Neural Network-Based Customer Behavior Modeling for Dynamic Conversion Funnel Optimization in Digital Retail

Abstract

The need to increase conversion of traffic in using the different platforms and touching points from the digital retail environment cannot be over emphasized.Some of the conventional status that are generally utilized in conversion optimization fail to capture the intricate and dynamic paths made by customers in their engagement with Internet shops. In order to capture this dynamic behavior of the customers in the conversion funnel, this paper presents a novel method based on neural networks. Besides, by using Artificial Neural Networks (ANNs) for the machine learning process, this model makes real-time predictions of the customer activity and reveals proper key moments for retailers to influence the customer’s decision-making process to optimize customer experience. These findings show that the application of the neural network model improves the accuracy of the representations of the customer behavior to allow for the provision of the best conversion strategies that increase sales productivity.