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Intelligent Call Routing for Telecommunications Call-Centers

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Intelligent Data Engineering and Automated Learning – IDEAL 2020 (IDEAL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12489))

Abstract

At telecommunications companies, call-centers have the highest interaction with customers, and the operators’ performance is vital because an excellent service satisfies the customer and helps a better operation. Therefore, attempts are made to use customer data, call operator data, and historical service data to improve support. Pairing a customer with an operator who is comfortable with the problem to solve helps companies reducing costs, improves customer service, and increases employee productivity. In this article, we propose an approach based on machine learning and optimization, which predicts the problem for which the customer is calling and routes the call and the customer to the most appropriate call operator. The results show that using large amounts of business data along with innovative algorithms such as LightGBM can improve the customer support performance.

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Acknowledgements

This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.

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Authors and Affiliations

  1. University of Minho, Campus Gualtar, 4710, Braga, Portugal

    Sérgio Jorge

  2. NOS Comunicações, Senhora da Hora, Portugal

    Carlos Pereira

  3. Algoritmi Center, University of Minho, Campus Gualtar, 4710, Braga, Portugal

    Paulo Novais

Authors
  1. Sérgio Jorge
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  2. Carlos Pereira
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  3. Paulo Novais
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Corresponding author

Correspondence to Sérgio Jorge .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Cesar Analide

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. Technical University of Madrid, Madrid, Spain

    David Camacho

  4. University of Manchester, Manchester, UK

    Hujun Yin

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Jorge, S., Pereira, C., Novais, P. (2020). Intelligent Call Routing for Telecommunications Call-Centers. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_28

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  • DOI: https://doi.org/10.1007/978-3-030-62362-3_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62361-6

  • Online ISBN: 978-3-030-62362-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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