As the retail industry evolves and customer expectations are shaped by the capabilities of today’s online world, brick-and-mortar retailers must know how to offer a shopping experience that resonates with a shopper’s digital needs and expectations. With personalized content and service now a basic feature of social media, entertainment and e-commerce platforms, consumers have come to expect the same level of personalization in the offline world – in brick-and-mortar stores. According to The Harris Poll, over 37% of people will stop doing business with a brand that doesn’t offer a personalized experience.

To offer a personalized experience, retailers need to thoroughly understand their customers, products and stores as well as their relationship and context with one another. However, it is not always easy to identify and analyze shoppers’ behavioral differences or individual consumption preferences using surface observations. In fact, only 57% of shoppers reported feeling satisfied with their recent in-store experiences in Zebra’s 2020 Shopper Study (which concluded prior to the COVID-19 outbreak).

Thus, the reason why artificial intelligence (AI) and, specifically, machine learning technologies warrant closer attention right now. (As retail technologist, Shawn Harris, explained in this Ask the Expert blog post, AI is really an umbrella term that encompasses machine learning.)

COVID-19 has prompted many customers to start demanding a completely contactless, yet still personalized, self-serve shopping experience. They are increasingly reaching for personal shopping solutions (PSS) – mobile devices provided in the store by retailers – in order to quickly find, grab and pay for the items they need. They want to be able to get in and get out without any friction and without having to get too close to others or that checkout belt that gives many pause.

In other words, retailers need a way to adapt to changing online and offline shopping behaviors and offer products and services that provide value and authenticity to customers at the point of sale (POS). AI-driven customer engagement aims to guide shoppers on their path-to-purchase and helps them find the perfect match of product to assist, save time and inspire.

Turning Raw Data into Actionable Intelligence in Real Time

Every time a mobile device is used for customer service, inventory management, fulfillment and point of sale activities, it is actively capturing insights that could help retailers improve operational efficiency and drive sales. This includes information about shoppers’ behavioral differences, individual consumption preferences and how well the in-store environment currently accommodates both.

At the same time, these devices are distributing data back to users to help inform decisions and guide actions. In fact, mobile devices such as the Zebra PS20 Personal Shopping Solution may be the only direct communication channel between retailer and shoppers in the store. Therefore, the synergy of the physical (in-store) and digital (data-derived) experience has crucial impact on the real-time decisions and actions of scan-and-go shoppers.

Fortunately, machine learning models are uniquely able to analyze the ever-increasing data pool derived via mobile devices in the store to help you understand current circumstances, anticipate upcoming trends and subsequently take action to influence desired outcomes.

The machine learning algorithms used in Re-Vision’s Impact CX (Customer Experience) Service can, for instance, trigger highly personalized product promotions and product suggestions on handheld mobile self-scanning devices used by shoppers while in store. Designed for an offline (i.e. brick-and-mortar) environment, Impact CX uses real-time behavioral data and factors such as demographics, weather, store location, time of day, day of the week and more to show the right products to the right customer at the right time. This helps to improve the relevancy for the customer and can lead to a 5-10% increase in basket spend.

Furthermore, the self-learning capabilities of machine learning models continuously improve product recommendations based on users’ behavioral feedback and shopping patterns. This leads to a dynamic solution that responds to new and developing consumer trends with increasingly accurate recommendations to subsequently drive value and customer loyalty.

Besides providing a connected and enjoyable shopping experience in-store, machine learning technologies offer a variety of other opportunities to generate value in retail stores.

Security and loss have always been a point of concern when introducing self-service solutions, which is why we developed the Re-Vision Impact CTRL (Control) service. Retailers need a way to facilitate an intelligent theft prevention process for self-scanning solutions. Rather than manually configuring rules and processes to identify theft and errors in self-scanning shopping trips, Impact CTRL uses machine learning models to predict the probability of theft or errors of scan-and-go shoppers in real time. Individual theft probabilities are assigned to every scan-and-go user which, in turn, improves rescan accuracy and operational efficiency by reducing unnecessary checks, checkout time and loss from customer theft. This further strengthens the value proposition of self-scanning solutions, especially as customer utilization of PSS devices continues to increase in the coming months and years.

The Takeaway

Retailers who survive and thrive despite today’s challenging climate are those who have an omnichannel strategy—not merely from a marketing perspective, but a customer engagement perspective as well.

With the powerful synergy of self-scanning scanning devices and machine learning/AI, you now have a way to offer a highly personalized experience to customers in real time that was, up to now, only available to online players. The complementary relationship of scan-as-you-shop devices and the machine learning-based impact platform helps to innovate the in-store experience in a way that benefits both you and your shoppers, no matter how fast they may be moving through the aisles and how little other interaction they may have with store associates or displays.