Multifocal learning for customer problem analysis
ACM Transactions on Intelligent Systems and Technology (TIST)
Agent personalized call center traffic prediction and call distribution
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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The aim of our research was to apply well-known data mining techniques (such as linear neural networks, multi-layered perceptrons, probabilistic neural networks, classification and regression trees, support vector machines and finally a hybrid decision tree - neural network approach) to the problem of predicting the quality of service in call centers; based on the performance data actually collected in a call center of a large insurance company. Our aim was two-fold. First, to compare the performance of models built using the above-mentioned techniques and, second, to analyze the characteristics of the input sensitivity in order to better understand the relationship between the performance evaluation process and the actual performance and in this way help improve the performance of call centers. In this paper we summarize our findings.