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A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation

Author

Listed:
  • Tianyuan Zhang

    (Centro de Investigação em Ciências da Informação, Tecnologias e Arquitetura (ISTA), Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisbon, Portugal)

  • Sérgio Moro

    (Centro de Investigação em Ciências da Informação, Tecnologias e Arquitetura (ISTA), Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisbon, Portugal)

  • Ricardo F. Ramos

    (Centro de Investigação em Ciências da Informação, Tecnologias e Arquitetura (ISTA), Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisbon, Portugal
    Instituto Politécnico de Coimbra, ESTGOH, Rua General Santos Costa, 3400-124 Oliveira do Hospital, Portugal
    CICEE—Centro de Investigação em Ciências Económicas e Empresariais, Universidade Autónoma de Lisboa, Rua de Santa Marta, Palácio dos Condes do Redondo, 56, 1169-023 Lisboa, Portugal)

Abstract

Numerous valuable clients can be lost to competitors in the telecommunication industry, leading to profit loss. Thus, understanding the reasons for client churn is vital for telecommunication companies. This study aimed to develop a churn prediction model to predict telecom client churn through customer segmentation. Data were collected from three major Chinese telecom companies, and Fisher discriminant equations and logistic regression analysis were used to build a telecom customer churn prediction model. According to the results, it can be concluded that the telecom customer churn model constructed by regression analysis had higher prediction accuracy (93.94%) and better results. This study will help telecom companies efficiently predict the possibility of and take targeted measures to avoid customer churn, thereby increasing their profits.

Suggested Citation

  • Tianyuan Zhang & Sérgio Moro & Ricardo F. Ramos, 2022. "A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation," Future Internet, MDPI, vol. 14(3), pages 1-19, March.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:3:p:94-:d:772256
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    References listed on IDEAS

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    1. Pereira, Francisco & Costa, Joana Martinho & Ramos, Ricardo & Raimundo, António, 2023. "The impact of the COVID-19 pandemic on airlines’ passenger satisfaction," Journal of Air Transport Management, Elsevier, vol. 112(C).

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