基于随机森林算法的广电客户流失预警模型

Early Warning Model about Radio and Television Customer Loss Based on Random Forest Algorithm

针对广电领域客户流失日益严重的问题,提出一种利用机器学习技术构建流失预警模型实现精准客户流失预警的方法。采用数据挖掘技术,结合该领域实际业务场景,基于随机森林算法和逻辑回归算法,构建广电领域客户画像标签体系,明确客户流失的定义,训练客户流失预测模型并在实践中对模型进行验证。结果显示,采取随机森林算法建立的流失预警模型在广电客户测试中表现良好,能在存量客群中有效地发现高风险且高价值的潜在流失客户群体,为实际工作指导一线人员提前制定高效、针对性的维系措施,提前挽留高风险客户,以达到降本增效的目的。

This paper proposes a method to address the increasingly severe problem of customer churn in the broadcasting industry through the use of machine learning technology.By leveraging data mining techniques and taking into account the practical business scenario of the field,this method constructs a customer portrait label system in the broadcasting domain based on both the random forest algorithm and logistic regression algorithm.The definition of customer churn is also clarified in order to train and test the customer churn prediction model.The experimental results demonstrate that the broadcasting customer churn warning model established using the random forest algorithm performs well in testing,enabling it to effectively identify valuable customer groups with potential high churn risk from a large pool of pre-existing customers.This allows front-line personnel to proactively design efficient and target-oriented maintenance measures,which can retain high-risk customers in advance,ultimately reducing costs and increasing efficiency.