基于可解释机器学习模型的电信行业客户流失预测研究

Research on telecom industry customer churn prediction based on explainable machine learning models

在电信行业中,客户流失的准确预测对于相关企业维持市场竞争力和增加收益至关重要。为此提出一个结合CatBoost算法和SHAP(shapley additive explanations)模型的客户流失预测框架,旨在提高预测的准确性,同时增强模型的可解释性。利用新疆某通信公司的实际营业数据,通过数据预处理及特征工程,构建预测模型,选取5种主要关键性能指标评估模型性能。实验结果显示,所提出模型在选取的评价指标上均优于当前主流机器学习预测模型。最后引入SHAP框架增强模型可解释性,揭示影响客户流失的关键因素,并提供具体的因素影响程度,为电信企业制定针对性的客户保留策略提供了科学依据。

In the telecom industry,accurate prediction of customer churn is crucial for the companies involved to maintain market competitiveness and increase revenue.To this end,a customer churn prediction framework combining CatBoost algorithm and SHAP model was proposed,aiming to improve the accuracy of prediction and enhance the interpretability of the model.Using the actual business data of a communication company in Xinjiang,the prediction model was constructed through data preprocessing and feature engineering,and five major key performance indicators were selected to evaluate the model performance.The experimental results show that the proposed model outperforms the current mainstream machine learning prediction models in all the above evaluation indicators.Finally,the SHAP framework was introduced to enhance the model interpretability,reveal the key factors affecting customer churn,and provide the specific influence degree of the factors,which provided a scientific basis for telecommunications enterprises to formulate targeted customer retention strategies.