  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.

Human-Computer Interaction;Multimedia