大数据下交叉销售问题的建模与预测
Modeling and Prediction of Cross-Selling Problems in Big Data
摘要:
多重Logistic回归和两阶段Logistic回归方法在处理多变量和大数据问题中具有较好的优越性。本文针对美国某保险公司一个保险项目11个月的交叉销售数据，分别在加利福尼亚州和非加利福尼亚州两个区域采用多重Logistic回归和两阶段Logistic回归方法，建立了由车险到家庭险的交叉销售模型，预测购买两种不同家庭险的潜在客户。实践表明，两阶段Logistic模型预测效果更佳。
Abstract:
Multiple Logistic method and Two-stage Logistic method all have good advantages of dealing with large number of variables and big data. The main purpose for this paper is building a cross-selling model from Auto Insurance to Home Insurance and then predicting the customers’ behavior. A famous American insurance company’s cross-selling data in eleven months is used in this paper. Multiple Logistic method and Two-stage Logistic method are separately applied to build cross-selling models on California and Non-California area. The results for these models can predict which products the prospects are more likely to purchase. Finally, it makes a conclusion that Two-stage Logistic model performs better on both California and Non-California data.
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