Enhancing Utility Customer Service and Compliance: An AI-Powered Approach to Call Analysis

Abstract

This study presents a framework for analyzing customer service call transcripts using a Large Language Model (LLM) and unsupervised machine learning. We employed BERTopic to identify core topics from summarized transcripts, refined through an iterative process against internal best practices. The LLM then generated detailed call reasons and agent responses, mapped to standardized tags via an embedding model for consistency. This framework, implemented on a scalable GCP architecture with robust security measures, allows for granular root cause analysis and identification of customer sentiment trends. Evaluation of the LLM demonstrated high recall rates for topic detection and accuracy in generating summaries and call reasons. This approach enables proactive identification of customer needs, targeted agent coaching, and compliance risk mitigation, ultimately enhancing customer experience and operational efficiency.

Keywords: Large Language Model (LLM), Speech Analytics, BERTopic, Customer Service, Topic Modeling, Compliance, Sentiment Analysis

DOI: 10.54941/ahfe1006051