Computer Vision Helps Brands See More Profit For Second-Hand Retail Markets

By Carlos Anchia, Plainsight

As consumer expectations shift – with increased awareness of corporate impact and responsibility for rising carbon levels – sustainability has become a top priority for retailers. One way for brands to demonstrate that they truly care about reducing their carbon footprint is by creating a second-hand market for their products to reduce waste. 

Name-brand retailers like Levi’s, Patagonia, Eileen Fisher, Coach, and Lululemon are leading the movement, creating robust online pre-owned marketplaces and establishing new models for industry peers to follow. In response to a study by GlobalData and thredUP, 60% of retailers said they have or are open to offering pre-owned goods to their customers. The study estimated that 23.8K retailers are interested.

Along with minimizing waste, modernizing pre-owned markets has been a boon for customers. Previously, finding the right pre-owned apparel might have meant hours of browsing through thrift stores, vintage markets, or traditional online marketplaces like eBay. Once shoppers got their hands on the perfect item, they often lacked the necessary context and evidence to verify its authenticity, ensure fair pricing, and understand the full story behind each unique piece.

Third-party players like ThredUp and RealReal have the leadership position, but brands are catching up quickly, recognizing the potential of second-hand markets to strengthen their relationships with loyal shoppers, add revenue streams, and opportunities to meet sustainability goals. According to the same GlobalData study, second-hand sales will reach $43 billion this year and $77 billion by 2025. 

Vision AI For Secondary Retailers

However, to achieve true scale, technology has to play an important role. In that, computer vision technology could represent a significant turning point for creating a profitable circular marketplace for individual brands and the fashion industry by helping directly address the considerable and controversial environmental impact of the fashion supply chain. 

The potential applications for machine learning and vision AI in retail are staggering. Imagine machine learning and Vision AI applied to the products themselves. Custom models created and deployed within retailer second-hand facilities could guarantee product quality, accurately identify unique brand thumbprints, capture insights for popular in-demand styles, and generate insights from archival collections, increasing their intrinsic value beyond their initial purchase. 

The technology is already available and what might seem futuristic is well within our reach. The added benefit is that vision AI gets more intelligent with use: The more items processed, the better the algorithm gets. GlobalData estimates that 36 billion clothing items are thrown away in the U.S. each year, 95% of which could be recycled or reused. Without AI-based automation technologies, the humans, costs, and time resources required to manually perform all the related tasks in compliance with brands’ aggressive sustainability goals are staggering. A design coordinator at Levi’s for example built a computer vision-powered application to help with the extraordinary time-consuming process of color-matching threads, displaying results in seconds, and eliminating hours of manual work. 

 Vision AI is an exploding solution across major markets, and fashion is among the latest applications. As fashion continues to embrace technology and recognize the true potential of AI, we can expect to see more brands create profitable second-hand marketplaces and businesses that contribute to their sustainability goals. 

Here are a few ways vision AI is transformative for the brand owned second-hand market: 

1. Vision AI can detect anomalies faster and more accurately. While opinions differ as to whether people are capable of multitasking, there’s no question that vision AI can. Models for retailers can continuously analyze designs and evaluate the quality of materials and workmanship. AI can easily detect fake and quickly discern a product’s quality. As models continue to learn, they get better and better at inspecting items ahead of resale, recycling, or upcycling.
2. Vision AI never blinks. The unblinking eye of vision AI platforms collects and analyzes data round the clock, enabling proactive responses and continuous process improvement. Solutions also can dynamically create valuable reference libraries of video and imagery training datasets and models to leverage for future training and quality standardization and control. 
3. Vision AI reveals previously hidden insights: By turning visual data into a source of insight, vision AI enables smarter, more strategic decision making. For retailers, learning more about items, sizes, styles, and trends, can provide for better planning, pricing, and customer service while even changing the makeup of future collections.

Striking a balance between good taste and positive social impact can be tricky, especially in an industry like fashion, where supply chain waste is often an open secret and sustainability is an increasingly vital brand tenet sought after by consumers. Retailers are making great strides by embracing new technologies like vision AI to enable cleaner, greener processes while building stronger brand loyalty and succeeding in new markets. Levi’s may be the first retailer to hire a Chief AI Officer, but as second-hand markets and sustainable practices continue, we expect to see retailers accelerate their pursuit of new AI technologies that drive breakthrough innovation.

About The Author

Carlos Anchia is CEO and Co-Founder of Plainsight.