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Forecasting incoming call volumes in call centers with recurrent Neural Networks

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  • Ebadi Jalal, Mona
  • Hosseini, Monireh
  • Karlsson, Stefan

Abstract

Researchers apply Neural Networks widely in model prediction and data mining because of their remarkable approximation ability. This study uses a prediction model based on the Elman and NARX Neural Network and a back-propagation algorithm for forecasting call volumes in call centers. The results can help determine the optimal number of agents necessary to reduce waiting time for customers, enabling profit maximization and reduction of unnecessary costs. This study also compares the performance of the Elman-NARX Neural Network model with the time-lagged feed-forward Neural Network in addressing the same problem. The experimental results indicate that the proposed method is efficient in forecasting the call volumes of call centers.

Suggested Citation

  • Ebadi Jalal, Mona & Hosseini, Monireh & Karlsson, Stefan, 2016. "Forecasting incoming call volumes in call centers with recurrent Neural Networks," Journal of Business Research, Elsevier, vol. 69(11), pages 4811-4814.
    • Handle: RePEc:eee:jbrese:v:69:y:2016:i:11:p:4811-4814
    DOI: 10.1016/j.jbusres.2016.04.035
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    References listed on IDEAS

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    1. YongSeog Kim & W. Nick Street & Gary J. Russell & Filippo Menczer, 2005. "Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms," Management Science, INFORMS, vol. 51(2), pages 264-276, February.
    2. Huang, Sun-Jen & Chiu, Nan-Hsing & Chen, Li-Wei, 2008. "Integration of the grey relational analysis with genetic algorithm for software effort estimation," European Journal of Operational Research, Elsevier, vol. 188(3), pages 898-909, August.
    3. Landajo, Manuel & de Andres, Javier & Lorca, Pedro, 2007. "Robust neural modeling for the cross-sectional analysis of accounting information," European Journal of Operational Research, Elsevier, vol. 177(2), pages 1232-1252, March.
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    Cited by:

    1. Benedikt Berger & Martin Adam & Alexander Rühr & Alexander Benlian, 2021. "Watch Me Improve—Algorithm Aversion and Demonstrating the Ability to Learn," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 55-68, February.
    2. Notz, Pascal M. & Wolf, Peter K. & Pibernik, Richard, 2023. "Prescriptive analytics for a multi-shift staffing problem," European Journal of Operational Research, Elsevier, vol. 305(2), pages 887-901.
    3. Theresa Maria Rausch & Tobias Albrecht & Daniel Baier, 2022. "Beyond the beaten paths of forecasting call center arrivals: on the use of dynamic harmonic regression with predictor variables," Journal of Business Economics, Springer, vol. 92(4), pages 675-706, May.
    4. Feng, Yi & Yin, Yunqiang & Wang, Dujuan & Dhamotharan, Lalitha, 2022. "A dynamic ensemble selection method for bank telemarketing sales prediction," Journal of Business Research, Elsevier, vol. 139(C), pages 368-382.
    5. Albrecht, Tobias & Rausch, Theresa Maria & Derra, Nicholas Daniel, 2021. "Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals’ forecasting," Journal of Business Research, Elsevier, vol. 123(C), pages 267-278.

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