Although the Internet age started in the previous millennium, the e-commerce boom is now approaching its peak.
In fact, online retail giants such as Amazon are simplifying the shopping experience by making products to be just one click away.
As such, the shopping experience nowadays can occur at any place, and anytime. This situation raises the need for retailers to adapt.
In 2017, about 7,000 retail enterprises had to close their operations while 3,800 retail stores already have plans to close down their doors in 2018.
Failure to formulate and implement a new plan may increase the chances of retailers being swept away by the retail apocalypse.
One of the best ways that they can try to keep up with the current e-commerce giants is through the deployment of artificial intelligence (AI), big data analysis as well as other emerging technologies in a bid to boost customer experiences.
The following are some of the ways that the largest retailers in the world are applying AI to ensure that their brick-and-mortar stores remain afloat.
Customized Customer Experiences
The ideal way of creating and maintaining customer loyalty includes appealing to the unique needs of shoppers.
A recent survey showed that 70% of the respondents would be extra loyal to brands that incorporated customization into their stores.
With both transactional and machine learning data at the core of their operations, retailers cannot only track but also assess customer loyalty cards, past purchases and behavior in a bid to gather insights and deliver customized provisions.
What Sephora is currently doing with Color IQ is a perfect example of tailored customer experiences.
Color IQ entails a machine learning-powered, in-store product that examines your skin’s surface to offer a personalized concealer shade and foundation recommendation.
Since it was unveiled back in 2012, Sephora stores have racked in 14 million Color IQ matches.
The company has also created a spinoff known as Lip IQ, which is meant for lipstick shades.
Minimized Out-of-Stock Items and Markdowns
With an understanding of store sales patterns, retailers are not only in a position to minimize safety stock but also avert the industry-standard technique of stocking across all locations.
This can be done by enabling automated machine learning to assign or restock inventory. By doing so, retailers will not have to depend on margin-draining markdowns.
Kiabi, a fast-fashion retailer is leveraging this strategy.
In fact, it utilizes machine learning to evaluate and rapidly replenish accessories and apparel automatically, which helps to boost the overall profitability of the retailer’s collection.
When making purchase choices, machine learning-powered insights have also proven quite beneficial.
Through utilizing AI-powered technologies to assess consumer shopping behaviors, retailers cannot only identify what products but also how much stock is required to satisfy customers’ ever-emerging expectations.
This process is essential in competing with leading e-commerce players, who tend to possess more readily available stock irrespective of where a shopper is located.
Additionally, businesses can also take advantage of smart technology in effectively predicting the items that will be in high demand and thus, stock up their shelves accordingly.