[论文解读] Silent Abandonment in Contact Centers: Estimating Customer Patience from Uncertain Data

Antonio Castellanos, Johan S. H. van Leeuwaarden+1 more|arXiv (Cornell University)|Apr 23, 2023

Advanced Queuing Theory AnalysisBusiness, Management and Accounting被引用 3

一句话总结

本文提出一种新颖方法，通过识别客户在未发出信号情况下的静默放弃行为（silent abandonment），结合文本分析与EM算法，估算客服中心的客户耐心程度。研究发现，30%–67%的放弃行为属于静默放弃，导致系统效率降低5%–15%，并表明在排队模型中考虑静默放弃可显著提升模型准确性。

ABSTRACT

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.

研究动机与目标

• 解决因客户未发出信号即离开而导致的信息不确定性问题，即静默放弃行为对客服中心的影响。
• 在因静默放弃导致数据缺失和右删失（censored data）的情况下，准确估算客户耐心程度。
• 通过将静默放弃纳入性能估计，提升排队模型的准确性。
• 提出可操作的策略，以减轻静默放弃带来的容量损失与效率低下问题。
• 比较不同客服渠道（聊天 vs. 消息）中客户耐心的差异，并分析其行为特征。

提出的方法

• 利用文本分析与支持向量机（SVM）模型，基于消息内容与行为模式，判断客户是否已放弃排队。
• 开发一种参数化估计器，用于估算客户耐心程度，同时考虑已观测到的放弃行为与未观测到的（静默）放弃行为。
• 应用期望最大化（EM）算法，在数据缺失与右删失条件下，估计客户耐心的分布。
• 构建一种改进的排队模型，将静默放弃纳入其中，以更真实地反映系统动态。
• 通过比较包含与不包含静默放弃的模型在拟合精度上的差异，验证模型性能。
• 采用生存分析技术处理放弃时间的删失数据。

实验结果

研究问题

• RQ1 客服中心中客户放弃行为的多大比例属于静默放弃？这一比例在不同服务渠道间是否存在差异？
• RQ2 静默放弃如何影响系统效率？其对座席利用率与容量损失的可测量影响是什么？
• RQ3 忽略静默放弃在多大程度上导致排队模型中客户耐心程度的估计产生偏差？
• RQ4 聊天系统与消息系统中客户耐心水平有何差异？造成这些差异的因素可能有哪些？
• RQ5 可采取哪些运营业务策略，以减轻静默放弃对服务表现与座席工作负荷的负面影响？

主要发现

• 在客服中心中，30%至67%的放弃客户在未发出任何信号的情况下静默离开排队。
• 静默放弃导致系统效率降低5%至15%，主要原因是座席空闲以及对不存在客户的工作浪费。
• EM算法估计得出，在消息系统中客户耐心为81.1分钟，而在聊天系统中仅为2分钟，反映出显著的渠道依赖性差异。
• 考虑静默放弃的排队模型与真实数据的拟合度显著优于忽略静默放弃的模型。
• 静默放弃比例在消息系统中更高（因等待时间更长），表明等待时长与静默放弃之间存在相关性。
• 静默放弃导致队列长度测量不准确，可能误导延迟公告及其他实时服务指标。