Caller-Agent Pairing in Call Centers Using Machine Learning Techniques with Imbalanced Data

Abstract

Call centers as the frontline of companies have high interaction with customers. Therefore, the call center performance is very important in the issue of customer satisfaction. Successful communications between agents and customers, satisfy customers and increase the performance of contact center. Call centers managers try to use historical data to improve the service to their clients. Pairing caller with the best suited agent using historical data, helps companies to reduce their costs and improve customer satisfaction. In this work, we proposed a model which optimize call centers outcome with using machine learning techniques to route the caller to the based-suited agent. The result shows using historical data of call center to find an intelligent pairing of callers and agents can improve the performance.

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