A Case Study: Optimization of Outbound Call Routing Using Data Mining Techniques

International outbound call management presents significant challenges for multinational corporations due to the need to establish reliable and cost-efficient connections with numerous countries worldwide. The complexity of this task is heightened by the fact that different providers offer varying call prices and quality levels, making optimization and efficient management a daunting task for support teams. Efficient call routing is crucial for economic savings, avoiding poor call quality, and improving the overall Quality of Service (QoS) for users. In this paper, we propose a system that leverages data mining techniques to collect and analyze call logs primarily from the perspectives of costs and quality. The system is designed to develop a Smart Call Routing mechanism that optimizes routing decisions based on real-time data. With the consideration toward Multi-Objective Optimization (MOO) or Pareto optimization, we introduce a set of rules to evaluate and select the optimal routing path, focusing on maximizing cost efficiency while maintaining high call quality. This approach allows companies to optimize their call routing strategies, leading to significant cost savings, improved call quality, and proactive issue management. By minimizing potential business losses, our solution not only enhances operational efficiency but also contributes to a superior user experience.