Deep Learning for Customer Relationship Management in E-commerce
Saadaldeen Rashid Ahmed, Lubna Qassim ALhashmi, Ali Jabbar Hussein, Ahmed Dheyaa Radhi, Ahmed Abdulmunem Radhi, Mohammed Rashid Ahmed, Abadal-Salam T. Hussain, Jamal Fadhil Tawfeq · 2024
Customer Relationship Management (CRM) plays a pivotal role in the success of e-commerce businesses by fostering stronger connections with customers. De-spite its importance, existing CRM approaches often fall short in effectively leveraging the vast amounts of data available in digital environments. This paper addresses this gap by proposing a novel framework that harnesses the power of deep learning techniques to enhance CRM in e-commerce. Building upon a comprehensive review of previous literature, we identify the need for advanced analytical methods to unlock insights from complex data patterns. Our approach in-volves the development and evaluation of artificial neural networks (ANNs) and deep neural networks (DNNs) tailored to CRM tasks such as customer segmentation, churn prediction, and recommendation systems. Leveraging a meticulously curated dataset encompassing diverse customer attributes and transaction history, we rigorously train and assess our models. Our results demonstrate the efficacy of deep learning in capturing nuanced customer behaviors and improving predictive accuracy in CRM tasks. Our highest achieved accuracy stands at 90%, showcasing the potential of our approach in driving superior CRM performance in e-commerce environments. Through this study, we contribute to bridging the research-practice gap in CRM by offering practical insights and methodologies for leveraging deep learning in e-commerce environments.