AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching
This work addresses the need for efficient agent coaching in contact centers, though it is incremental as it applies existing NLP methods to a specific domain.
The paper tackled the problem of identifying coachable calls in contact centers by using pre-trained transformer models to analyze call transcripts based on quality assurance questions, resulting in an effective system for recommending calls that improved agent coaching and performance.
In recent years, the utilization of Artificial Intelligence (AI) in the contact center industry is on the rise. One area where AI can have a significant impact is in the coaching of contact center agents. By analyzing call transcripts using Natural Language Processing (NLP) techniques, it would be possible to quickly determine which calls are most relevant for coaching purposes. In this paper, we present AI Coach Assist, which leverages the pre-trained transformer-based language models to determine whether a given call is coachable or not based on the quality assurance (QA) questions asked by the contact center managers or supervisors. The system was trained and evaluated on a large dataset collected from real-world contact centers and provides an effective way to recommend calls to the contact center managers that are more likely to contain coachable moments. Our experimental findings demonstrate the potential of AI Coach Assist to improve the coaching process, resulting in enhancing the performance of contact center agents.