Improving Efficiency: Optimizing the Average Processing Time of Crm Systems Based on Machine Learning

Abstract

This paper delves into the innovative application of machine learning (ML) technologies to reduce Average Handle Time (AHT) in Customer Relationship Management (CRM) systems, a critical metric for assessing customer service efficiency and effectiveness. Through an in-depth analysis, we examine how ML algorithms can automate routine tasks, provide predictive insights, and enable personalized customer interactions, thereby streamlining operations and enhancing customer satisfaction. The integration of ML within CRM systems poses unique challenges, including technical integration complexities, data privacy concerns, and the need for continuous adaptation and training. By exploring practical implementation strategies, this study highlights the transformative potential of ML in redefining customer service paradigms. Furthermore, we address the ethical considerations and change management approaches essential for successful ML adoption. Our findings suggest that leveraging ML not only reduces AHT but also significantly improves the overall customer service experience by allowing agents to focus more on complex, value-added interactions. The paper concludes with a forward-looking perspective on the future of ML in CRM, emphasizing continuous improvement, ethical data use, and the cultivation of a culture of innovation. This study contributes to the growing body of knowledge on the intersection of ML and CRM, offering valuable insights for organizations seeking to enhance their customer service operations through technological advancements.