Paper Key : IRJ************066
Author: Adnan Mahedi Panwala ,Ishan Goswami ,Professor Hasmukh Panchal
Date Published: 03 Apr 2025
Abstract
E-commerce businesses face significant challenges in managing reverse logistics, with product returns increasing operational costs and affecting sustainability. This research explores how predictive analytics can enhance returns management by reducing return rates, preventing fraudulent returns, and improving cost efficiency. Using primary data collected through surveys from e-commerce customers and industry professionals, this study analyzes key trends in return behaviors, reasons for product returns, and the impact of fraud on businesses.The research examines customer perceptions of return policies, the role of AI-driven solutions in minimizing unnecessary returns, and the potential of predictive analytics in detecting fraudulent activities such as false defect claims and the misuse of return policies. The findings highlight the growing need for data-driven decision-making in e-commerce to optimize reverse logistics operations while maintaining customer satisfaction. By leveraging predictive analytics, businesses can implement proactive strategies to minimize returns, enhance fraud detection, and promote sustainable and cost-effective returns management. This study provides valuable insights for e-commerce companies looking to refine their return policies and improve overall supply chain efficiency.
DOI Requested