Silent Abandonment in Contact Centers: Estimating Customer Patience from Uncertain Data
Silent Abandonment in Contact Centers: Estimating Customer Patience from Uncertain Data
复制标题
联络中心的无声放弃：根据不确定的数据评估客户的耐心
DOI：
10.48550/arxiv.2304.11754
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Y. Goldberg
中科院分区：
文献类型：
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作者：
Antonio Castellanos;G. Yom;Y. Goldberg
在寻求改善服务的过程中，企业通过联络中心为客户提供与客服人员互动的机会，这种互动主要基于文本。近年来，这已成为与企业沟通的热门渠道之一。然而，联络中心面临运营方面的挑战，因为衡量客户体验的常用指标，比如了解客户是否放弃排队以及他们等待服务的意愿（耐心程度），都受到信息不确定性的影响。我们将这项研究聚焦于这种不确定性的一个主要来源的影响：客户的无声放弃。这些客户在等待对其咨询的回复时离开系统，但没有给出这样做的任何迹象，比如关闭互动的移动应用程序。结果，系统没有察觉到他们已经离开，浪费了客服人员的时间和能力，直到意识到这一事实。在本文中，我们表明30% - 67%的放弃客户是无声地放弃系统的，并且这种客户行为使系统效率降低了5% - 15%。为此，我们开发了一些方法来识别两种联络中心（聊天和消息系统）中的无声放弃客户。我们首先使用文本分析和支持向量机模型来估计实际的放弃水平。然后我们使用一个参数估计器并开发一种期望最大化算法来准确估计客户的耐心程度，因为客户耐心程度是使排队模型与数据拟合的一个重要参数。我们展示了在排队模型中考虑无声放弃如何极大地提高关键绩效指标的估计准确性。最后，我们提出了在运营上应对无声放弃现象的策略。
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.