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This New Tool Uses Details Too Small for the Human Eye to Make Personalized Recommendations

AI-driven personalization tools are being rolled out by technology companies in order to help retailers provide a more customized experience. The latest feature from omnichannel engagement platform Emarsys is aiming to improve the personalization performance of retailers, by tailoring each experience to the individual consumer and not to larger shopper segments.

Personalization is a growing area of investment for merchants, as consumers expect to be served an individual experience, especially from online retailers that they visit regularly. Recent studies have shown that when retailers recognize a returning customer and tailor their shopping journey accordingly, consumers report being more likely to purchase and more likely to return in the future. Much like a store associate might remember a consumer’s face, e-commerce sites should remember a frequent customer.

But many current solutions create customized experiences tied to a particular demographic or subsection of the consumer base – not to the individual shopper. While this can still produce some positive results, it is not as effective as a solution that can identify each personal nuance and respond accordingly.

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“Being able to personalise on a genuinely one-to-one level with customers, based entirely on their behaviour and preferences, is something that even Amazon can only dream of,” said Raj Balasundaram, SVP of AI at Emarsys.

With the new Emarsys “AI Stylist” tool, retailers will be able to recommend products to each customer that match their browsing and purchase history. By tapping into Google Cloud’s image recognition technology, products from the retail catalog can be tagged according to material, texture, color, pattern and fit – including details unrecognizable to the human eye.

Woman shopping online with smartphone, fashion store in background
When a retailer makes a tailored recommendation to an individual shopper, they are more likely to purchase.
CREDIT: nikolas_stock - Adobe Stock

This information is then cross-referenced with shopper behavior online, looking at both browsing and purchase activity, in order to gauge preferences. This analysis is used to generate personalized product recommendations, which are automatically served to the shopper in order to guide the shopper journey and prompt additional purchasing.

Not only does this improve the customer’s experience and increase the chance of add-on sales, but tools like AI Stylist can also inform merchandising strategy. The use of artificial intelligence means that the recommendations will only grow more accurate over time, providing clear insight into the most popular product styles and features. This information can help retailers manage their current inventory, plan for future assortments, and inform future collections.

“It’s about shifting from thinking about broad terms like ‘demand’ to the individual preferences and need of individual customers,” said Paul Gunn, head of digital marketing and CRM at Frasers Group, parent of Sports Direct. “One of the things that COVID-19 highlighted was the fact that trends are very difficult if you’re always looking at them in retrospect, and AI Stylist enables us to automate in a way that recognises trends as they’re being created.”

Insight into upcoming trends can also help reduce waste, by minimizing the amount of product that will remain unsold at the end of the season. With many companies trying to improve their sustainability performance, this can be a useful way to make manufacturing  simultaneously more efficient and economical.

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