We propose an automated triage design for intelligent customer routing in live‐chat contact centers and demonstrate its implementation using a real‐world data set from an S&P 500 firm. The proposed design emerges as a synthesis of text analytics and predictive machine learning methods. Using numerical experiments based on the simulation of the firm's contact center, we demonstrate the service level, time, and labor cost benefits of the automated design over two other triage designs (i.e., customer choice triage and human expert triage) that are commonly employed in the real world. Through additional analyses, we explore the generalizability of the automated design for creating solutions for different types of communication channels. Our work has implications for managing customer relations under emerging communication technologies (e.g., live‐chat, e‐mail, and social media) and more broadly for demonstrating the use of text analytics and machine learning to improve Operations Management practice.

中文翻译：

---

改善联络中心的客户路由：基于文本分析的自动分类设计

我们提出了一种用于实时聊天联络中心中智能客户路由的自动分类设计，并使用标准普尔500公司的真实数据集来演示其实现。提出的设计是文本分析和预测性机器学习方法的综合体现。使用基于公司联络中心模拟的数值实验，我们证明了自动化设计相对于其他两种通常采用的分类设计（即客户选择分类和人工专家分类）的服务水平，时间和人工成本收益。现实世界。通过其他分析，我们探索了自动设计的通用性，以为不同类型的通信渠道创建解决方案。我们的工作对在新兴通信技术（例如，