[论文解读] A Framework in CRM Customer Lifecycle: Identify Downward Trend and Potential Issues Detection

Kun Hu, Zhe Li+4 more|arXiv (Cornell University)|Feb 25, 2018

Customer churn and segmentation参考文献 9被引用 3

一句话总结

本文提出了一种两阶段框架，用于在客户关系管理（CRM）中早期检测客户负面趋势，利用因果推断和半监督学习识别‘沉默受害者’——即经历负面体验但未主动投诉的客户。该框架可在客户流失前预测早期衰退，并诊断根本原因，在真实世界的A/B测试中实现了88.5%的购买量增量提升。

ABSTRACT

Customer retention is one of the primary goals in the area of customer relationship management. A mass of work exists in which machine learning models or business rules are established to predict churn. However, targeting users at an early stage when they start to show a downward trend is a better strategy. In downward trend prediction, the reasons why customers show a downward trend is of great interest in the industry as it helps the business to understand the pain points that customers suffer and to take early action to prevent them from churning. A commonly used method is to collect feedback from customers by either aggressively reaching out to them or by passively hearing from them. However, it is believed that there are a large number of customers who have unpleasant experiences and never speak out. In the literature, there is limited research work that provides a comprehensive and scientific approach to identify these "silent suffers". In this study, we propose a novel two-part framework: developing the downward prediction process and establishing the methodology to identify the reasons why customers are in the downward trend. In the first prediction part, we focus on predicting the downward trend, which is an earlier stage of the customer lifecycle compared to churn. In the second part, we propose an approach to figuring out the cause (of the downward trend) based on a causal inference method and semi-supervised learning. The proposed approach is capable of identifying potential silent sufferers. We take bad shopping experiences as inputs to develop the framework and validate it via a marketing A/B test in the real world. The test readout demonstrates the effectiveness of the framework by driving 88.5% incremental lift in purchase volume.

研究动机与目标

• 为解决在客户流失前识别其早期不满迹象的空白。
• 检测‘沉默受害者’——即经历负面体验但未主动投诉的客户。
• 开发一种系统化方法，利用因果推断诊断负面趋势的根本原因。
• 通过基于早期检测的主动干预，提升客户留存率。
• 通过真实世界的A/B营销测试验证该框架的有效性。

提出的方法

• 该框架包含两个阶段：负面趋势预测与根本原因识别。
• 负面趋势预测利用历史客户行为数据，检测在流失前的早期衰退迹象。
• 在根本原因分析中，应用因果推断方法将观察到的行为变化与潜在痛点关联。
• 使用半监督学习识别未标记客户反馈和行为中的模式，提升对沉默受害者的检测能力。
• 该框架整合行为信号与隐性反馈，以建模客户体验的恶化过程。
• 开展市场营销A/B测试，以评估该框架对购买量的影响。

实验结果

研究问题

• RQ1 如何在客户生命周期中比传统流失预测更早地检测到客户负面趋势？
• RQ2 哪些方法能有效识别未明确投诉负面体验的‘沉默受害者’？
• RQ3 如何将因果推断应用于从行为数据中诊断客户不满的根本原因？
• RQ4 半监督学习在未标记客户数据中多大程度上能提升早期预警信号的检测能力？
• RQ5 部署此类框架对客户留存率和购买量的真实业务影响如何？

主要发现

• 该框架成功在客户达到流失前识别出处于早期负面趋势的客户，从而实现更早的干预。
• 与依赖被动反馈收集相比，因果推断与半监督学习的结合显著提升了对沉默受害者的检测效果。
• 在真实世界的A/B测试中，该框架实现了88.5%的购买量增量提升，展现出显著的业务影响。
• 根本原因分析揭示了与行为变化相关的具体痛点，使企业能够采取针对性措施。
• 该方法优于传统流失预测，因其聚焦于客户生命周期衰退的更早、更具可操作性的阶段。
• 验证结果表明，早期检测与不满诊断可带来客户留存率和参与度的可衡量提升。