LLM-Based Insight Extraction for Contact Center Analytics and Cost-Efficient Deployment
This work addresses the need for efficient and scalable analytics in contact centers, offering incremental improvements through optimized model selection and cost strategies.
The paper tackles the problem of automating call driver generation for contact center analytics by developing a system that uses Large Language Models to provide actionable insights for agents and administrators, resulting in a cost-efficient deployment with strategies and analysis for production environments.
Large Language Models have transformed the Contact Center industry, manifesting in enhanced self-service tools, streamlined administrative processes, and augmented agent productivity. This paper delineates our system that automates call driver generation, which serves as the foundation for tasks such as topic modeling, incoming call classification, trend detection, and FAQ generation, delivering actionable insights for contact center agents and administrators to consume. We present a cost-efficient LLM system design, with 1) a comprehensive evaluation of proprietary, open-weight, and fine-tuned models and 2) cost-efficient strategies, and 3) the corresponding cost analysis when deployed in production environments.