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

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 tedi...

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Main Authors: WONG, Kam Kwai, WANG, Xingbo, WANG, Yong, HE, Jianben, ZHANG, Rong, QU, Huamin
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Language:English
Published: Institutional Knowledge at Singapore Management University 2023
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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
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-87952023-04-04T03:16:39Z Anchorage: Visual Analysis of Satisfaction in Customer Service Videos Via Anchor Events WONG, Kam Kwai WANG, Xingbo WANG, Yong HE, Jianben ZHANG, Rong QU, Huamin 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. 2023-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7792 info:doi/10.1109/TVCG.2023.3245609 https://ink.library.smu.edu.sg/context/sis_research/article/8795/viewcontent/Anchorage_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Behavioral sciences Customer satisfaction 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
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Behavioral sciences
Customer satisfaction
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
spellingShingle Behavioral sciences
Customer satisfaction
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
WONG, Kam Kwai
WANG, Xingbo
WANG, Yong
HE, Jianben
ZHANG, Rong
QU, Huamin
Anchorage: Visual Analysis of Satisfaction in Customer Service Videos Via Anchor Events
description 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.
format text
author WONG, Kam Kwai
WANG, Xingbo
WANG, Yong
HE, Jianben
ZHANG, Rong
QU, Huamin
author_facet WONG, Kam Kwai
WANG, Xingbo
WANG, Yong
HE, Jianben
ZHANG, Rong
QU, Huamin
author_sort WONG, Kam Kwai
title Anchorage: Visual Analysis of Satisfaction in Customer Service Videos Via Anchor Events
title_short Anchorage: Visual Analysis of Satisfaction in Customer Service Videos Via Anchor Events
title_full Anchorage: Visual Analysis of Satisfaction in Customer Service Videos Via Anchor Events
title_fullStr Anchorage: Visual Analysis of Satisfaction in Customer Service Videos Via Anchor Events
title_full_unstemmed Anchorage: Visual Analysis of Satisfaction in Customer Service Videos Via Anchor Events
title_sort anchorage: visual analysis of satisfaction in customer service videos via anchor events
publisher Institutional Knowledge at Singapore Management University
publishDate 2023
url https://ink.library.smu.edu.sg/sis_research/7792
https://ink.library.smu.edu.sg/context/sis_research/article/8795/viewcontent/Anchorage_av.pdf
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