Forecasting and Controlling Key Performance Indicators in Call Centers

Abstract

This paper proposes a methodology for modeling and controlling the performance of call centers. Most call centers use CRM (Customer Relationship Management) systems to record data of all contacts between agents and clients. These data may be autocorrelated. To model autocorrelated processes effectively, the proposed methodology integrates in a logical way ARIMA (Autoregressive Integrated Moving Average) modeling and SPC (Statistical Process Control) tools. ARIMA is used to model the process and identify the model that best fits the time series. The fitted model is used to compute residuals, predict future values for the quality variable(s) being monitored and determine the prediction errors. To achieve these goals, the Box-Jenkins methodology is employed. These outputs are then used to apply SPC, in this case the Shewhart control charts for autocorrelated data. First, the computed residuals are used to build the control charts in Phase I of SPC, verify the process stability and estimate the process parameters. Then, these parameters are used to establish the control limits of the charts used in Phase II of SPC to monitor and control the prediction errors. The proposed methodology is tested in a case study of a large call center in Portugal. The results of the case study suggest that ARIMA modeling and SPC, when properly integrated, provide a set of effective tools for monitoring call center performance when autocorrelated data are available. This paper has important implications for both theory and practice.