标题： 文本中心中的静默弃用：识别、量化及其操作影响的缓解 显示英文标题

标题： Silent Abandonment in Text-Based Contact Centers: Identifying, Quantifying, and Mitigating its Operational Impacts

摘要： 在提高服务的探索中，公司向客户提供通过短信与代理互动的选择。 与传统呼叫中心相比，这样的客服中心面临独特的挑战，因为测量客户体验的代理指标如放弃和耐心度涉及不确定性。 这种不确定性的主要来源是静默放弃，即客户在不通知系统的情况下离开，浪费代理时间并使其状态不明。 静默放弃还掩盖了客户是否得到了服务或离开了。 我们的目标是衡量静默放弃的规模并减轻其影响。 分类模型显示，在17家公司中，3%至70%的客户静默放弃。 在一项研究中，71.3%的放弃客户是静默放弃，导致代理效率降低3.2%，系统容量降低15.3%，每位代理每年产生5457美元的成本。 我们开发了一种期望最大化（EM）算法，在不确定性下估计客户耐心度并识别影响协变量。 我们发现，公司应使用分类模型来估计放弃范围，并使用我们的EM算法来评估耐心度。 我们建议通过预测可能的静默放弃行为或改变服务设计来操作性地减轻静默放弃的影响。 具体而言，我们表明，虽然允许客户在队列中等待时书写会带来数据缺失的挑战，但它也会显著增加耐心度并减少服务时间，从而减少放弃率并降低人员需求。 显示英文摘要

摘要： In the quest to improve services, companies offer customers the option to interact with agents via texting. Such contact centers face unique challenges compared to traditional call centers, as measuring customer experience proxies like abandonment and patience involves uncertainty. A key source of this uncertainty is silent abandonment, where customers leave without notifying the system, wasting agent time and leaving their status unclear. Silent abandonment also obscures whether a customer was served or left. Our goals are to measure the magnitude of silent abandonment and mitigate its effects. Classification models show that 3%-70% of customers across 17 companies abandon silently. In one study, 71.3% of abandoning customers did so silently, reducing agent efficiency by 3.2% and system capacity by 15.3%, incurring $5,457 in annual costs per agent. We develop an expectation-maximization (EM) algorithm to estimate customer patience under uncertainty and identify influencing covariates. We find that companies should use classification models to estimate abandonment scope and our EM algorithm to assess patience. We suggest strategies to operationally mitigate the impact of silent abandonment by predicting suspected silent-abandonment behavior or changing service design. Specifically, we show that while allowing customers to write while waiting in the queue creates a missing data challenge, it also significantly increases patience and reduces service time, leading to reduced abandonment and lower staffing requirements.