用大模型从用户评论中提取可操作的属性与特征，助力企业提升满意度和收入。
From Reviews to Actionable Insights: An LLM-Based Approach for Attribute and Feature Extraction
- 基于营销理论区分感知属性与可行动特征，通过提示工程实现精准提取。
- 在2万条星巴克点评上验证，模型与人工一致率达高，处理速度提升180倍。
- 输出可落地的洞察，适合企业做客户体验优化与数据驱动决策。
本研究提出一种系统性大语言模型（LLM）方法，从顾客评论中提取产品与服务的属性、特征及其关联情感。基于营销理论，框架区分感知属性与可行动特征，生成可解释且管理可用的洞察。将该方法应用于2万条星巴克门店的Yelp评论，评估了八个提示变体在随机抽样评论上的表现。通过与人工标注的一致性及对顾客评分的预测效度来评估模型性能。结果显示，LLM与人工编码者高度一致，且具备强预测有效性，证实方法可靠性。人工编码平均需6分钟/篇，而模型仅需2秒/篇，实现人工无法企及的规模。分析识别出显著影响顾客满意度的关键属性与特征及其情感倾向，帮助企业定位‘愉悦点’与‘痛点’，设计针对性改进措施。我们展示了如何构建结构化评论数据驱动的可操作营销仪表盘，实现跨门店、跨时间的情感追踪、绩效对比及高杠杆改进项识别。模拟表明，提升关键服务特征的情感可带来每店平均1%-2%的收入增长。
原文摘要 · Abstract (English)
This research proposes a systematic, large language model (LLM) approach for extracting product and service attributes, features, and associated sentiments from customer reviews. Grounded in marketing theory, the framework distinguishes perceptual attributes from actionable features, producing interpretable and managerially actionable insights. We apply the methodology to 20,000 Yelp reviews of Starbucks stores and evaluate eight prompt variants on a random subset of reviews. Model performance is assessed through agreement with human annotations and predictive validity for customer ratings. Results show high consistency between LLMs and human coders and strong predictive validity, confirming the reliability of the approach. Human coders required a median of six minutes per review, whereas the LLM processed each in two seconds, delivering comparable insights at a scale unattainable through manual coding. Managerially, the analysis identifies attributes and features that most strongly influence customer satisfaction and their associated sentiments, enabling firms to pinpoint "joy points," address "pain points," and design targeted interventions. We demonstrate how structured review data can power an actionable marketing dashboard that tracks sentiment over time and across stores, benchmarks performance, and highlights high-leverage features for improvement. Simulations indicate that enhancing sentiment for key service features could yield 1-2% average revenue gains per store.
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