  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.
