基于布朗运动的客服中心机器服务阈值设定研究

Research on Machine Service Threshold Setting of Customer Service Center Based on Brownian Motion

摘要: 随着人工智能技术的快速发展，智能客服系统已成为客服中心提升服务效率、优化顾客体验的关键工具。然而，智能客服的引入也带来了服务深度设定与顾客路由分配等运营决策挑战。本文针对同质顾客场景，构建了一个基于布朗运动的客服中心成本优化模型，旨在通过优化强制顾客使用智能客服的比例和智能客服的服务深度，实现客服中心总成本的最小化。模型综合考虑了顾客放弃成本、服务失败成本、等待成本及人工服务成本，并通过数值模拟与敏感性分析，揭示了智能客服能力对最优决策变量的影响机制。研究结果表明，智能客服能力的提升会促使最优强制比例增加，并改变最优服务深度的设定策略。本文的研究为客服中心在智能客服应用中的运营管理提供了理论依据与实践指导。

Abstract: With the rapid development of artificial intelligence technology, intelligent customer service system has become a key tool for customer service centers to improve service efficiency and optimize customer experience. However, the introduction of intelligent customer service also brings operational decision-making challenges such as service depth setting and customer routing allocation. Aiming at the homogeneous customer scenario, this paper constructs a cost optimization model of customer service center based on Brownian motion, aiming to minimize the total cost of customer service center by optimizing the proportion of forced customers to use intelligent customer service and the service depth of intelligent customer service. The model comprehensively considers customer abandonment cost, service failure cost, waiting cost and labor service cost. Through numerical simulation and sensitivity analysis, the influence mechanism of intelligent customer service capability on optimal decision variables is revealed. The results show that the improvement of intelligent customer service capability will increase the optimal mandatory proportion and change the setting strategy of optimal service depth. The research of this paper provides theoretical basis and practical guidance for the operation and management of customer service center in intelligent customer service application.