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An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach

Author

Listed:
  • Latifah Almuqren

    (Information Systems Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia)

  • Fatma S. Alrayes

    (Information Systems Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia)

  • Alexandra I. Cristea

    (Information Systems Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia
    Computer Science Department, University of Durham, Durham DH13LE, UK)

Abstract

With the rising growth of the telecommunication industry, the customer churn problem has grown in significance as well. One of the most critical challenges in the data and voice telecommunication service industry is retaining customers, thus reducing customer churn by increasing customer satisfaction. Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. The related research reveals that many studies have focused on developing churner prediction models based on historical data. These models face delay issues and lack timelines for targeting customers in real-time. In addition, these models lack the ability to tap into Arabic language social media for real-time analysis. As a result, the design of a customer churn model based on real-time analytics is needed. Therefore, this study offers a new approach to using social media mining to predict customer churn in the telecommunication field. This represents the first work using Arabic Twitter mining to predict churn in Saudi Telecom companies. The newly proposed method proved its efficiency based on various standard metrics and based on a comparison with the ground-truth actual outcomes provided by a telecom company.

Suggested Citation

  • Latifah Almuqren & Fatma S. Alrayes & Alexandra I. Cristea, 2021. "An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach," Future Internet, MDPI, vol. 13(7), pages 1-19, July.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:7:p:175-:d:588849
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    References listed on IDEAS

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    3. Glady, Nicolas & Baesens, Bart & Croux, Christophe, 2009. "Modeling churn using customer lifetime value," European Journal of Operational Research, Elsevier, vol. 197(1), pages 402-411, August.
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