As part of Solutions Review’s Premium Content Series—a collection of contributed columns written by industry experts in maturing software categories—Rohan Sanil, the CEO and Co-Founder of Deep North, shares insights into the ways AI and computer vision can help retail businesses.
The past ten years have seen traditional retailers struggle to increase customer engagement and foot traffic in their physical locations. The Internet, the emergence of online merchants like Amazon, and the spread of the epidemic have dramatically altered the retail environment. So how can stores compete with their online counterparts—and other brick-and-mortar businesses—now that customers are returning to stores? Delivering individualized service, convenience, and other engagement characteristics is necessary to encourage purchases and loyalty.
The issue is that, unlike online rivals, physical retailers severely lack a clear view of their customers’ browsing and purchasing habits. That could refer to how long a customer must wait in line before purchasing the in-store path-to-purchase. Technology can empower retailers to gain data-rich insights once only available online to use their in-store environments better to meet shoppers’ needs and run their stores more efficiently and effectively. These include artificial intelligence (AI) and computer vision.
Sensor Analytics Versus AI and Computer Vision
First, it’s essential to understand the difference between sensor analytics—commonly used today in the retail environment—and computer vision. Sensor analytics is based on the use of physical sensor devices in-store. These work by converting stimuli such as movement or sound into electrical signals, which are converted to code and then processed by computers.
Computer vision is a field of AI that focuses on replicating the capacities of human vision. Computer vision takes sensor analytics a step further, as anything one’s eye can see can be analyzed. It trains computers to interpret and understand the visual world as humans do. For example, sensors may determine whether a person has walked into a store and an aisle and even identify spills that could negatively affect the shopping experience.
The Use of AI and Computer Vision In-Store
Physical retailers can use AI and computer vision with their on-site store camera infrastructure to understand their customers and how they behave while ensuring customer privacy. With these technologies, retailers can obtain real-time insights for decision-making so they can positively affect key metrics like in-store (and back-of-house) operations, labor planning and allocation, and overall consumer experiences. Retailers can do this through more effective product merchandising and marketing, staff optimization, and other efforts to drive in-store conversions, happy customers, and sizable cost savings.
Specifically, the following are six applications of AI and computer vision in retail and how they can be used to impact the in-store environment positively:
With these technologies, retailers can determine metrics around the number of people walking into and out of a store and the total number of people in a store.
Customer Demographics and Repeat Visitors
AI and computer vision can enable retailers to determine characteristics like customer age range, gender, and the people who made more than one trip to the store on a single day.
With this functionality, retailers can understand heatmaps/the number of entrances into a zone. They can also determine the length of time spent in-store and the time spent in a specific store zone.
AI and computer vision clarify the number of people waiting in line for checkout and the average wait time spent in a queue before reaching the checkout.
Fraud and Loss Prevention
Retailers can use computer vision and AI to combat retail shrink with better loss prevention solutions at the front of the store. Fraud at checkout, including at staffed registers and self-checkouts, necessitates the integration of data from item-level tracking with computer vision and POS. Comparing item-level counts to POS-generated counts can help associates discover fraud and act in real-time. AI and computer vision can help retailers enhance checkout processes by making them more intelligent, which reduces theft and improves inventory control.
The technologies allow retailers to understand shelf engagement, including the number of touch gestures made towards shelved items. It also delivers POS transaction time and conversion details. In addition, they can provide retailers with insight into the dominant customer path, including zone-to-zone traffic patterns from shopper entry to exit.
AI and Computer Vision Beyond Retail
In addition to traditional retail, computer vision and AI can positively impact industries like quick service restaurants, shopping centers, transportation, commercial real estate, manufacturing, and warehouses. These technologies can help businesses address various challenges. These include limited visibility into in-store and back-of-house operations, lack of insight into customer behavior and journey, inadequate labor planning and allocation data, and lack of real-time insights for decision-making to make processes more efficient and effective and drive positive customer experiences.
With the complete visibility and real-time insights into consumers’ journeys that are delivered by AI and computer vision, including how they interact with staff, goods, and services, physical retailers are empowered to create better and more meaningful customer experiences. These, in turn, positively affect loyalty and revenues and reduce costs. AI and computer vision are the missing pieces for brick-and-mortar stores to reap many benefits previously only available online.
- Applications of AI and Computer Vision in Retail (and Beyond) - August 26, 2022