Advanced Algorithms for Enhancing Customer Churn Prediction Accuracy using Real-Time Data Analytics

Abstract

Customer churn is a critical challenge for businesses, as retaining existing customers is more cost-effective than acquiring new ones. This paper, Advanced Algorithms for Enhancing Customer Churn Prediction Accuracy Using Real-Time Data Analytics, explores the integration of cutting-edge algorithms with real-time data analytics to improve the accuracy and timeliness of churn predictions. By leveraging machine learning techniques such as ensemble models, neural networks, and advanced feature engineering, this approach identifies key behavioral patterns and risk factors for churn. The inclusion of real-time data processing frameworks, such as Apache Kafka and Spark Streaming, allows for continuous monitoring and rapid decision-making, enabling businesses to proactively implement retention strategies. The proposed methodology outperforms traditional static models, demonstrating higher prediction accuracy and reduced latency in decision-making. Additionally, the paper presents a case study in the telecommunications sector, illustrating how the framework reduces customer attrition and enhances revenue retention. The findings highlight the potential of combining advanced algorithms with real-time analytics to transform churn management practices across industries.