Predictive Analytics for Customer Retention: A Data-Driven Framework for Proactive Engagement and Satisfaction Management

Neeraj Kripalani

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 6, 1109-1116

Abstract: This comprehensive article examines the implementation of predictive analytics and data-driven frameworks for enhancing customer retention in modern business environments. The article explores how advanced analytics, machine learning algorithms, and proactive engagement strategies can significantly improve customer satisfaction and reduce churn rates. Through detailed article analysis of usage patterns, engagement metrics, and customer behavior, the article demonstrates the effectiveness of sophisticated intervention strategies in maintaining strong customer relationships. The article investigates the development and implementation of satisfaction score algorithms, real-time monitoring systems, and customized support mechanisms that enable organizations to identify and address potential issues before they lead to customer attrition. Furthermore, it evaluates the impact of integrated feedback systems and sentiment analysis in creating more responsive and effective customer retention strategies. The article provides valuable insights into how organizations can leverage data analytics to create more personalized and proactive customer engagement approaches, ultimately leading to improved customer lifetime value and business sustainability.

Keywords: Predictive Analytics; Customer Retention; Data-Driven Customer Engagement; Customer Satisfaction Management; Machine Learning in Business (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT241061149
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i6:id:504

DOI: 10.32628/CSEIT241061149

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