将客户之声转变：大型语言模型用于识别客户需求
Transforming the Voice of the Customer: Large Language Models for Identifying Customer Needs
AI总结：
本文研究了大型语言模型在自动化识别客户需求方面的有效性，证明其在多个产品和服务类别中表现优异，优于基础模型，并为VOC应用提供了新的自动化方法。
摘要：
识别客户需求（CNs）是产品创新和营销策略的基础。然而，过去三十年来，客户之声（VOC）应用一直依赖专业分析师手动解读定性数据并提出'要完成的任务'。这项任务具有认知负担、耗时且难以扩展。尽管当前实践使用机器学习来筛选内容，但精确制定CNs的关键最终步骤仍依赖专家人类判断。我们与市场研究人员进行了多项研究，评估大型语言模型（LLMs）能否自动化CN抽象。在各种产品和服务类别中，我们证明了监督微调（SFT）的LLMs至少与专业分析师一样好，并且明显优于基础LLMs。这些结果适用于其他基础LLMs，并且需要相对'小'的模型。抽象出的CNs是良好制定的，足够具体以指导创新，并且基于源内容而不产生幻觉。我们的分析表明，SFT训练使LLMs能够学习专业CN制定的底层语法和语义惯例，而不是依赖记忆中的CNs。自动化繁琐任务通过使大规模发现高价值见解并重新聚焦分析师于更高价值的任务，转变了VOC方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
