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DOI: 10.14569/IJACSA.2020.0110567
PDF

Customer Churn Prediction Model and Identifying Features to Increase Customer Retention based on User Generated Content

Author 1: Essam Abou el Kassem
Author 2: Shereen Ali Hussein
Author 3: Alaa Mostafa Abdelrahman
Author 4: Fahad Kamal Alsheref

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 5, 2020.

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Abstract: Customer churn is a problem for most companies because it affects the revenues of the company when a customer switch from a service provider company to another in the telecom sector. For solving this problem we put two main approaches: the first one is identifying the main factors that affect customers churn, the second one is detecting the customers that have a high probability to churn through analyzing social media. For the first approach we build a dataset through practical questionnaires and analyzing them by using machine learning algorithms like Deep Learning, Logistic Regression, and Naïve Bayes algorithms. The second approach is customer churn prediction model through analyzing their opinions through their user-generated content (UGC) like comments, posts, messages, and products or services' reviews. For analyzing the UGC we used Sentiment analysis for finding the text polarity (negative/positive). The results show that the used algorithms had the same accuracy but differ in arrangement of attributes according to their weights in the decision.

Keywords: Customer churn; telecom sector; churn prediction; sentiment analysis; machine learning; customer retention

Essam Abou el Kassem, Shereen Ali Hussein, Alaa Mostafa Abdelrahman and Fahad Kamal Alsheref, “Customer Churn Prediction Model and Identifying Features to Increase Customer Retention based on User Generated Content” International Journal of Advanced Computer Science and Applications(IJACSA), 11(5), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110567

@article{Kassem2020,
title = {Customer Churn Prediction Model and Identifying Features to Increase Customer Retention based on User Generated Content},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110567},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110567},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {5},
author = {Essam Abou el Kassem and Shereen Ali Hussein and Alaa Mostafa Abdelrahman and Fahad Kamal Alsheref}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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