AI 中文总结
该研究用大语言模型注释消费品公司对话文本，分解客户服务满意度为多轴，验证注释与客户自评的关系，发现轴间相关性及共线性，指出分解价值在于归因和覆盖，能识别对话数据中细微的客户体验驱动因素。
AI 中文摘要
我们使用大语言模型（GPT - 4.1）对一家全球消费品公司约9000条支持对话文本进行注释，将客户服务满意度分解为多个组成轴（整体、客服、结果、产品和客户努力），并根据客户给自己的满意度评级验证了大语言模型的注释。五个轴中有四个与自我报告的满意度紧密相关，产品满意度与可用代理相比则较弱。未调整的相关性也低估了一致性，排除严重分歧后整体相关性上升。这些轴高度共线，添加到总分中并不能改善对客户评级的预测，分解的价值在于归因和覆盖。且全面统计的满意度明显低于调查报告。分解满意度作为一种方法的前景是能够在对话数据中识别更细微的客户体验驱动因素。
英文摘要
We used a large language model (GPT-4.1) to annotate the text of about 9,000 support conversations at a global consumer-goods firm, decomposing customer-care satisfaction into component axes (overall, agent, outcome, product, and customer effort), and validated the LLM annotations against the satisfaction ratings customers gave themselves. Four of five axes track self-reported satisfaction closely (overall, agent, and outcome near an unadjusted 0.65; effort -0.54), while product satisfaction is weak against the available proxy. The unadjusted correlation also understates the alignment: the disagreements concentrate in a small, readable tail of divergent sessions rather than in general drift, and the overall correlation rises to 0.811 when only the severe divergences are excluded and to 0.914 when the full divergent tail is excluded. The axes are also highly collinear, and adding them to the overall score does not improve prediction of the customer's rating, the decomposition's value is not incremental prediction but attribution and coverage. And, with greater coverage the picture of the data changes. Read on every contact rather than the few that return a survey, satisfaction is markedly lower than the survey reports (a full-census 2.91 against the surveyed 3.62 on a five-point scale). The promise of decomposed satisfaction as a methodology is the ability to identify more nuanced drivers of customer experience in conversational data.
Comments25 pages, 7 figures, 6 tables
