Predictive Analytics to Increase Roster Robustness in an Inbound Call Center

Abstract

Staff rostering is a crucial task in inbound call centers, as personnel costs usually account for the largest share of operating costs. Uncertainty of capacity, such as the presence of agents is often disregarded during rostering. This paper addresses the problem of uncertainty by using predictive analytics to predict agent absences and thus increase roster robustness. Operational data from four years of a call center serves as a basis for our use case. Predictors include characteristics of the service agents such as attendance history and regular working hours as well as other factors such as the weekday. Of the prediction algorithms tested, decision trees outperform other predictive modeling approaches. Evaluation based on an expected value framework shows that the predictive analytics approach performs best compared to the planned, unchanged roster and a general staff surcharge of 10%.