Phone calls are an essential communication channel in today’s contact centers, but they are more difficult to analyze than written or form-based interactions. To that end, companies have traditionally used surveys to gather feedback and gauge customer satisfaction. In this work, we study the relationship between self-reported customer satisfaction (CSAT) and automatic utterance-level indicators of emotion produced by affect recognition models, using a real dataset of contact center calls. We find (1) that positive valence is associated with higher CSAT scores, while the presence of anger is associated with lower CSAT scores; (2) that automatically detected affective events and CSAT response rate are linked, with calls containing anger/positive valence exhibiting respectively a lower/higher response rate; (3) that the dynamics of detected emotions are linked with both CSAT scores and response rate, and that emotions detected at the end of the call have a greater weight in the relationship. These findings highlight a selection bias in self-reported CSAT leading respectively to an over/under-representation of positive/negative affect.

电话呼叫是当今联络中心的重要沟通渠道，但它们比书面或基于表单的交互更难分析。为此，公司传统上使用调查来收集反馈并衡量客户满意度。在这项工作中，我们使用联络中心呼叫的真实数据集，研究自我报告的客户满意度 (CSAT) 与情感识别模型产生的自动言语级别情绪指标之间的关系。我们发现 (1) 正价与较高的 CSAT 分数相关，而愤怒的存在与较低的 CSAT 分数相关；(2) 自动检测到的情感事件与 CSAT 响应率相关联，包含愤怒/正价的呼叫分别表现出较低/较高的响应率；(3) 检测到的情绪动态与 CSAT 分数和响应率相关，并且在通话结束时检测到的情绪在这种关系中具有更大的权重。这些发现凸显了自我报告的 CSAT 中的选择偏差，分别导致积极/消极影响的过度/不足。