Metodología para incrementar la precisión en la estimación del volumen de llamadas en los centros de contacto del sector de telecomunicaciones a través de modelos de series de tiempos en el proceso de Workforce Management

Abstract

An erroneous inbound call forecast can lead to excess or shortage of agents, which translates into business losses or customer dissatisfaction. Therefore, we seek to design and validate an optimal statistical forecasting model that adjusts to the real traffic requirements and that handles the theoretical-practical assumptions of time series that allows the optimization of resources in terms of human talent. The Box-Jenkins statistical methodology based on time series was used to create a forecast model which was compared with the traditional model currently used in the Workforce process. It was possible to build a statistical methodology that adds value to the process and its ability to characterize the behavior of incoming calls is statistically supported, which leads to a better control in the estimation of resources, which will increase efficiency and reliability in the staffing process.