re revealing a need to see which surfaces they’ve already covered. Analysts train for years to make these leaps.
And base AI models, indeed, failed spectacularly—identifying real customer needs only 40% of the time, worse than humans. The breakthrough came from supervised fine-tuning with about 1,000 professionally-extracted customer needs from past Voice of Customer studies.
Turns out, with the right training data, AI does it better.
MIT researchers partnered with Applied Marketing Science, a firm with 30 years of VOC experience. They trained the model on real studies spanning ten product categories. Once trained, it processed entire datasets without fatigue—all 14,341 customer reviews in the wood stain study, not just the curated subset analysts typically review due to time constraints.
And it didn’t hallucinate. When evaluators checked whether extracted needs followed from source material, the fine-tuned LLM scored 92% versus 84% for human analysts.
The practical test: a professional product development association needed to understand member experience. Standard consulting project, $50K minimum, months of work. They uploaded interview transcripts to the fine-tuned model instead. Got results in minutes. The insight: new members found insider jargon off-putting, so they redesigned onboarding to be more accessible.
It is not just about quality. Customer insight has always been episodic:you commission a study, get a report, make decisions, then operate blind for months. By the time you realize a need has shifted, you’re behind.
If extraction is automated, you can monitor continuously. Product teams track how needs evolve as features ship. Support surfaces emerging needs from call transcripts in real-time. Marketing identifies gaps before competitors.
The feedback loop shrinks from quarters to days.
One caveat: this only works if you do it right. You need quality training data and proper fine-tuning. But for anyone sitting on past VOC studies, interview transcripts, or review data, you already have what you need. The methodology generalizes across categories.
If you’ve been treating customer needs analysis as something only specialists can do, that assumption just got invalidated.
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