With the advancement of electronic service platforms, customers exhibit various purchasing behaviors. Given the extensive array of options and minimal exit barriers, customer migration from one digital service to another has become a common challenge for businesses. Customer churn prediction (CCP) emerges as a crucial marketing strategy aimed at estimating the likelihood of customer abandonment. In this paper, we aim to predict customer churn intentions using a novel robust meta-classifier. We utilized three distinct datasets: transaction, telecommunication, and customer churn datasets. Employing Decision Tree, Random Forest, XGBoost, AdaBoost, and Extra Trees as the five base supervised classifiers on these three datasets, we conducted cross-validation and evaluation setups separately. Additionally, we employed permutation and SelectKBest feature selection to rank the most practical features for achieving the highest accuracy. Furthermore, we utilized BayesSearchCV and GridSearchCV to discover, optimize, and tune the hyperparameters. Subsequently, we applied the refined classifiers in a funnel of a new meta-classifier for each dataset individually. The experimental results indicate that our proposed meta-classifier demonstrates superior accuracy compared to conventional classifiers and even stacking ensemble methods. The predictive outcomes serve as a valuable tool for businesses in identifying potential churners and taking proactive measures to retain customers, thereby enhancing customer retention rates and ensuring business sustainability.

随着电子服务平台的进步，顾客呈现出多样化的购买行为。鉴于广泛的选择和最小的退出壁垒，客户从一种数字服务迁移到另一种数字服务已成为企业面临的共同挑战。客户流失预测（CCP）成为一项重要的营销策略，旨在估计客户放弃的可能性。在本文中，我们的目标是使用新颖的稳健元分类器来预测客户流失意图。我们使用了三个不同的数据集：交易数据集、电信数据集和客户流失数据集。使用决策树、随机森林、XGBoost、AdaBoost 和 Extra Trees 作为这三个数据集的五个基本监督分类器，我们分别进行交叉验证和评估设置。此外，我们采用排列和 SelectKBest 特征选择来对最实用的特征进行排名，以实现最高的准确性。此外，我们利用 BayesSearchCV 和 GridSearchCV 来发现、优化和调整超参数。随后，我们将精炼后的分类器分别应用于每个数据集的新元分类器的漏斗中。实验结果表明，与传统分类器甚至堆叠集成方法相比，我们提出的元分类器表现出更高的准确性。预测结果是企业识别潜在流失者并采取主动措施留住客户的宝贵工具，从而提高客户保留率并确保业务可持续性。