Data Mining Approach for Analyzing Call Center Performance
<|reference_start|>Data Mining Approach for Analyzing Call Center Performance: 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 perform-ance evaluation process and the actual performance and in this way help improve the performance of call centers. In this paper we summarize our findings.<|reference_end|>
@article{paprzycki2004data,
    title={Data Mining Approach for Analyzing Call Center Performance},
    author={Marcin Paprzycki, Ajith Abraham and Ruiyuan Guo},
    journal={The 17th International Conference on Industrial & Engineering
  Applications of Artificial Intelligence and Expert Systems, Canada, Springer
  Verlag, Germany, 2004 (forth coming)},
    year={2004},
    archivePrefix={arXiv},
    eprint={cs/0405017},
    primaryClass={cs.AI}
}