Anchorage: Visual Analysis of Satisfaction in Customer Service Videos Via Anchor Events

• Main Authors: WONG, Kam Kwai, WANG, Xingbo, WANG, Yong, HE, Jianben, ZHANG, Rong, QU, Huamin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Behavioral sciences; Customer satisfaction; Customer services; Data visualization; video data; video visualization; Videos; visual analytics; Visual analytics; Visualization; Broadcast and Video Studies; Numerical Analysis and Scientific Computing; Sales and Merchandising
• Online Access: https://ink.library.smu.edu.sg/sis_research/7792; https://ink.library.smu.edu.sg/context/sis_research/article/8795/viewcontent/Anchorage_av.pdf
• Institution: Singapore Management University

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 , 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. 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 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.