There’s a booming marketplace for the resale of used and vintage goods. The confluence of a glut of returned merchandise and a growing demand for product sustainability has created an industry that is changing the retail landscape: reCommerce.
Retail returns topped $761 billion in 2022 and is projected to reach trillions over the next few years. Due to the complexity of processing these items for resale, merchandise is frequently incinerated, exported, liquidated for pennies on the dollar or dumped into landfills. This is not only unsustainable, it’s costly. As online shopping flourishes and the culture of ‘buy and return’ continues to overwhelm retailers, a resulting used goods marketplace has emerged to a tune of $36 billion in 2022. And that’s just apparel, electronics adds $32.5 billion, used furniture another $12 billion, the list goes on -– placing the resale industry sternly in the trillions.
While there is an abundance of product and a variety of resale marketplaces where these items could be sold, the barrier is in the processing and listing of these products. This has traditionally been a labor-intensive process where products must be manually identified, valued, and characterized before listing for sale. With artificial intelligence (AI) and machine learning, all this is changing.
How AI is Changing the Product Resale Market
Retailers, along with used product resellers, non-profits, thrifters and liquidators, want to capitalize on this thriving reCommerce market to cut the billions of dollars going to waste. AI and machine learning has become the answer. Here are the key ways AI is enabling the reCommerce industry to scale.
- Attribute Identification. Using AI and machine vision, resellers are able to quickly take what was once a very manual process of identifying and assigning product attributes and automate this identification process. Product characteristics such as color, style and even brand can be quickly assessed, automatically, using simple photographs. This can cut hundreds of hours off product processing when used for large batches of items, such as returns, donations and overstocks.
- Product Valuation. Returned, donated and used items typically don’t have designated tags or SKUs that can identify the value or assign a price. Using AI technology, identified attributes can be used to scour data sets across resale markets to properly identify current item value and set a market price. This too takes hours off of the traditionally manual product pricing process and is very effective at optimizing resale revenue.
- Marketplace Listing Automation. Product marketplaces from Poshmark, threadUP and The RealReal to eBay, OfferUp and Facebook Marketplace all require specific adjustments and characterizations to effectively list on their sites. AI technology and automation is being used to produce fully compliant, SEO optimized listings for each marketplace so that products are showcased effectively, no matter where they are being sold.
- Empowering Listing Volume. Listing products for resale is a numbers game. And with so much product volume to move, retailers specifically need to optimize their listing abilities to recoup lost profits from returned goods. AI empowers scalability and incredible volume, enabling sellers to move from a 5,000-item turn a month to exponentially increase overnight.
- Lowers Labor Costs. The cost of human labor has been the number one barrier to increasing reCommerce scale. Through AI and machine vision technology, resellers are closing the labor gap and optimizing profitability without the costs, challenge and difficulty of recruiting workers or shipping product offshore to perform processing. This further reduces costs and enhances rapid growth through product processing scale.
There are many uses for AI and machine learning technology, but perhaps none are as market changing as how AI can transform reCommerce. AI bridges the gap between sustainability, labor shortages, and the increasing consumer demand for recycled goods. It will indeed change the game for today’s retailers as they move quickly to compete in today’s competitive retail industry and maximize the losses from high volumes of returns.
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