Modeling and Analysis of Call Center Arrival Data: A Bayesian Approach

Abstract

In this paper we present a modulated Poisson process model to describe and analyze arrival data to a call center. The attractive feature of this model is that it takes into account both covariate and time effects on the call volume intensity and in so doing enables us to assess the effectiveness of different advertising strategies along with predicting the arrival patterns. A Bayesian analysis of the model is developed and an extension of the model is presented to describe potential heterogeneity in arrival patterns.The proposed model and the methodology are implemented using real call center arrival data.

Hazard rate models for early detection of reliability problems using information from warranty databases and upstream supply chain

“…Note however that time dependent covariates are not considered in this manuscript and will be the focus of future work. Literature from other research areas such as marketing and political science (King, 1988;Helsen and Schmittlein, 1993;Soyer and Tarimcilar, 2008) confirm the above limitations of conventional models on certain datasets which share a lot of the same properties as warranty claims, and demonstrate that hazard rate models are able to overcome these limitations and outperform conventional models in terms of estimate stability and predictive accuracy.…”

An Information Stock Model of Customer Behavior in Multichannel Customer Support Services

“…However, most of this work has been done primarily using analytical queuing models (Kleinrock 1975, Gans et al 2003. There is a significant body of work on data-driven statistical models for predicting call center traffic to aid with staffing and workforce management decisions (e.g., Avramidis et al 2004;Bassamboo and Zeevi 2009;Brown et al 2005;Cezik and L'Ecuyer 2008;Mehrotra and Fama 2003;Mehrotra et al 2010;Shen and Huang 2008;Soyer and Tarimcilar 2008;Taylor 2008Taylor , 2012Weinberg et al 2007). However, this literature typically models only telephone call arrivals and often assumes exogenous arrival rates for queries.…”

Modeling customer demand in print service environments using bootstrapping

“…Ibrahim et al (2012) used statistical models to forecast the incoming call volumes to make staffing decisions and build work schedules in telephone call centers. Weinberg, Brown and Stroud (2007), and Soyer and Tarimcilar (2008) use Bayesian techniques in their forecasts with application to call center data. Steinmann and De Freitas Filho (2013) have used simulation to generate data that can be used to evaluate the forecasting algorithms for inbound call center.…”