E-commerce big data processing based on an improved RBF model

In the dynamic landscape of China’s booming economy, the surge in e-commerce customer volume presents both opportunities and challenges, notably in managing customer churn (CC). Addressing this critical issue, this study introduces an innovative approach employing a radial basis function neural network for predicting CC within the e-commerce sector. To enhance the model’s performance in handling the vast and complex data inherent to e-commerce, the least absolute shrinkage and selection operator regression algorithm is employed, optimizing the model’s predictive accuracy. By meticulously analyzing the customer lifecycle, this refined model adeptly predicts churn at various stages, enabling the identification of features most correlated with churn. Empirical results underscore the model’s exceptional capability, achieving a prediction accuracy of 95% and a remarkably low loss rate of 3%. Furthermore, during the excavation, advanced, stable, and decline stages of the customer lifecycle, accuracy levels of 97.6, 93.1, 92.7, and 91.8% are attained, respectively, facilitating the precise selection of highly correlated customer features. Thus, the advanced churn prediction model proposed herein significantly contributes to the e-commerce domain, offering a robust tool for strategizing customer retention and mitigating churn.