Data Mining Approach for Analyzing Call Center Performance
Abstract
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.
Cite
@article{arxiv.cs/0405017,
title = {Data Mining Approach for Analyzing Call Center Performance},
author = {Marcin Paprzycki and Ajith Abraham and Ruiyuan Guo},
journal= {arXiv preprint arXiv:cs/0405017},
year = {2007}
}