Rough set analysis on call center metrics

Abstract

Managers in call centers use metrics to measure organizational performance; unfortunately, these metrics only reveal how well service agents process calls. To achieve substantial quality improvement, managers need to obtain in-depth information from operational metrics such as first call resolution (FCR). This research incorporated rough set theory (RST) in analyzing FCR to produce decision rules. RST has been known to reduce the number of attributes and attribute values without affecting the original results. Data from a tariff (industry segment) call center in Taiwan were used in this study, and a four-step process was used to produce the final decision rules. The results were verified by a 10-fold cross-validation process against the decision rules produced without applying RST. The decision rules produced with RST are as effective as those produced without, but with reduced number of attributes and attribute values. This reduced decision rule set can help managers analyze the current operator procedures more efficiently and subsequently improve the call center efficiency. Without affecting effectiveness, the RST application reduces the needed number of attributes and attribute values to produce a more compact decision rule set, and this increased efficiency, in turn, allows managers to delve deeper into the operational factors.