Call center performance evaluation using big data analytics

Quality monitoring for the call centers can be described as the process of listening to the recorded calls in order to measure the performance of a customer service representative or agent. The main challenge of quality monitoring is that managers have no time to listen all the records and therefore only a few of the stored calls are randomly selected. This results in inaccurate performance measurements, since most of call records can not be listened. This paper presents a distributed call monitoring system for assessing all recorded calls using several quality criteria. In the proposed system, we analyze large amount of call records using popular Hadoop MapReduce framework and utilize text similarity algorithms such as Cosine and n-gram. We also integrated slang word lists to our monitoring system. Empirical call records are used to demonstrate the performance of proposed call monitoring system.