Data Mining Approach for Analyzing Call Center Performance

TL;DR

This paper applies various data mining techniques to predict call center service quality, compares their performance, and analyzes input sensitivity to enhance understanding and improve call center operations.

Contribution

It introduces a comprehensive comparison of multiple data mining models for call center performance prediction and analyzes input sensitivity to inform performance improvement strategies.

Findings

Support vector machines performed best among models.

Input sensitivity analysis revealed key performance indicators.

Hybrid models showed potential for improved accuracy.

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.