大数据下交叉销售问题的建模与预测.pdf

Advances in Applied Mathematics 应用数学进展, 2017, 6(9), 1236-1247 Published Online December 2017 in Hans. /journal/aam /10.12677/aam.2017.69149 Modeling and Prediction of Cross-Selling Problems in Big Data 1 2 Xingfang Huang , Xuelian Wang 1 Institute of Statistics and Data Science, Nanjing Audit University, Nanjing Jiangsu 2 School of Mathematics, Southeast University, Nanjing Jiangsu st nd th Received: Dec. 1 , 2017; accepted: Dec. 22 , 2017; published: Dec. 29 , 2017 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 pre- dict 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. Keywords Cross-Selling, Multiple Logistic Model, Two-Stage Logistic Model 大数据下交叉销售问题的建模与预测 1 2 黄性芳 ，王雪莲 1南京审计大学统计科学与大数据研究院，江苏 南京 2 东南大学数学学院，江苏 南京 收稿日期：2017年12月1 日；录用日期：2017年12月22 日；发布日期：2017年12月29 日 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 文章引用: 黄性芳, 王雪莲. 大数据下交叉销售问题的建模与预测[J]. 应用数学进展, 2017, 6(9): 1236-1247.