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

Almuqren, Latifah; Alrayes, Fatma S.; Cristea, Alexandra I.

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.

Citation

Almuqren, L., Alrayes, F. S., & Cristea, A. I. (2021). An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach. Future Internet, 13(7), Article 175. https://doi.org/10.3390/fi13070175

• Journal Article Type: Article
• Acceptance Date: Jun 21, 2021
• Online Publication Date: Jul 5, 2021
• Publication Date: 2021
• Deposit Date: Nov 1, 2021
• Publicly Available Date: Nov 1, 2021
• Journal: Future Internet
• Electronic ISSN: 1999-5903
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 7
• Article Number: 175
• DOI: https://doi.org/10.3390/fi13070175
• Public URL: https://durham-repository.worktribe.com/output/1223936