Using Personalized Model to Predict Traffic Jam in Inbound Call Center

Abstract

In this paper, I describe a general approach to scaling data mining applications in a call center environment. A call center operates with customers calls directed to agents for service based on online call traffic prediction. Existing methods for call prediction exclusively implement inductive machine learning, which often gives inaccurate prediction for call center during abnormal traffic jam. This paper proposes an agent personalized call prediction method that encodes agent skill information as the prior knowledge to call prediction and distribution. The developed call broker system is tested on handling a telecom call center traffic jam happened in 2008. The results show that the proposed method predicts the occurrence of traffic jam earlier than existing depersonalized call prediction methods. The empirical results of cost-return calculation indicate that the ROI (return on investment) is enormously positive for any call center to implement such an agent personalized call broker system as a scalable solution. This paper focussed primarily on issues related to the accuracy of call predictions during abnormal events happen in a call center environment.