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Predicting Customer Churn in Telecommunication Companies: A Predictive Analytics Case Study Inspired by e&

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Title: Predicting Customer Churn in Telecommunication Companies: A Predictive Analytics Case Study Inspired by e&
Authors: Almheiri, Nadia
Committee Members: Hammou Messatfa
Summary: Customer churn is a significant challenge in the telecommunications industry, particularly in competitive markets such as the United Arab Emirates, where customers can easily switch between providers such as e& and du. Telecommunications companies must move beyond reactive approaches and adopt data-driven strategies that enable early identification of churn risk and support proactive retention. This study develops an interpretable churn prediction framework using logistic regression and decision tree models within IBM SPSS. Following the CRISP-DM methodology, the research identifies key demographic, behavioural, and service-related factors that influence churn, and evaluates model performance using accuracy, precision, recall, and ROC-AUC. The findings confirm that churn is driven primarily by contract type, internet service type, payment method, and value-added service adoption rather than demographic characteristics. The full-profile logistic regression model achieved an accuracy of 94.9% and an AUC of 0.977, demonstrating that interpretable models can deliver strong predictive performance while remaining accessible to business teams. The model outputs translate directly into actionable retention strategies, including targeting month-to-month contract holders, promoting value-added services, and incentivising automatic payment adoption. This study contributes a transparent and practically deployable churn prediction framework tailored to telecommunications environments, with future work recommended to validate the approach using real operational data from UAE providers.
URL: https://repository.rit.edu/theses/12580
Database: OpenDissertations
FullText Text:
  Availability: 0
Header DbId: ddu
DbLabel: OpenDissertations
An: ddu.oai.repository.rit.edu.theses.13718
AccessLevel: 6
PubType: Dissertation/ Thesis
PubTypeId: dissertation
PreciseRelevancyScore: 0
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  Data: Predicting Customer Churn in Telecommunication Companies: A Predictive Analytics Case Study Inspired by e&
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  Data: <searchLink fieldCode="AR" term="%22Almheiri%2C+Nadia%22">Almheiri, Nadia</searchLink>
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  Label: Summary
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  Data: Customer churn is a significant challenge in the telecommunications industry, particularly in competitive markets such as the United Arab Emirates, where customers can easily switch between providers such as e& and du. Telecommunications companies must move beyond reactive approaches and adopt data-driven strategies that enable early identification of churn risk and support proactive retention. This study develops an interpretable churn prediction framework using logistic regression and decision tree models within IBM SPSS. Following the CRISP-DM methodology, the research identifies key demographic, behavioural, and service-related factors that influence churn, and evaluates model performance using accuracy, precision, recall, and ROC-AUC. The findings confirm that churn is driven primarily by contract type, internet service type, payment method, and value-added service adoption rather than demographic characteristics. The full-profile logistic regression model achieved an accuracy of 94.9% and an AUC of 0.977, demonstrating that interpretable models can deliver strong predictive performance while remaining accessible to business teams. The model outputs translate directly into actionable retention strategies, including targeting month-to-month contract holders, promoting value-added services, and incentivising automatic payment adoption. This study contributes a transparent and practically deployable churn prediction framework tailored to telecommunications environments, with future work recommended to validate the approach using real operational data from UAE providers.
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RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Customer Churn
        Type: general
      – SubjectFull: Telecommunications
        Type: general
      – SubjectFull: Logistic Regression
        Type: general
      – SubjectFull: Decision Trees
        Type: general
      – SubjectFull: Predictive Analytics
        Type: general
      – SubjectFull: Customer Retention
        Type: general
      – SubjectFull: UAE Market
        Type: general
    Titles:
      – TitleFull: Predicting Customer Churn in Telecommunication Companies: A Predictive Analytics Case Study Inspired by e&
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Almheiri, Nadia
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Type: published
              Y: 2026
ResultId 1