In the quest to improve services, companies offer customers the opportunity to interact with agents through contact centers, where the communication is mainly text-based. This has become one of the favorite channels of communication with companies in recent years. However, contact centers face operational challenges, since the measurement of common proxies for customer experience, such as knowledge of whether customers have abandoned the queue and their willingness to wait for service (patience), are subject to information uncertainty. We focus this research on the impact of a main source of such uncertainty: silent abandonment by customers. These customers leave the system while waiting for a reply to their inquiry, but give no indication of doing so, such as closing the mobile app of the interaction. As a result, the system is unaware that they have left and waste agent time and capacity until this fact is realized. In this paper, we show that 30%-67% of the abandoning customers abandon the system silently, and that such customer behavior reduces system efficiency by 5%-15%. To do so, we develop methodologies to identify silent-abandonment customers in two types of contact centers: chat and messaging systems. We first use text analysis and an SVM model to estimate the actual abandonment level. We then use a parametric estimator and develop an expectation-maximization algorithm to estimate customer patience accurately, as customer patience is an important parameter for fitting queueing models to the data. We show how accounting for silent abandonment in a queueing model improves dramatically the estimation accuracy of key measures of performance. Finally, we suggest strategies to operationally cope with the phenomenon of silent abandonment.

翻译：在提升服务质量的探索中，企业为客户提供通过联络中心与客服人员互动的机会，其中沟通主要基于文本。近年来，这已成为客户与企业交流的首选渠道之一。然而，联络中心面临运营挑战，因为衡量客户体验的常见代理指标（例如客户是否已放弃排队以及其等待服务的意愿（耐心））存在信息不确定性。本研究聚焦于此不确定性的一个主要来源：客户的静默放弃。这些客户在等待回复其咨询时离开系统，但未给出任何离开迹象，例如未关闭交互的移动应用程序。因此，系统不知晓其已离开，并在此事实被察觉前浪费客服人员的时间与产能。本文显示，30%-67%的放弃客户会静默离开系统，且此类客户行为使系统效率降低5%-15%。为此，我们开发了两种识别静默放弃客户的方法，分别适用于聊天系统和消息系统两类联络中心。我们首先利用文本分析和SVM模型估计实际放弃水平，随后使用参数估计器并开发期望最大化算法以准确估计客户耐心，因为客户耐心是拟合排队模型数据的重要参数。我们展示了在排队模型中考虑静默放弃如何显著提升关键性能指标的估计精度。最后，我们提出从运营层面应对静默放弃现象的策略。