سامانه همایش های دانشگاه آزاد اسلامی واحد نجف آباد

صفحه اصلی / International Conference on Artificial Intelligence; City, Industry and Health

Customer Churn Prediction in the Telecommunications Industry Using Machine Learning Techniques

نویسندگان :

Atefeh Ahmadi Jazi¹ Nasim Noorafza²

1- Department of Computer Engineering, Na.C, Islamic Azad University, Najafabad, Iran 2- Department of Computer Engineering, Na.C, Islamic Azad University, Najafabad, Iran

کلمات کلیدی :

Customer churn prediction،Machine learning،Data mining،Hybrid models،Deep networks،Data balancing

چکیده :

Customer churn is a key challenge in the telecommunications industry, directly affecting the profitability and growth of organizations. The significance of customer churn prediction can be examined from various aspects, including high customer acquisition costs, implementing effective customer retention strategies, continuously improving services and products, and enhancing financial efficiency and competitiveness. Accurate and timely prediction of this phenomenon enables companies to take proactive measures and maintain customer satisfaction. This review paper examines algorithms and machine learning techniques used in predicting customer churn. Findings from past studies indicate that hybrid methods and machine learning play a significant role in improving prediction accuracy. Additionally, the existing challenges and opportunities are analyzed, and future directions in this domain are proposed.

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