Agent personalized call center traffic prediction and call distribution

Authors:
Rafiq A. Mohammed;Paul Pang
Affiliations:
Department of Computing, Unitec Institute of Technology, New Zealand;Department of Computing, Unitec Institute of Technology, New Zealand
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Year:
2011

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Abstract

A call center operates with customers calls directed to agents for service based on online call traffic prediction. Existing methods for call prediction implement exclusively inductive machine learning, which gives often under accurate prediction for call center 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 conducted cost-return calculation indicates that the ROI (return on investment) is enormously positive for any call center to implement such an agent personalized call broker system.