Table of Contents
E-commerce returns challenge retailers, increasing costs and logistical complexities. Industry estimates indicate that about 10% of online purchases—4 billion parcels—are returned, straining retailers who rely on inefficient manual handling. AI-driven automation is poised to streamline returns, cut handling costs, and enhance efficiency. Typically, returns involve moving products back to warehouses for inspection, a costly process averaging $20.
Forbes’ Dennis Mitzner describes AI's transformative role, automating various returns processes by gathering detailed data, thus minimizing dependence on customer service. AI also supports sorting and assessing items, allowing inventory turnaround from months to days and reducing handling costs by over 20%.
Looking forward, AI solutions could enable direct resale of returned products, enhancing efficiency and preventing returns caused by mismatched customer expectations. Karl Paadam of Yummy emphasizes AI's impact on product accuracy, which reduces returns through optimized listings.
AI also allows retailers to convert returns into revenue by assessing product conditions and demand in real time. Aviad Raz of ReturnGO notes AI's ability to evaluate condition via customer-uploaded images, aiding in resale strategies.
Competition in AI is rising as startups and logistics providers innovate to optimize returns. Joose Toiviainen of Daze points out that brands understanding returns as a business model will thrive by enhancing logistics.
AI is rapidly reshaping returns management. Machine learning improves logistics and reduces excess stock in sectors like fashion and electronics. Dr. Yishai Ashlag states that retailers must combine flexible operations with AI optimization.
