Abstract
Using Personalized Model to Predict Traffic Jam in Inbound Call Center
Highlights
In today’s world call centers are operated as service centers and means of revenue generation
This paper proposes a personalized call prediction method considering the importance of agent skill information for call center staff scheduling and management
To exhibit the advantages of my method, I used a standard Multivariate Linear Regressions (MLR) as the base prediction function, and evaluate prediction performance by both call volume in terms of the number of calls, and the root mean squared error (RMSE)
Summary
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 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. Two call broker models: (1) personalized agent software broker, and (2) supervisor involved personalized software broker are further developed during the research
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