Technology
Want more in-store sales, better customer experience? Try AI, ML on for size
David Wilkinson, president and GM at NCR Retail, explains how artificial intelligence, machine learning and automation are not only beneficial on the store floor, but can help keep technology running smoothly on the back end as well.
October 5, 2021 by David Wilkinson
The brick-and-mortar shopping experience rarely begins at the front door. Mobile device-wielding customers — even those who love the thrill of on-site shopping — often look up prices, inventory, physical locations and so forth before setting foot in a store.
When consumers use their smartphones, laptops, tablets or even watches to search for and purchase items using shopping apps, they leave behind a treasure trove of data. This enables retailers to create a more personalized, efficient customer experience. Yet far too many retailers fail to link the online and in-store shopping experiences to maximize this data to its full potential.
Here's where artificial intelligence and machine learning come in. AI and ML can provide that necessary connection and allow today's retailers to leverage the power of automation to provide the shopping experience today's consumers expect.
Let's explore how AI and ML can take your CX to the next level.
Linking systems, customer data and sales data
The modern store IT architecture focuses on bringing together all of the various systems that make a store (or chain of stores) tick — think inventory, payments, e-commerce and supply chain systems.
Virtualization makes this goal possible, linking multiple systems together to be simple and accessible to staff, reducing error and frustration and driving productivity.
But the benefits don't stop there. By linking these systems, data becomes centralized, and it becomes even easier to implement and reap the benefits of AI. AI takes mountains of customer and sales data and distills it down into patterns and behaviors that retailers can use to get a more comprehensive look at their customers and, more importantly, better understand their buying behaviors.
Further, when information is run through ML algorithms, these methods help applications learn what products or services to up sell by recommending complementary items or more personalized recommendations. Applications can let shoppers know what they need before they know themselves. For example, the app can issue a coupon for a favorite product, thus, enticing a customer into the store. Similarly, say a customer is checking out at a store and the cashier has the ability to see past purchases through a loyalty account. She can say something like, "I see you last purchased 12 ounces of Starbucks Dark Roast Sumatra ground coffee two weeks ago. Is it time for you to restock yet?"
In short, by linking disparate systems together and introducing AI and ML into the mix, all data can be evaluated quickly and efficiently together, giving retailers the ability to meld the online and in-store experiences and create a continuous CX, no matter the touchpoint.
Optimizing product layout
Technology can also help determine the placement of goods. When ML is paired with a retailer's sales data, it can discover buying preferences and pinpoint what items shoppers buy together. Based on this information, AI can suggest what items to put next to each other in the store to entice customers to buy more.
Skeptical because you already know, without data, that placing chocolate next to peanut butter increases sales of both? Or that pairing slacks with a sweater during colder months is a no brainer? Sure, there are obvious combinations that associates can identify based on their own buying behaviors. But AI takes it a step further.
An example of this is grocery chain Kroger, which is leveraging "smart shelves" to help shoppers find more of what they need. According to Deloitte, "sensors can identify customers who are using their mobile app in the store and will highlight products these customers might be interested in based on their purchase history." Examples include directing parents to kid-friendly snacks, those with gluten allergies to gluten-free foods, or sending an alert if an item on the mobile shopping list is marked down. The data and insights gained from the app can be used to refine specific store assortments and better plan store layouts based on established shopping patterns.
AI suggestions are based on solid data that comes from what customers tend to purchase in a particular store — not just intuition or past experience. Although intuition and past experience are important, AI can easily and automatically pinpoint how to best maximize store assortments and layouts to drive more customized experiences (and hopefully more revenue).
Driving recognition technology
Increasingly, retailers are using AI-enabled platforms that include facial recognition or gait recognition to help close the loop with a POS system.
For example, some supermarkets employ facial recognition technology at the POS to deter the purchase of age-restriction items. Self-checkout machines scan a shopper's face with a camera, estimating his or her age. If the consumer appears to be too young to purchase cigarettes, alcohol or vapes, for example, the AI-enabled technology will alert a store employee who can check the person's ID. This can be particularly beneficial if staff members are hesitant to ask shoppers for their IDs or to deter older shoppers from purchasing age-restricted goods for their younger pals.
By preventing underaged purchases, retailers avoid what could be hefty fines, penalties and worse. On the flip side, it also makes shopping easier for those of age and feeds into the ever-present need for frictionless checkout.
Through ML, video cameras and motion sensors, gait recognition systems use the way a human body moves to recognize it. They're particularly useful for video surveillance and can help companies shore up their security measures and prevent theft, particularly in crowded retail stores.
Identifying problems before they start
AI, ML and automation are not only beneficial on the store floor, but they also help keep technology running smoothly on the back end as well.
For instance, brands using automated managed services can proactively identify and solve potential IT mishaps, so store processes, employees and customers don't suffer the consequences. Have you ever entered a self-checkout lane only to find out the credit card reader is down without any signal that staff have been alerted? Or asked an associate to look up the location of an item for you but their systems aren't accurately reflecting what's on the shelves or shown online? These experiences are less than ideal for employees and customers alike and could harm customers' perception of your brand.
But automated processes can nip such challenges in the bud, allowing IT staff to be alerted of and address these problems as or before they arise, providing a more seamless shopping experience.
Conclusion
It's predicted that by 2024, e-commerce will rise to $7 trillion in annual sales activity — roughly 25% of all sales. Although online shopping continues taking a larger slice of the pie, three-quarters of sales will still take place inside stores' four walls.
Personalization at the store is no longer a nice-to-have for today's shoppers. It's a necessity. And it doesn't have to be a cumbersome process to execute. To attract more shoppers to brick-and-mortar stores and to improve the customer experience once they arrive, retailers will be able to better compete with the help of AI and ML.
After all, why sell harder when you can simply sell smarter?
David Wilkinson is president and GM at NCR Retail
About David Wilkinson
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