摘要：用户流失是电信运营商一项成本高昂且长期存在的挑战。在成熟市场中，每月约 1.9% 的流失率每年侵蚀数十亿美元收入。预测模型能够准确识别有流失风险的客户，但通常无法被纳入一线 CRM 工作流，因为高性能集成模型和非线性架构缺乏透明度：仅有一个概率评分、却不知道客户面临风险的原因，客户留存专员便无法设计个性化干预措施。本文旨在弥补这一缺口。我们在 IBM Telco Customer Churn 基准数据集上比较了四种分类器——Logistic Regression、Random Forest、XGBoost 和 LightGBM——该数据集包含 7,043 条记录、19 个特征，原始流失率为 26.5%；仅对训练集划分使用 SMOTE 将样本平衡至 50%。Logistic Regression 取得最高的 AUC-ROC（0.8411），LightGBM 获得最高准确率（78.42%）；四种模型的 AUC 均处于 0.011 的区间内（0.831—0.841），5 折交叉验证进一步确认领先模型的表现实际上不相上下。解释以两种粒度提供：全局 SHAP 排名显示，客户在网时长、累计费用和按月合约是最主要的流失信号；而实例级 SHAP 与 LIME 分解则揭示每项预测背后的驱动因素。在这些输出的基础上，我们提出一个四层 CRM 集成架构：该架构将风险评分和归因向量转换为分层客户细分，将顶级特征映射为结构化留存行动模板，并将营销活动结果导入再训练反馈循环。预计针对风险最高的五分之一客户进行定向干预，可使整体流失率降低 3.3—5.3 个百分点，并在每个营销活动周期保留约 19.9 万至 31.9 万美元价值。

Abstract:Subscriber attrition is a costly, persistent challenge for telecommunications providers, with monthly churn of roughly 1.9% in mature markets eroding billions in revenue annually. Predictive models can flag at-risk customers accurately, yet they are routinely excluded from frontline CRM workflows because high-performing ensemble and non-linear architectures are opaque: a retention specialist cannot design a personalised intervention from a probability score alone, without knowing why a subscriber is at risk. This paper addresses that gap. We benchmark four classifiers--Logistic Regression, Random Forest, XGBoost, and LightGBM--on the IBM Telco Customer Churn benchmark (7,043 records; 19 features; 26.5% churn, balanced to 50% via SMOTE on the training partition only). Logistic Regression attains the strongest AUC-ROC (0.8411) and LightGBM the highest accuracy (78.42%); all four fall within a 0.011 AUC band (0.831--0.841), and 5-fold cross-validation confirms the leading models are effectively tied. Explanations are delivered at two granularities: a global SHAP ranking identifying tenure, total charges, and month-to-month contract as the dominant churn signals, and instance-level SHAP and LIME decompositions that expose the drivers behind each prediction. Building on these outputs, we introduce a four-layer CRM integration architecture that converts risk scores and attribution vectors into tiered segmentation, maps top features to structured retention-action templates, and routes campaign outcomes into a retraining feedback loop. Targeting the highest-risk quintile is projected to cut overall churn by 3.3--5.3 percentage points, preserving an estimated $199K--$319K per campaign cycle.