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Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals’ forecasting

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  • Albrecht, Tobias
  • Rausch, Theresa Maria
  • Derra, Nicholas Daniel

Abstract

Machine learning (ML) techniques within the artificial intelligence (AI) paradigm are radically transforming organizational decision-making and businesses’ interactions with external stakeholders. However, in time series forecasting for call center management, there is a substantial gap between the potential and actual use of AI-driven methods. This study investigates the capabilities of ML models for intra-daily call center arrivals’ forecasting with respect to prediction accuracy and practicability. We analyze two datasets of an online retailer’s customer support and complaints queue comprising half-hourly observations over 174.5 weeks. We compare practically relevant ML approaches and the most commonly used time series models via cross-validation with an expanding rolling window. Our findings indicate that the random forest (RF) algorithm yields the best prediction performances. Based on these results, a methodological walk-through example of a comprehensive model selection process based on cross-validation with an expanding rolling window is provided to encourage implementation in individual practical settings.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jbrese:v:123:y:2021:i:c:p:267-278
    DOI: 10.1016/j.jbusres.2020.09.033
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    6. Sagarika Mishra & Michael T. Ewing & Holly B. Cooper, 2022. "Artificial intelligence focus and firm performance," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1176-1197, November.

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