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International Conference on Computational Science and Its Applications

ICCSA 2020: Computational Science and Its Applications – ICCSA 2020 pp 471–486Cite as

Towards Explainable Direct Marketing in the Telecom Industry Through Hybrid Machine Learning

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12254))

Abstract

Direct marketing enables businesses to identify customers that could be interested in product offerings based on historical customer transactions data. Several machine learning (ML) tools are currently being used for direct marketing. However, the disadvantage of ML algorithmic models is that even though results could be accurate, they lack relevant explanations. The lack of detailed explanations that justify recommendations has led to reduced trust in ML-based recommendations for decision making in some critical real-world domains. The telecommunication domain has continued to witness a decline of revenue in core areas such as voice and text messaging services which make direct marketing useful to increase profit. This paper presents the conceptual design of a machine learning process framework that will enable telecom subscribers that should be targeted for direct marketing of new products to be identified, and also provide explanations for the recommendations. To do this, a hybrid framework that employs supervised learning, case-based reasoning and rule-based reasoning is proposed. The operational workflow of the framework is demonstrated with an example, while the plan of implementation and evaluation are also discussed.

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Authors and Affiliations

  1. Department of Information Technology, Cape Peninsula University of Technology, Cape Town, South Africa

    Russel Petersen & Olawande Daramola

Authors
  1. Russel Petersen
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  2. Olawande Daramola
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Corresponding author

Correspondence to Olawande Daramola .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Petersen, R., Daramola, O. (2020). Towards Explainable Direct Marketing in the Telecom Industry Through Hybrid Machine Learning. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12254. Springer, Cham. https://doi.org/10.1007/978-3-030-58817-5_35

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  • DOI: https://doi.org/10.1007/978-3-030-58817-5_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58816-8

  • Online ISBN: 978-3-030-58817-5

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