# Transforming the Voice of the Customer: Large Language Models for Identifying Customer Needs

arXiv: https://arxiv.org/abs/2503.01870 · HTML: https://mlsanity.com/paper/2503.01870/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2025.

## Built on by (most cited first, 3 of 3)

- [From Model Choice to Model Belief: Establishing a New Measure for LLM-Based Research](https://mlsanity.com/paper/2512.23184/) (2025)
- [Computational KJ-Ho: An Analyst-Bias-Free Insight Extraction Framework from Large-Scale Qualitative Data Using Domain-Specialized LLMs](https://mlsanity.com/paper/2608.16467/) (2026)
- [AI-Moderated Interviews for Market Research and Digital Twins Calibration](https://mlsanity.com/paper/2609.29143/) (2026)

## Abstract (arXiv)

Identifying customer needs (CNs) is fundamental to product innovation and marketing strategy. Yet for over thirty years, Voice-of-the-Customer (VOC) applications have relied on professional analysts to manually interpret qualitative data and formulate "jobs to be done." This task is cognitively demanding, time-consuming, and difficult to scale. While current practice uses machine learning to screen content, the critical final step of precisely formulating CNs relies on expert human judgment. We conduct a series of studies with market research professionals to evaluate whether Large Language Models (LLMs) can automate CN abstraction. Across various product and service categories, we demonstrate that supervised fine-tuned (SFT) LLMs perform at least as well as professional analysts and substantially better than foundational LLMs. These results generalize to alternative foundational LLMs and require relatively "small" models. The abstracted CNs are well-formulated, sufficiently specific to guide innovation, and grounded in source content without hallucination. Our analysis suggests that SFT training enables LLMs to learn the underlying syntactic and semantic conventions of professional CN formulation rather than relying on memorized CNs. Automation of tedious tasks transforms the VOC approach by enabling the discovery of high-leverage insights at scale and by refocusing analysts on higher-value-added tasks.