Consumer expectations for speed and high-quality service keep rising—so what can retailers do to maintain a competitive edge? The next retail paradigm shift will see offline and online shopping converge into a single, seamless channel. Retailers of all sizes need to be one step ahead of their customers’ needs and their competitors’ next innovation, requiring a digital transformation powered by artificial intelligence.
Below, we break down some of the key trends retailers can act on in the AI space and narrate a scenario that highlights the process of building comprehensive, informed shopper profiles with AI.
Personalize the storefront for every customer.
AI-powered retail spaces recognize shoppers and adapt in-store product displays, pricing and service through biometric recognition to reflect customer’ profiles, loyalty accounts or unlocked rewards and promotions—creating a custom shopping experience for each visitor, at scale.
Guide discovery based on the shopper’s needs and preferences.
As customers to look to build confidence in a purchase decision, automated assistants can help narrow down the selection by recommending products based on shoppers’ needs, preferences and fit.
Capture emotional responses and act accordingly.
By recognizing and interpreting facial, biometric and audial cues, AI interfaces can identity shoppers’ in-the-moment emotions, reactions or mindset and deliver appropriate products, recommendations or support—ensuring that a retail engagement doesn’t miss its mark.
Extend dynamic consumer outreach based on real-time information.
Advanced CRM and marketing systems learn a consumer’s behaviors and preferences through repeated interactions to develop a detailed shopper profile and utilize this information to deliver proactive and personalized outbound marketing—tailored recommendations, rewards or content.
Respond to customer feedback with R&D.
Deep learning algorithms collect and interpret customer feedback and sentiment, as well as purchasing data, to support generation-generation product and service designs that better satisfy customer preference or fulfill unmet needs in the marketplace.
Here’s a possible scenario: Anton is a recent college graduate who just accepted a job at a PR agency and needs to purchase a wardrobe appropriate for his new workplace.
- Anton goes to his favorite men’s clothing store, where his mobile device is detected by the store’s operating system and he is ‘logged in’ as a premium member.
- Using his store app, Anton requests assistance shopping for professional clothes. Store associates are alerted to Anton’s request and begin to stock a fitting room with products that meet his criteria, using an AI styling assistant tool to provide recommendations based on Anton’s profile and previous purchases.
- Anton brings some of his favorite pieces into the fitting room to try. In-store shopping assistants input into their app Anton’s reactions to his clothes. AI software can guide further product suggestions and fine-tune offerings.
- Anton tries on a blazer and likes the color and style, but wants a more fitted size. An employee orders a custom blazer for Anton, to be shipped directly to his home. Anton happily purchases the blazer and two other items in store.
- Anton’s reaction to the blazer fit, along with the garments he rejected, are catalogued and help inform the retailer’s next season of clothing.
- The retailer’s CRM algorithm sends Anton coupons as he becomes assimilated into his new role. One year later, Anton receives a promotion suggesting new shirt styles to help him spruce up his wardrobe for his annual review.
As Anton’s example conveys, artificial intelligence translates personalized convenience of online shopping to physical retail, instantaneously refining product recommendations and rewarding customer loyalty. AI supports in-store associates by putting at hand all the information they need for each individual customer. The result is a comprehensive view of shopper behavior for retailers and a seamless experience for customers, regardless of the retail channel.