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3 Ways Fashion Retailers are Using AI for Operational Efficiency


Today’s apparel retailers can choose from unending menu of high-tech offerings promising to transform their business. But not all technological tools–particularly those calling themselves artificial intelligence (i.e. fashion AI)–are created equal.

In fact, after the 2019 NRF Big Show, the LA Times boldly announced that retailers are wasting their time on tech that looks exciting, yet doesn’t ultimately serve merchants or their customers.

Sure, there are fashion AI platforms that pack more pizzazz than punch.

But most apparel retailers are savvy enough to tell the difference. Here are three AI applications that apparel retailers are using to boost productivity, drive profits, and improve customer experience.


Help Online Shoppers Complete the Look

Shoppers are hip to the reality that merchandisers don’t have enough time to put real thought into website product recommendations. Sometimes, the products even compete with, rather than augment, the item a shopper is viewing. And, the process of generating product suggestions can be laborious and time-consuming.

That’s why apparel retailers are turning to “complete the look” technology to reduce the time merchandisers spend manually suggesting items and to make automated suggestions more effective than ever. Adidas, for example, recently partnered with AI platform Findmine to take kick product suggestions into high gear.

Before adding AI to the mix, Adidas’ online “complete the look” tool took merchandisers 27 steps and about 20 minutes per product. Beyond that, the manual process resulted in fewer than 10% of the brand’s products appearing in recommendations, according to Chain Store Age. Adidas knew that the potential revenue lift from e-commerce would be much greater if they could recommend full looks, rather than individual items.

Adidas may have been skeptical that programmed outfits could rise to the standards of those generated by a merchandiser. Yet after an initial 6-week A/B test, it was clear that neither merchants nor customers could tell the difference between looks generated by fashion AI platforms and those suggested my Adidas employees.

With fashion AI at work powering their “complete the look” tool, Adidas has seen powerful results. Adidas decreased the time merchandisers spend on recommendation-related tasks by 95 percent and increased the number of items featured in “complete the look” by 960 percent. Brian Klavitter, Adidas’ senior director of consumer experience, says this fashion AI tool “helped ensure that our newest products have cross-selling from day 1, improving conversion, average order value, and customer satisfaction.”


Expedite and Enhance the Design Process

The fashion industry will always be driven by a high degree of gut feel. The best fashion AI applications—perhaps more appropriately described as “augmented intelligence”—arm human designers with cutting-edge tools to more effectively perform their jobs.

Take the recent collaboration between Tommy Hilfiger, Fashion Institute of Technology (FIT), and IBM to spark design inspiration. The partnership showcased what that happens when the industry’s most creative minds capitalize on its most powerful technology. As Forbes describes, FIT students applied IBM’s fashion AI tool to hundreds of thousands of Hilfiger’s product and runway images to existing patterns from fabric sites.

“The machine learning analysis gave us insights about the Tommy Hilfiger colors, silhouettes and prints that we couldn’t begin to consume or understand with the human mind,” described Michael Ferraro, executive director of FIT’s Inform Design and Tech Lab.

The end result? New inspiration for designers. The technology lays the groundwork for retailers to eventually let consumers customize merchandise in a way that’s seamless and true to the iconic Tommy Hilfiger brand. Of course, retailers like Nike and Levi’s are already offering a certain level of product customization. In the coming years, you can expect to see AI advances further reduce turnaround times and facilitate speedy trend predictions, bringing products with time-sensitive demand to market quicker than ever before.


Give Brick-and-Mortar Shoppers What They Want

The retail industry is abuzz about personalization, i.e. anticipating and meeting each individual shopper’s desires. The practice is relatively straightforward in the world of e-commerce because retailers know shoppers’ browser, purchase, click, and linger history. With all this data at their disposal, retailers have gotten adept at presenting shoppers with tempting, personalized offers.

In an apparel store setting, personalization is a taller order. It’s hard to know with accuracy what SKUs customers in a single store want, and then deftly deliver it to them.  The matter is made more complicated when one product has endless colorways and SKUs. Consider that a single bra, for example, comes in as many as 72 sizes and at least two colors. To deliver, stores need to understand which SKUs are in demand at a specific store and then ensure those SKUs are easy to find and beautifully merchandised.

Levi’s partners with the AI experts at CB4 to overcome these obstacles. CB4 applies patented AI algorithms to Levi’s raw POS data to discover hyperlocal selling patterns. This means Levi’s can easily identify individual items that more most likely to sell in a specific store. CB4 sends store managers alerts when their store’s most in-demand products are underselling. Store managers take action to improve the product’s availability and appearance and restore lost sales. The tool also uses machine learning to understand store managers behavior, and help them maximize customer satisfaction within their environment.

Thanks to this and other AI technologies, the seemingly impossible task of personalization in brick-and-mortar settings is now a reality.


The Future of Fashion Retail

As shoppers are treated to more personalized experiences at every touchpoint, more demands are placed on retailers and their store managers. This is especially true for apparel sellers, who are expected to deliver relevant, trend-minded garments in the sizes and colors shoppers want. In the past, stakeholders in fashion organizations may have chaffed at the idea of supplanting their artistry with the assistance of, well, a robot. But today’s leading apparel retails know that the best AI offerings augment human workers without replacing them.

CB4 helps apparel retailers rise to the increasingly complex demands of brick-and-mortar shoppers by removing store execution problems that hurt sales most. Here’s how it works.

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