Leveraging Large Language Models for Evaluating Customer Service Conversations and Retrieval-Augmented Generation for Pre-Call Insights
Abstract
Customer service is one of the critical aspects that determines the success of most businesses. Evaluating the conversational and performance efficiency of customer service representatives (CSRs) plays a huge role. However, conventional methods such as customer feedback and manual call reviews can be very time-consuming and inconsistent. This paper proposes the integration of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to automate the evaluation of conversations that CSRs have with customers. By analyzing metrics such as empathy, tone, clarity, and problem resolution, LLMs objectively assess the CSR’s performance. This would help managers and CSRs with insights into conversational improvements, while RAG equips CSRs with relevant information before customer interactions. The results suggest that this approach not only automates efficiently the whole process of employee evaluation from the business’s perspective but also enhances the quality of customer interactions.
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