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当前,我国保险业在深度和广度上仍有提升空间,而在数字化背景下,保险公司正面临着如何有效运用数据技术来制定精准客户画像和营销策略的重大挑战。 本案例基于CHFS数据,构建了家庭商业保险购买影响因素的分析框架,并建立了预测模型。案例围绕家庭基本信息、健康状况及保险配置三大特征集,选取了22个特征值,将数据分为测试集与验证集,运用欠抽样和过采样技术处理非均衡数据,并评估了LightGBM、随机森林、XGBoost三大模型的效能。结果显示,采用SMOTE方法处理后的随机森林模型表现最佳。案例对特征影响程度进行了剖析,并据此描绘了家庭商业保险购买客户群体的特征画像。本案例为保险公司精准营销策略提供了数据支持,也为我国保险市场的繁荣发展注入了新活力。
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