E-commerce Customer Churn Prediction based on Customer Segmentation and AdaBoost

doi:10.3969/j.issn.1007-7375.e16-4206

Abstract

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 AdaBoost algorithm is employed to predict. At last, an empirical study on B2C 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.

Key words: customer segmentation, imbalanced data, SMOTE, AdaBoost

CLC Number:

F270

WU Xiaojun, MENG Sufang. E-commerce Customer Churn Prediction based on Customer Segmentation and AdaBoost[J]. Industrial Engineering Journal , 2017, 20(2): 99-107.

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