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

Abdelrahman Ahmed (), Uthayasankar Sivarajah (), Zahir Irani (), Kamran Mahroof () and Vincent Charles ()
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Abdelrahman Ahmed: University of Bradford
Uthayasankar Sivarajah: University of Bradford
Zahir Irani: University of Bradford
Kamran Mahroof: University of Bradford
Vincent Charles: University of Bradford

Annals of Operations Research, 2024, vol. 333, issue 2, No 17, 939-970

Abstract: 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 marked by subjectivity, which in turn results in a distorted picture of agent performance. To address the challenge of identifying and removing subjectivity, empirical research is required. 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 Deep Neural Network (DNN) for a multi-classification problem. The study establishes the first baseline for subjectivity detection, with an accuracy of 75%, which is comparable to relevant speech studies in emotional recognition and performance classification. We conclude, among other things, that in order to achieve the best performance evaluation, subjective calls should be removed from the evaluation process or subjective scores deducted from the overall results.

Keywords: Subjective evaluation; Agent Performance; Customer Behaviour; Deep neural network; Call Centre (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-022-04874-2

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