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

[2503.01870] Transforming the Voice of the Customer: Large Language Models for Identifying Customer Needs Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2503.01870 (cs) [Submitted on 25 Feb 2025 ( v1 ), last revised 8 Apr 2026 (this version, v2)] Title: Transforming the Voice of the Customer: Large Language Models for Identifying Customer Needs Authors: Artem Timoshenko , Chengfeng Mao , John R. Hauser View a PDF of the paper titled Transforming the Voice of the Customer: Large Language Models for Identifying Customer Needs, by Artem Timoshenko and 2 other authors View PDF Abstract: 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. Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); General Economics (econ.GN) Cite as: arXiv:2503.01870 [cs.CL] (or arXiv:2503.01870v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2503.01870 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Chengfeng Mao [ view email ] [v1] Tue, 25 Feb 2025 21:55:35 UTC (1,394 KB) [v2] Wed, 8 Apr 2026 18:51:54 UTC (2,872 KB) Full-text links: Access Paper: View a PDF of the paper titled Transforming the Voice of the Customer: Large Language Models for Identifying Customer Needs, by Artem Timoshenko and 2 other authors View PDF view license Current browse context: cs.CL < prev | next > new | recent | 2025-03 Change to browse by: cs cs.AI cs.HC econ econ.GN q-fin q-fin.EC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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