Understanding the wave before it hits: Converging digital and physical CX with edge computing
The premier challenge in retail is painstakingly familiar in most data-driven industries. Organizations are struggling to optimize the customer experience in the digital realm with that in traditional physical locations, relying on progressive techniques in cognitive computing, the Internet of Things (IoT), and data science for strategic advantage.
But while most realize the impending digital disruption is as unavoidable as the notion that the physical shopping experience is here to stay, a profound development has occurred, resulting in a coalescence of these experiences so that one considerably improves the other—availing both customers and businesses.
Many of the more avant-garde use cases for edge computing, in which IoT data is processed and acted upon at the cloud’s edge, illustrate a trend in the way in which the personalization and individualization so prized in business are fostered from one realm to the next via:
♦ Facial recognition: Based on techniques such as deep learning and computer vision, facial recognition is a means by which the physical and digital worlds are consolidated.
♦ Augmented reality (AR): The cognitive visualizations of AR provide a natural correlation between the digital and physical experience by virtue of this technology’s capability of overlaying digital imagery in reality.
♦ Machine learning: In addition to powering many facial recognition systems, machine learning remains one of the most efficacious approaches for customer segmentation and personalized experiences in the digital and physical worlds. Machine learning enables organizations to analyze “all the data that happens in between clicks and touches,” ventured Ben Harris, CEO of Decibel. “That’s where you really learn about someone’s behavior and their experience.”
The application of these technologies to edge computing is indispensable for fusing the digital and physical realms for individualized customer experiences. Savvy retailers will monetize this convergence to empower both themselves and consumers by “collapsing those cycles and making it more convenient for the customer by putting more data in their hands,” acknowledged Robert Long, head of IoT and senior director of Applied Technology at Stibo Systems.
Monetizing geolocation data
Frequently, edge computing deployments are predicated on the timely application of geolocation data for real-time marketing opportunities. Nowhere are those opportunities quite as pervasive as in retail, in which any business with a digital footprint can attract customers via edge use cases such as digital billboards. Popular apps such as Waze, which Long described as “Google Maps with traffic highlights and re-routes,” are viable digital advertising platforms for abundant reasons. While consumers navigate their vehicles with such applications, retailers (and other businesses) can position location-specific advertising that also allows them to immediately interact and connect directly to the advertising customer, Long noted. The advantages of digital billboards include:
♦ Direct engagement: Customers can click directly on these advertisements to learn more about purchase opportunities: “Now, I don’t have to remember a website and do it later; I don’t have to remember a phone number and call them later,” Long said.
♦ Consumer responsiveness: By positioning these billboards on sites that consumers are already on, retailers can maximize consumer response.
♦ User experience: Advertisers can idealize customer interactions by not only engaging them online, but also by compelling them into stores with the aforementioned geolocation data.
Smart price tags
While digital billboards fulfill one of the promises of IoT initially ascribed to connected vehicles—targeted advertising based on geolocation data—other edge deployments are based on geographic data within physical spaces. Although the means of implementing this use case vary, smart price tags (in which items are marketed and discounted according to the individuals nearest in-store screens) represent a compelling edge use case throughout Europe and America. According to Long, “You can actually choose what to display on those screens, and they can all go back to a centralized controller. They can all be autonomous on the edge; they can all have their own intelligence.” Bethany Allee, marketing VP, Cybera, referenced uses cases in which digital display cases on connected refrigerators “talk to you while you’re looking at the different vitamin water options. It’ll talk to you about what’s inside or make recommendations.” The success of smart price tags depends on several factors, including:
♦ Purchase history: This hyper-geographic location data deployment requires rapid analytics of consumers’ previous buying history to market related goods or services when they near the screen.
♦ Sensor data: Several IoT use cases rely on sensor data. Many of them in the retail space are based on close up smartphones and “the proximity of your field sensor, Bluetooth, and Wi-Fi,” Long mentioned.
♦ Store apps: Specials based on individuals’ purchase history can also be marketed to them when they use retailers’ apps within their physical locations. AR can considerably enhance this capability for smart pricing and other retail needs. Cognitive analytics of user behavior on store apps “enables us to identify where the biggest opportunities are across a website or app for improvement,” Harris said.