服务的共创：使用霍克斯过程对联络中心中的服务进行建模
The Co-Production of Service: Modeling Services in Contact Centers Using Hawkes Processes
AI总结：
本文提出双变量标记霍克斯过程聚类模型来刻画联络中心客户与坐席的对话交互，在近500万条消息数据集上验证了其优于经典模型的拟合效果，并展示了该模型在改进客户路由策略和揭示服务异质性影响方面的运营价值。
摘要：
在客户支持联络中心，每次服务交互都涉及客户与坐席之间的消息对话；双方共同交换信息、解决问题，并共同生产服务。由于服务进展由迄今为止的对话历史所塑造，我们提出了一种客户-坐席交互的双变量标记霍克斯过程聚类模型。为评估我们的服务随机模型，我们将其应用于包含近500万条消息的行业联络中心数据集。通过新颖的残差分析比较和多次蒙特卡洛拟合优度检验，我们表明霍克斯聚类模型确实捕捉到了服务核心的动态，并且超越了未纳入服务历史的经典模型。此外，在完全数据驱动的模拟中，我们展示了如何利用这种依赖历史的模型在运营中为基于预测的路由策略提供依据。我们表明，根据霍克斯模型进行简单修改，即可超越广泛使用且经过充分研究的客户路由策略。通过对联络中心文献中提出的理论模型进行分析，我们证明了服务异质性会导致这种性能不佳，而且，如果不谨慎管理服务结束环节，这种异质性就会出现。
英文摘要：
In customer support contact centers, every service interaction involves a messaging dialogue between a customer and an agent; together, they exchange information, solve problems, and collectively co-produce the service. Because the service progression is shaped by the history of conversation so far, we propose a bivariate, marked Hawkes process cluster model of the customer-agent interaction. To evaluate our stochastic model of service, we apply it to an industry contact center dataset containing nearly 5 million messages. Through both a novel residual analysis comparison and several Monte Carlo goodness-of-fit tests, we show that the Hawkes cluster model indeed captures dynamics at the heart of the service and also surpasses classic models that do not incorporate the service history. Furthermore, in an entirely data-driven simulation, we demonstrate how this history-dependent model can be leveraged operationally to inform a prediction-based routing policy. We show that widely-used and well-studied customer routing policies can be outperformed with simple modifications according to the Hawkes model. Through analysis of a stylized model proposed in the contact center literature, we prove that service heterogeneity can cause this underperformance and, moreover, that such heterogeneity will occur if service closures are not carefully managed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
