Big Data Analytics in CRM: A Conceptual Model for Customer Segmentation and Lifetime Value Prediction

Abstract

The integration of big data analytics into Customer Relationship Management (CRM) systems has emerged as a transformative approach for enhancing customer segmentation and predicting Customer Lifetime Value (CLV). This study presents a comprehensive conceptual model that leverages advanced big data analytics techniques to optimize CRM strategies. The model incorporates machine learning algorithms, clustering methods, and predictive analytics to facilitate more accurate customer segmentation and CLV estimation. By synthesizing existing literature, this research identifies critical gaps in traditional CRM approaches and proposes a data-driven framework that addresses scalability, real-time processing, and personalization challenges. The study highlights the theoretical underpinnings of big data analytics in CRM, emphasizing its role in improving marketing efficiency, customer retention, and profitability. Furthermore, the research discusses practical implications for businesses adopting these advanced analytics techniques, along with potential limitations and future research directions. The findings underscore the importance of integrating artificial intelligence and real-time analytics into CRM systems to achieve dynamic customer behavior modeling and enhanced decision-making capabilities.