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. The time for retailers to adapt is now.
To survive, retailers must have the right technology in place to derive the right insights. Retailers spend billions looking for insights, but without analysing 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.
Here are a few ways some of the world’s largest retailers are employing AI to keep their brick-and-mortar stores afloat.
Tailored Customer Experiences
Appealing to shoppers’ unique needs is the most effective way to create and maintain customer loyalty. According to a recent survey, 70 percent of respondents said they would be more loyal to brands that integrated customisation into their stores. With machine learning and transactional data at the center of their operations, retailers can track and analyse customer behaviour, past purchases, and loyalty cards to glean insights and deliver tailor-made offerings. Machine learning-based solutions can even recommend location-level assortments and predict demand by fulfillment path.
Sephora uses Color IQ – its exclusive machine learning-driven, in-store product – to scan the surface of customers’ skin and provide a personalised 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.
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. By allowing automated machine learning to allocate or replenish inventory, retailers will no longer need to rely on seasonal, margin-draining markdowns.
Fast-fashion behemoth H&M recently announced plans to adopt AI and big data capabilities to analyse store receipts and returns as a means of evaluating purchases per location, and stock inventory based on these insights. By analysing 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.
Machine learning-driven insights also prove helpful when making inventory purchase decisions. By using AI-driven technologies to analyse 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.
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. Utilising data and analytics to tailor inventory orders to customer behaviour is necessary to surviving and thriving in the retail revolution.
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