These New AI Tools Help Retailers Give Faster and More Accurate Product Recommendations

Madeleine Streets
·3 min read

Artificial intelligence and machine learning are becoming more common technologies in retail, as businesses understand their capabilities. Earlier today, Adobe launched two news applications of AI for its Magento commerce platform: visual similarity-driven product recommendations and live search.

Retail investments in technology have accelerated alongside the growing popularity of e-commerce; merchants need to keep up with consumer expectation. As consumers hand over more data, there is a growing demand for this information to fuel a more personalized experience – which is exactly what AI can help achieve.

“The online economy will surely dominate retail well after the pandemic is over,” said Adobe in a company announcement. “That’s why we’re announcing several new ways Adobe Sensei, our AI and machine-learning technology, is being integrated with Magento Commerce to help our global merchants deliver more data-driven and highly-personalized online shopping experiences.”

To strengthen its Product Recommendations tool, Adobe has added the capability to recommend new items to shoppers based on visual similarity. Supported by AI technology, the new solution is able to take any product a consumer is viewing and identify related products that might interest the shopper, based on similarities in color, material, silhouette, texture or size. If a customer is looking for a specific item, these are likely to provide greater choice – and ultimately the right fit.

Artificial intelligence can track trends and patterns within a large catalog of items. This automation can save retail employees from having to manually tag and group various kinds of product, freeing up resources; it also ensures that new additions to merchandise are immediately grouped accordingly. Both existing users of the product recommendation tool and new users of Magento can quickly implement the solution, as it relies on visuals not behavioral data analysis.

“The benefit of this recommendation type is that it allows merchants with a large catalog of products to showcase a wide range of their products to shoppers,” said the Adobe statement. “[Also it] empowers customers to consider all the potential alternatives to the product that they’re looking at, encouraging even wider catalog discovery and inspiration.”

Building off this focus on recommendations is the new Live Search tool from Adobe, which will launch in the first half of 2021. Through data analysis, participating merchants can suggest products to consumers as they’re typing into the search bar; the results will adjust as more information is given. As the function is supported by machine learning, the suggestions will grow more accurate over time as more users search and select products.

“For retailers, being able to guide shoppers to the most relevant search results without needless delays or technical complexities can become a competitive advantage,” said Adobe in the statement. “An Econsultancy study shows that not only is an effective site search capability an important interaction point for shoppers, it can result in a more than 50 percent higher conversion rate if executed properly by the retailer.”

This feature uses the same shopper analysis as the Product Recommendations tool, meaning that it will be seamless to implement when it rolls out to merchants next year.

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