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• W2078295034 abstract "Customer satisfaction is a very important indicator of how successful a contact center is at providing services to the customers. Contact centers typically conduct a manual survey with a randomly selected group of customers to measure customer satisfaction. Manual customer satisfaction surveys, however, provide limited values due to high cost and the time lapse between the service and the survey. In this paper, we demonstrate that it is possible to automatically measure customer satisfaction by analyzing call transcripts enabling companies to measure customer satisfaction for every call in near real-time. We have identified various features from multiple knowledge sources indicating prosodic, linguistic and behavioral aspects of the speakers, and built machine learning models that predict the degree of customer satisfaction with high accuracy. The machine learning algorithms used in this work include Decision Tree, Naive Bayes, Logistic Regression and Support Vector Machines (SVMs). Experiments were conducted for a 5-point satisfaction measurement and a 2-point satisfaction measurement using customer calls to an automotive company. The experimental results show that customer satisfaction can be measured quite accurately both at the end of calls and in the middle of calls. The best performing 5-point satisfaction classification yields an accuracy of 66.09% outperforming the DominantClass baseline by 15.16%. The best performing 2-point classification shows an accuracy of 89.42% and outperforms both the DominantClass baseline and the CSRJudgment baseline by 17.7% and 3.3% respectively. Furthermore, Decision Tree and SVMs achieve higher F-measure than the CSRJudgment baseline in identifying both satisfied customers and dissatisfied customers." @default.
• W2078295034 created "2016-06-24" @default.
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• W2078295034 date "2009-01-01" @default.
• W2078295034 modified "2023-10-04" @default.
• W2078295034 title "Towards real-time measurement of customer satisfaction using automatically generated call transcripts" @default.
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• W2078295034 doi "https://doi.org/10.1145/1645953.1646128" @default.
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