【摘要】 为了准确识别高价值电子商务客户,提高对非流失客户的预测精度,本文首先对电子商务客户进行Kmediods聚类细分识别出高价值客户,再应用过采样和欠采样相结合的改进SMOTE处理不平衡的电子商务客户数据,最后用Ada Boost算法进行预测。实证研究表明,与成熟的客户流失预测算法BP神经网络、支持向量机(SVM)和改进支持向量机(CW-SVM)相比,该方法能更好地提高预测效果,与未细分前预测效果对比,客户细分后预测效果更好。更多还原

【Abstract】 In order to identify the high value customers as well as improve the prediction accuracy of non-churn customers,the high value customer groups are identified by K-mediods clustering and the churn data processed with improved SMOTE(synthetic minority oversampling teachnique), which combines oversampling and undersampling methods to balance the datasets and generates the certain size of positive and negative samples by setting sampling ratio and controlling the model training time, then Ada Boost algorithm is employed to predict. At last, an empirical study on B2 C E-commerce platform shows that the integrated model has better efficiency and higher prediction precision compared with the mature customer churn prediction algorithms, such as BP Neural, SVM(support vector machine) and CW-SVM(class weighted support vector machine). Meanwhile, the prediction model of e-commerce customer churning based on customer segmentation has been proved to have better prediction performance.更多还原