Optimizing Call Center Agent Efficiency through Deep learning based Classifications using SMFCCAE

Abstract

Call centers are integral to business organizations worldwide, facilitating customer inquiries, handling complaints, and conducting telephonic sales. These centers play a critical role in customer service, managing the interface between major businesses and their clients. Most organizations leverage call centers to streamline operations and route customer inquiries to the appropriate channels. However, maintaining quality in the call center industry is a complex challenge, heavily reliant on the performance of Call Center Representatives (CSRs) who handle large volumes of calls. Traditional qualitative assessments are often limited, relying on manual evaluation of small call samples. With the advent of Deep Learning Techniques (DLTs), it has become possible to more accurately assess the performance of call center agents. This study introduces a novel approach named SMFCCE (Selecting Minimal Features for Call Center Agents Efficiency), which optimizes the feature selection process from CSR data, leading to faster and more accurate classification—achieving approximately 85% accuracy. Additionally, this study offers insights and recommendations for enhancing the overall effectiveness of call center operations