Managing customer retention is critical to a company’s profitability and firm value. However, predicting customer churn is challenging. The extant research on the topic mainly focuses on the type of model developed to predict churn, devoting little or no effort to data preparation methods. These methods directly impact the identification of patterns, increasing the model’s predictive performance. We addressed this problem by (1) employing feature engineering methods to generate a set of potential predictor features suitable for the banking industry and (2) preprocessing the majority and minority classes to improve the learning of the classification model pattern. The framework encompasses state-of-the-art data preprocessing methods: (1) feature engineering with recency, frequency, and monetary value concepts to address the imbalanced dataset issue, (2) oversampling using the adaptive synthetic sampling algorithm, and (3) undersampling using NEASMISS algorithm. After data preprocessing, we use XGBoost and elastic net methods for churn prediction. We validated the proposed framework with a dataset of more than 3 million customers and about 170 million transactions. The framework outperformed alternative methods reported in the literature in terms of precision-recall area under curve, accuracy, recall, and specificity. From a practical perspective, the framework provides managers with valuable information to predict customer churn and develop strategies for customer retention in the banking industry.

中文翻译：

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提高零售银行客户流失预测性能的框架

管理客户保留对于公司的盈利能力和公司价值至关重要。然而，预测客户流失具有挑战性。该主题的现有研究主要集中在为预测客户流失而开发的模型类型上，很少或根本没有在数据准备方法上投入精力。这些方法直接影响模式的识别，提高模型的预测性能。我们通过以下方式解决了这个问题：（1）采用特征工程方法生成一组适合银行业的潜在预测器特征；（2）预处理多数类和少数类以改进分类模型模式的学习。该框架包含最先进的数据预处理方法：(1) 具有新近度、频率和货币价值概念的特征工程，以解决不平衡数据集问题，(2) 使用自适应合成采样算法进行过采样，以及 (3)使用 NEASMISS 算法进行欠采样。数据预处理后，我们使用 XGBoost 和弹性网络方法进行流失预测。我们使用超过 300 万客户和约 1.7 亿笔交易的数据集验证了所提出的框架。该框架在精确召回曲线下面积、准确性、召回率和特异性方面优于文献中报道的替代方法。从实践的角度来看，该框架为管理者提供了有价值的信息，以预测客户流失并制定银行业客户保留策略。