On Predicting a Call Center’s Workload: A Discretization-Based Approach

  • Luis Moreira-Matias
  • Rafael Nunes
  • Michel Ferreira
  • João Mendes-Moreira
  • João Gama
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)

Abstract

Agent scheduling in call centers is a major management problem as the optimal ratio between service quality and costs is hardly achieved. In the literature, regression and time series analysis methods have been used to address this problem by predicting the future arrival counts. In this paper, we propose to discretize these target variables into finite intervals. By reducing its domain length, the goal is to accurately mine the demand peaks as these are the main cause for abandoned calls. This was done by employing multi-class classification. This approach was tested on a real-world dataset acquired through a taxi dispatching call center. The results demonstrate that this framework can accurately reduce the number of abandoned calls, while maintaining a reasonable staff-based cost.

Keywords

call centers arrival forecasting agent scheduling discretization multi-class classification 

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References

  1. 1.
    Avramidis, A.N., Deslauriers, A., L’Ecuyer, P.: Modeling daily arrivals to a telephone call center. Management Science 50(7), 896–908 (2004)CrossRefMATHGoogle Scholar
  2. 2.
    Taylor, J.W., Snyder, R.D.: Forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal exponential smoothing. Omega 40(6), 748–757 (2012)CrossRefGoogle Scholar
  3. 3.
    Weinberg, J., Brown, L.D., Stroud, J.R.: Bayesian forecasting of an inhomogeneous poisson process with applications to call center data. Journal of the American Statistical Association 102(480), 1185–1198 (2007)CrossRefMATHMathSciNetGoogle Scholar
  4. 4.
    Millán-Ruiz, D., Hidalgo, J.I.: Forecasting call centre arrivals. Journal of Forecasting 32(7), 628–638 (2013)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L.: Predicting taxi-passenger demand using streaming data. IEEE Transactions on Intelligent Transportation Systems 14(3), 1393–1402 (2013)CrossRefGoogle Scholar
  6. 6.
    Shen, H., Huang, J.Z.: Forecasting time series of inhomogeneous poisson processes with application to call center workforce management. The Annals of Applied Statistics, 601–623 (2008)Google Scholar
  7. 7.
    Aldor-Noiman, S., Feigin, P.D., Mandelbaum, A.: Workload forecasting for a call center: Methodology and a case study. The Annals of Applied Statistics, 1403–1447 (2009)Google Scholar
  8. 8.
    Cappé, O., Godsill, S., Moulines, E.: An overview of existing methods and recent advances in sequential monte carlo. Proceedings of the IEEE 95(5), 899–924 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Luis Moreira-Matias
    • 1
    • 2
    • 3
  • Rafael Nunes
    • 3
  • Michel Ferreira
    • 1
    • 5
  • João Mendes-Moreira
    • 2
    • 3
  • João Gama
    • 2
    • 4
  1. 1.Instituto de TelecomunicaçõesPortoPortugal
  2. 2.LIAAD-INESC TECPortoPortugal
  3. 3.FEUPU. PortoPortoPortugal
  4. 4.Faculdade de EconomiaU. PortoPortoPortugal
  5. 5.DCC-FCUPU. PortoPortoPortugal

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