| Abstract |
In the modern business ecosystem, call centers are a pivotal touchpoint between companies and their clients, having huge influence over customer satisfaction and operational efficacy.
However, conventional approaches to call center management often lack the depth and precision required to extract actionable insights from customer interactions.
This capstone project takes on this challenge head-on by introducing a comprehensive framework designed to improve call center operations inside businesses. Using audio call records as rich stores of consumer insights and agent performance metrics is a key component of our approach. Through utilizing modern AI technologies like graph visualization and sentiment analysis, our solution provides stakeholders with valuable insights into consumer interactions with agents.
Our solution's key component is its user-friendly interface, which makes it easy to access a wide range of capabilities that have been carefully designed to maximize call center performance.
Our technology gives users the ability to identify errors in agent performance and discover recurrent issues, which in turn helps them improve customer satisfaction, increase worker productivity, and optimize operational procedures.
In summary, this project is an attempt to bridge the gap between data and actionable intelligence to transform call center management. Our goal is to significantly improve customer experiences, agent skills, and overall organizational efficiency in call center environments by utilizing AI-driven analytics.
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