Not only can some AI technologies produce iffy results, brands considering adding AI-based bells and whistles should be clear on goals for the features first, says Gumz. However, a recent view of a Wayfair product page featuring a teal pillow made with pleated, synthetic silk-like material turned up ’visually similar’ products including a throw pillow with musical notes printed in black and white and another with a multicolored toucan embroidered on a white background.

The online retailer serves up ’Visually Similar Items’ on product pages. Wayfair began rolling out its visual search capabilities in 2017. It’s not clear whether product discovery will always turn up what consumers might expect. It might have more problems detecting similarity based on texture, he says. Visual AI or “computer vision” tends to work well to surface similar patterns and colors, such as among a large collection of floor rugs, says Kevin Gumz, vice-president of applied AI and data platforms at Kin and Carta. Kris says the improved experience has helped improve conversion rates. Wayfair, Forever21, Neiman Marcus and others have been leaning into AI and visual search, says Kami Kris, chief operating officer of Loop Integration, the ecommerce arm of digital consultancy Kin + Carta. “There could be something buried, and if you didn’t write a merchandising rule, I will never see that product,” he says. Fast-fashion brands that churn out new items rapidly but might not have time to set up complex merchandising rules for that eighteenth seasonal winter hoodie might also benefit, says Green. The visual similarity tech is well-suited for e-commerce brands with a large catalog of products – for instance, a seller that offers furniture from hundreds of chair and sofa makers or a B2B firm selling thousands of electronic parts.

Think of the visual similarity recommendation tool as a digitized sales associate who knows a shop’s inventory well and can help a shopper find something with similar color, shape, size, material or style, or when “You don’t really know what you want till you see it,” says Ryan Green, senior product marketing manager at Adobe. For example, Amazon’s StyleSnap finds items that are similar to clothing someone in a photo is wearing, such as a pair of frayed jeans or chunky sweater. Advancements in the space include visual search tools that allow people to find products based on imagery from photos they upload. Other firms that have developed visual AI technology for ecommerce include Syte and Vue.ai. There is an expectation now that search will take less effort.” What the AI can do is that and find similar characteristics which improves the search and lessens the frustration. And you wouldn’t have a human on the other end putting in 400 descriptors for the item. “People won’t type in ‘rounded’ or ‘multiple colors’ and other types of terms. “It’s interesting because you’re matching things you can’t easily describe,” says Travis Johnson, global chief executive office at the e-commerce consultancy Podean. Historically, product recommendation required consumer behavioral analysis, but now these AI-powered leaps offer the potential for a better consumer experience and a flatter playing field for smaller vendors looking to compete against better-funded competitors.

While Amazon, Target, eBay and others have rolled out such features, Adobe is now making this option available to smaller and mid-sized merchants who use its Magento Commerce platform. The idea is to give people an easy way to find things that have visual attributes that resemble products they already like, based on similar hues, patterns and even textures. Next week, Adobe is launching a product recommendation tool that uses machine learning and artificial intelligence to surface items that are visually similar to others.

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