Delivering customer services through video communications has brought new opportunities to analyze customer satisfaction for quality management. However, due to the lack of reliable self-reported responses, service providers are troubled by the inadequate estimation of customer services and the tedious investigation into multimodal video recordings. We introduce Anchorage, a visual analytics system to evaluate customer satisfaction by summarizing multimodal behavioral features in customer service videos and revealing abnormal operations in the service process. We leverage the semantically meaningful operations to introduce structured event understanding into videos which help service providers quickly navigate to events of their interest. Anchorage supports a comprehensive evaluation of customer satisfaction from the service and operation levels and efficient analysis of customer behavioral dynamics via multifaceted visualization views. We extensively evaluate Anchorage through a case study and a carefully-designed user study. The results demonstrate its effectiveness and usability in assessing customer satisfaction using customer service videos. We found that introducing event contexts in assessing customer satisfaction can enhance its performance without compromising annotation precision. Our approach can be adapted in situations where unlabelled and unstructured videos are collected along with sequential records.

翻译：通过视频通信提供客户服务为质量管理的满意度分析带来了新机遇。然而，由于缺乏可靠的自陈式反馈，服务提供者常因客户服务质量评估不足以及对多模态视频录制的繁琐调研而困扰。我们提出Anchorage——一种通过归纳客服视频中的多模态行为特征并揭示服务流程中的异常操作来评估客户满意度的可视分析系统。我们利用具有语义意义的操作，将结构化事件理解引入视频，帮助服务提供者快速定位其感兴趣的事件。Anchorage支持从服务和操作层面对客户满意度进行综合评估，并通过多维度可视化视图实现客户行为动态的高效分析。我们通过案例研究和精心设计的用户研究对Anchorage进行了全面评估。结果表明，该系统在利用客户服务视频评估满意度方面具有有效性和可用性。我们发现在评估客户满意度时引入事件上下文可在不降低标注精度的前提下提升性能。本方法可适用于收集到未标注、非结构化视频及顺序记录的场景。