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    Data-driven subjective performance evaluation: An attentive deep neural networks model based on a call centre case

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    Publication date
    2022-10
    Author
    Ahmed, Abdelrahman M.
    Sivarajah, Uthayasankar
    Irani, Zahir
    Mahroof, Kamran
    Vincent, Charles
    Keyword
    Subjective evaluation
    Agent performance
    Customer behaviour
    Deep neural network
    Call centre
    Call quality monitoring
    Rights
    © The Authors 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
    Peer-Reviewed
    Yes
    Open Access status
    openAccess
    
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    Abstract
    Every contact centre engages in some form of Call Quality Monitoring in order to improve agent performance and customer satisfaction. Call centres have traditionally used a manual process to sort, select, and analyse a representative sample of interactions for evaluation purposes. Unfortunately, such a process is characterised by subjectivity, which in turn creates a skewed picture of agent performance. Detecting and eliminating subjectivity is the study challenge that requires empirical research to address. In this paper, we introduce an evidence-based machine learning-driven framework for the automatic detection of subjective calls. We analyse a corpus of seven hours of recorded calls from a real-estate call centre using a Deep Neural Network (DNN) for a multi-classification problem. The study draws the first baseline for subjectivity detection, achieving an accuracy of 75%, which is close to relevant speech studies in emotional recognition and performance classification. Among other findings, we conclude that in order to achieve the best performance evaluation, subjective calls should be removed from the evaluation process, or subjective scores should be deducted from the overall results.
    URI
    http://hdl.handle.net/10454/19294
    Version
    Published version
    Citation
    Ahmed AM, Sivarajah U, Irani Z, Mahroof K and Vincent C (2022) Data-driven subjective performance evaluation: An attentive deep neural networks model based on a call centre case. Annals of Operations Research. Accepted for publication.
    Link to publisher’s version
    https://doi.org/10.1007/s10479-022-04874-2
    Type
    Article
    Collections
    Management and Law Publications

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