OPTIMIZING SHOPPING MALL DORMANT VIP CUSTOMER PROMOTION DECISIONS WITH BACK PROPAGATION NEURAL NETWORKS PREDICTION MODEL

To avoid the inaccuracy and cognitive bias of the existing customer retention predictive model, a robust database with sufficient mining techniques have attracted great attention in the field of consumer research, especially in the optimization of retailing business decisions. However, choosing optimum promotion decisions that maximize the retailer profit while minimizing advertising costs are still a challenging task. By minimizing mall costs and maximizing the revisit intention of the target dormant VIP customer, this study utilizes an intelligent and convenience model based on back propagation neural networks to organize a large-scale shopping mall objective when constructing the publicity solution. When compared to the previous non-differentiation strategy, the proposed back propagation neural networks prediction model helped the mall reduce overall advertising costs by 66.2% while precisely measuring and awakening the dormant VIPs with 90%