Performance Evaluation of Human Agents in Call Center Using Data Mining Approach

Document Type: Research Paper

Authors

Abstract

Many problems concerning human resource management are restricted by service level constraints. High service levels often imply factors such as low service time. Thus, one of the most important challenges in call centers is performance evaluation of human agents to determine service and skill levels. In this study, some features of the service time have been exacted to evaluate and to determine skill level of human agents. Input data to determine service and skill level and to group of human agents are features extracted from operational data of human agent. Using these features can help more effectively to evaluate and to prioritize agents and are not difficult to evaluate, to prioritize, and to determine service and skill level than other evaluating metrics. First, Human agents’ performance has been evaluated by principal components analysis (PCA). Then, Agents and their performance features have been evaluated and visualized. K-means algorithm has been applied to prioritize agents by which three clusters of agents have been detected. The performance of the human agents is evaluated using data obtained from a contact center of an electronic system designer and manufacturer. The results show that using data mining techniques facilitates agents' performance evaluation.

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