Real-Time Conversational Analysis Using LLMs for B2B E-Commerce Customer Value Management

The integration of artificial intelligence into business communications has created new opportunities for real-time analysis of customer interactions. This study presents an integrative framework that combines Large Language Models (LLMs) with Customer Value Management (CVM) to enhance the understanding and management of Business-to-Business (B2B) customer relationships. The proposed framework is designed to process conversational data directly, analyze its linguistic and emotional aspects, and map the results into customer value indicators, such as the Customer Satisfaction Index (CSI), the Customer Lifetime Value (CLV), and the Customer Retention Probability (CRP). The system architecture has four main layers, namely Input, Processing, Integration, and Decision layers, which convert conversational data into actionable business insights. The Random Forest (RF) classifier achieved 91% accuracy in customer segmentation (Loyal, At-Risk, Critical), while the Logistic Regression (LR) model for customer retention prediction had an AUC of 0.83. Therefore, it can be concluded that integrating LLM-based conversational intelligence with CVM metrics can enhance the accuracy of analysis and improve decision-making effectiveness. The findings of this research contribute to the development of AI-based customer value analytics by bridging natural language understanding and quantitative business intelligence. The proposed approach provides a scalable and interpretive solution to improve proactive B2B customer engagement and support adaptive decision-making systems in the data-driven enterprise era.