Investigating the Use of Machine Learning and Deep Learning Models for Churn Rate Estimation

It can be concluded that the increasing availability of large customer datasets and the need for accurate predictive analytics have made Machine Learning and Deep Learning essential tools for customer churn prediction. These models provide higher prediction accuracy, the ability to analyze complex behavioral patterns, and support for real-time decision-making. As a result, organizations can effectively identify potential churners and implement targeted retention strategies, ultimately improving profitability and long-term customer relationships.  ML and DL models is their ability to support real-time analytics. Businesses today require fast and automated decision-making systems that can analyze customer activity continuously. Machine learning systems can be integrated with business platforms to monitor customer behavior in real time and generate churn risk scores. This allows companies to take proactive measures before customers actually leave the service. Moreover, ML and DL models improve business decision-making by providing data-driven insights. These models not only predict churn but also identify the key factors contributing to it, such as poor service quality, high pricing, or lack of engagement.  

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