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While the age of the internet began in the last millennium, the ecommerce boom is just reaching its peak. Online retail behemoths like Amazon are easing the shopping process by making products just one click away. Today’s shopping experience can take place anywhere, anytime, even while waiting for coffee or picking up the kids from soccer practice. The time for retailers to adapt is now.

In 2017, 7,000 retail businesses had to cease operations, and 3,800 retail stores have already announced plans to shut their doors in 2018. Without a new strategy in place, retailers risk becoming victims of the “retail apocalypse.”

To survive, retailers must have the right technology in place to derive the right insights. Retailers spend billions of dollars looking for insights, but without analyzing the full network of data, findings will remain fuzzy and incomplete. One way retailers can attempt to keep up with today’s ecommerce giants is to deploy AI, big data analysis, and other emerging technologies to improve customer experiences.

This tailored approach can help keep brick-and-mortar customers happy, while simultaneously facilitating the cost efficiency that drives margins.


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Here are a few ways some of the world’s largest retailers are employing AI to keep their brick-and-mortar stores afloat.

1. Tailored customer experiences

Appealing to shoppers’ unique needs is the most effective way to create and maintain customer loyalty. In fact, according to a recent survey, 70 percent of respondents said they would be more loyal to brands that integrated customization into their stores. With machine learning and transactional data at the center of their operations, retailers can track and analyze customer behavior, past purchases, and loyalty cards to glean insights and deliver tailor-made offerings. In fact, machine learning-based solutions can even recommend location-level assortments and predict demand by fulfillment path.

A prime example of this is what Sephora is doing with Color IQ, its exclusive machine learning-driven, in-store product that scans the surface of your skin to provide a personalized foundation and concealer shade recommendation. Since launching this technology in 2012, Sephora stores have generated 14 million Color IQ matches, and the company has created a spinoff, Lip IQ, for lipstick shades. By bringing in-store personalization to the next level, the company found a creative and successful way to increase foot traffic — and other retailers are starting to take note.

2. Reduced markdowns and out-of-stock items

With insights into store sales patterns, retailers can reduce safety stock and avoid the industry-standard approach of stocking equally across locations. Not to mention, by allowing automated machine learning to allocate or replenish inventory, retailers will no longer need to rely on seasonal, margin-draining markdowns.

Take, for example, fast-fashion behemoth H&M, which recently announced plans to adopt AI and big data capabilities to analyze store receipts and returns as a means of evaluating purchases per location — and ultimately to stock inventory based on these insights. By analyzing purchases in a more granular way, H&M determined that floral skirts in pastel colors sold much better than expected, and as such, stocked its shelves with more of these items and fewer of others.

Fast-fashion retailer Kiabi is also employing this strategy in Europe. The company uses machine learning to test and quickly replenish apparel and accessories in an automated fashion, improving the total profitability of its collection.

Both in the U.S. and abroad, this method will undoubtedly help reduce product excess, as stores stop ordering bulk items that go unsold, helping reduce product waste and saving on purchasing and shipping costs.

3. Specialized inventory

Machine learning-driven insights also prove helpful when making inventory purchase decisions. By using AI-driven technologies to analyze consumer shopping habits, retailers can determine exactly what products and how much inventory is needed to meet customers’ ever-evolving expectations. This process is key to competing with ecommerce players, which tend to have more readily available inventory, regardless of the shopper’s location.

Companies can also use smart technology to effectively predict what items will be hot and stock their shelves accordingly, before customers get frustrated that the trendiest item is out of stock at their nearest store. For example, as an early adopter of machine learning, Walgreens is using information technology to tailor inventory for anticipated flu outbreaks and to reduce overstocks by predicting which stores will best sell promotions.

Utilizing data and analytics to tailor inventory orders to customer behavior is necessary to surviving and thriving in the retail revolution. The reason: If a customer cannot find what they want when they are physically in the store, they will order online. Like any previous industrywide change, those that adapt early and remain agile will stay fresh and competitive. Science and data can help retailers move closer to maximizing their own potential, creating an authentic and personal relationship with their customers.

Corey Tollefson is the senior vice president and general manager at Infor Retail, a company that builds business applications with last-mile functionality and scientific insights for select industries delivered as a cloud service.

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