REAL-TIME CUSTOMER CHURN PREDICTION AND RETENTION ANALYTICS USING STREAMING DATA

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

In today's highly competitive business environment, retaining existing customers has become more critical than acquiring new ones. Traditional churn prediction systems rely on batch processing of historical data, which often fails to capture rapidly changing customer behavior and provides delayed insights. This paper proposes an intelligent framework for real-time customer churn prediction and retention analytics designed to process continuous streaming data. By integrating real-time feature engineering with adaptive machine learning models, the system identifies early warning signals of dissatisfaction as they emerge. The proposed framework goes beyond mere prediction by incorporating a retention analytics engine that generates personalized intervention strategies, such as targeted offers and proactive communication. Experimental results, implemented via a streaming-based Python architecture, demonstrate the system's ability to maintain high prediction accuracy while adapting to concept drift in dynamic environments. Ultimately, this research provides a roadmap for organizations to transition from reactive churn management to proactive, data-driven customer engagement

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Keywords

Customer Churn, Real-Time Analytics, Streaming Data, Retention Analytics, Machine Learning, Concept Drift, Proactive Intervention.