AI-based Solution for automated call scoring and quality monitoring in call centers

The creation of a complete AI-driven tool, called Judy, to improve the performance of call records and real - time quality control in contact centers is the main target of this diploma thesis. Also, we aim to provide an easy system that enables managers to efficiently listen to call records and select those that need their attention, resulting in enhanced customer support and enhanced agent performance. To achieve this goal, we conducted a literature review covering areas related to AI-driven call analysis, performance management, and call center operations. We then selected and described the Action Design Research (ADR) methodology, which was used to guide the development of our solution. To understand the specific needs and challenges faced by call center managers and agents, we analysed the call center environment and identified key aspects that would inform the design of Judy. The results of this research demonstrated that Judy significantly improved the process of reviewing call recordings, helped managers better assess agent performance quality, and contributed to enhanced customer experiences and optimized agent performance. The iterative BIE cycles within the ADR methodology allowed for the continuous refinement of the solution based on feedback and evaluation results. The significance of this research lies in its contribution to both the practical application and theoretical understanding of AI-driven call analysis and performance management. By developing and implementing Judy, this study offers valuable insights into the potential of AI to address the challenges faced by call center managers and agents and advances the body of knowledge in the field of AI-driven solutions for call center operations.