Research Article 1 Using Personalized Model to Predict Traffic Jam in Inbound Call Center Rafiq A. Mohammed 1 1 Victoria University of Wellington, New Zealand 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. Keywords: data mining, predictions, scalability, personalized call broker, call center traffic jam. Received on 16 August 2016, accepted on 06 September 2016, published on 19 January 2017 Copyright © 2017 Rafiq A. Mohammed, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited. doi: 10.4108/eai.18-1-2017.152101 1. Introduction In today’s world call centers are operated as service centers and means of revenue generation. The key trade-off between customer service quality and efficiency of business operations faced by an operations manager in a call center is also the central tension that a human resource manager needs to manage (Aksin, Armony, & Mehrotra, 2007). By looking at the importance of providing efficiency at service quality, this paper describes forecasting approaches that can be applied to any call center. A case study research (Mohammed, 2008) is conducted on Telecom New Zealand (TNZ) call center data for the years 2007 and 2008 during the period of normal and abnormal (i.e. traffic jam) call distributions. This paper proposes a personalized call prediction method considering the importance of agent skill information for call center staff scheduling and management. Applying the proposed method, two call broker models: (1) personalized agent software broker, and (2) supervisor involved personalized software broker are further developed during the research to construct a call center IT solution for small size companies, and as well for large companies such as Telecom New Zealand. 2. Statement of the problem The existing methods for call predictions implement inductive systems and are based on global models and thus cannot generate consistently good prediction accuracy, especially when traffic jam is confronted and/or if there is an abnormal increase of call volume which in turn makes calls to be abandoned affecting the service levels in the call center. TNZ performs call predictions based on historical call forecasting approach and some estimated techniques implemented using Microsoft Excel spreadsheets. The TNZ management uses the Erlang C model for performing optimized prediction of agents. To overcome the operational service challenges of service quality TNZ uses skilled-based routing to solve the matching of agents to the customer needs. These real-time scheduling techniques and optimization models enable TNZ call center to manage EAI Endorsed Transactions on Scalable Information Systems EAI Endorsed Transactions on Scalable Information Systems 12 2016 - 01 2017 | Volume 4 | Issue 12 | e2 * Corresponding author. Email: RafiqA.Mohammed@gmail.com