Abstract
In this article, we present a novel framework for measuring productivity of customer service representative (CSR) in real estate call centers. The framework proposes a binary classification task for measuring CSR productivity. Generative and discriminative classifiers are compared in this study. The generative classifier is Naive Bayes (NB) versus the discriminative classifiers which are: logistic regression (LR) and linear support vector machine (LSVM). To train the classifiers, a speech corpus (7 h) is collected and annotated from three different call centers located in Egypt. The accuracy results on this corpus show that LSVM can lead to the best results (82%) and machine learning methods may successfully replace subjective evaluation methods commonly used in that domain.
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Ahmed, A., Hifny, Y., Toral, S., Shaalan, K. (2018). A Call Center Agent Productivity Modeling Using Discriminative Approaches. In: Shaalan, K., Hassanien, A., Tolba, F. (eds) Intelligent Natural Language Processing: Trends and Applications. Studies in Computational Intelligence, vol 740. Springer, Cham. https://doi.org/10.1007/978-3-319-67056-0_24
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DOI: https://doi.org/10.1007/978-3-319-67056-0_24
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