Customer service has evolved beyond in-person visits and phone calls to include live chat, AI chatbots and social media, among other contact options. Service providers typically refer to these contact modalities as "channels". Within each channel, customer service agents are tasked with managing and resolving a stream of inbound service requests. Each request involves milestones where the agent must decide whether to keep assisting the customer or to transfer them to a more skilled -- and often costlier -- provider. To understand how this request resolution process should be managed, we develop a model in which each channel is represented as a gatekeeper system and characterize the structure of the optimal request resolution policy. We then turn to the broader question of the firm's customer service design, which includes the strategic problem of which channels to deploy, the tactical questions of at what level to staff the live-agent channel and to what extent to train an AI chatbot, and the operational question of how to control the live-agent channel. Examining the interplay between strategic, tactical, and operational decisions through numerical methods, we show, among other insights, that service quality can be improved, rather than diminished, by chatbot implementation.

翻译：客户服务已从传统的面对面访问和电话沟通，演变为涵盖在线聊天、AI聊天机器人及社交媒体等多种联系渠道。服务提供商通常将这些联系方式称为“渠道”。在每个渠道中，客服人员负责处理并解决持续流入的服务请求。每个请求均包含若干关键节点，客服人员需在此决定是继续协助客户，还是将其转接至技能更高（通常成本也更高）的服务提供方。为理解应如何管理此请求解决流程，我们构建了一个模型，将每个渠道表征为一个守门员系统，并刻画了最优请求解决策略的结构特征。随后，我们转向企业客户服务设计的更广泛议题，包括应部署哪些渠道的战略问题、人工客服渠道人员配置水平与AI聊天机器人训练程度的战术问题，以及如何控制人工客服渠道的运营问题。通过数值方法研究战略、战术与运营决策间的相互作用，我们发现（除其他见解外）聊天机器人的实施反而能够提升而非降低服务质量。