Toward a recommender system for assisting customers at risk of churning in e-commerce platforms based on a combination of Social Network Analysis (SNA) and deep learning

Recently, e-commerce platforms have gained significant attention in the media. As the number of customers using these platforms continues to grow, they face challenges such as limited support, making it increasingly difficult for customers to find answers to their inquiries, leading to a high attrition rate. This study aims to address the issue of unanswered customer queries in online product discussion forums by leveraging a combination of SNA and deep learning techniques. The primary objective is to reduce the attrition rate by enhancing customer support through peer-to-peer interactions. Initially, we analyze the customer interaction network and thread structures using SNA to identify isolated inquiries. Following this, we apply a deep learning-based model to calculate a similarity score between these queries, which serves as the foundation for our semantic similarity approach to product discussion questions. The results of our experiment, conducted on a Moroccan e-commerce platform, demonstrate the efficacy of our recommendation method in connecting customers with relevant answers and fellow customers who can assist them. The proposed deep learning model provided an accuracy of 0.8529 and a mean squared error of 0.1168.