Emotional Sentiment Detection in CRM Communications for Real-Time Escalation Triggers

Abstract

Customer Relationship Management (CRM) systems are critical in maintaining effective communication and
enhancing customer satisfaction. While existing research has explored sentiment analysis to gauge customer
emotions in CRM communications, most approaches focus on post-interaction analytics rather than enabling
proactive, real-time interventions. The current gap lies in the limited integration of fine-grained emotional
sentiment detection that can trigger immediate escalation in customer support workflows. Prior studies
predominantly utilize generic sentiment classification models, which often fail to capture nuanced emotional
states such as frustration, anger, or urgency that require prompt action. Furthermore, these models rarely
account for the context-specific language used in CRM dialogues, leading to delayed or inadequate responses
that negatively impact customer experience. This research addresses the critical need for advanced, contextaware emotional sentiment detection methods tailored for CRM environments, enabling real-time escalation
triggers to assist support agents in prioritizing high-risk interactions. By leveraging deep learning techniques
combined with domain-specific language models, this study aims to develop a robust system capable of
interpreting subtle emotional cues within multi-channel CRM communications—including emails, chat, and
social media. The anticipated outcome is a framework that enhances customer service responsiveness by
automatically identifying emotionally charged messages and escalating them to specialized teams or supervisors
without delay. This real-time capability promises to improve customer retention, reduce resolution time, and
elevate overall satisfaction, filling an important gap in CRM-driven customer engagement research.