The Construction of a Model for Predicting Users' Repeat Purchase Behavior and its Impact on the Economic Efficiency of Enterprises

Abstract

Aiming at the shortcomings in efficiency and accuracy of the current prediction methods of user repeat purchase behavior in e-commerce enterprises, an intelligent prediction model of user repeat purchase behavior based on machine learning was proposed. In order to enhance the quality of the experimental data, Kernel Principal Components Analysis (KPCA) and the synthetic Minority oversampling technique (SMOTE) were first used to preprocess the data. After that, repeat purchase behavior is predicted using a Support Vector Machine (SVM). Then, the Sparrow Search Algorithm (SSA), based on multi-strategy optimization, is suggested to overcome the SSVM's drawbacks. The Smooth Support Vector Machine (SSVM) is employed as the feature classifier for classification. On this basis, an intelligent prediction model of user repeat purchase behavior based on ISA-SSVM is constructed to achieve efficient prediction of user repeat purchase behavior. The results showed that the fitness value of the ISA-SSVM algorithm was always higher than other algorithms as the number of iterations increases. And its convergence speed is fast, when the number of iterations is 13, the fitness value reaches 94.6%. The error value of this model is 0.14, the loss value is 0.20, the F1 value is 0.957, the recall value is 0.965, the MAE value is 8.52, the fit degree is 0.992, the prediction accuracy is 97.92%, and the AUC value is 0.995, all of which are better than the other two models. As a result, the ISA-SSVM developed in this work outperforms previous models in terms of its ability to forecast customers' recurrent purchasing behavior. The research approach is helpful for e-commerce businesses to implement precision marketing, which has a good effect on the advantages of e-commerce businesses.