5 Ways Computer Vision Can Transform the In-Store Experience
It’s well known that traditional retailers have struggled to drive foot traffic and customer engagement in brick-and-mortar stores over the past decade. The internet and rise of retailers like Amazon.com — coupled obviously with the onset of the pandemic — have changed the retail landscape in unprecedented ways.
But with consumers now venturing back in-store, how can retailers effectively compete with their online counterparts and other brick-and-mortar businesses? A clear imperative is to deliver personalized service, convenience and other engagement factors to drive purchases and loyalty.
However, what physical stores critically lack, unlike their online competitors, is clear visibility into consumers’ browsing and shopping activities. That could mean something as simple as how long a customer waits in line before being able to purchase all the way to the in-store path-to-purchase for a shopper.
Computer vision is one approach that retailers can employ to optimize their in-store environments so they can best meet shoppers’ needs and run their stores more efficiently and effectively. Computer vision is, in brief, a field of artificial intelligence that focuses on replicating powerful capacities of human vision. In its simplest terms, it trains computers to interpret and understand the visual world the same way humans do.
Brick-and-mortar retailers can use computer vision in combination with their existing store camera infrastructure to understand who their customers are and how they behave, while ensuring customer privacy. Following are five ways computer vision can be leveraged:
- Footfall analytics: With computer vision, retailers are able to determine metrics like the number of people walking into a store, the number of people walking out of a store, and the total number of people in a store at a moment in time.
- Customer demographics and repeat visitors: This technology 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.
- Customer journey: 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 as well as length of time spent in a specific store zone.
- Queue management and fraud prevention at checkout: Computer vision provides clarity on the number of people waiting in line for checkout and the average wait time spent in queue before reaching checkout. It can also reduce shrinkage at checkout and self-checkout.
- In-store analytics: This technology provides the ability for retailers to understand shelf engagement, including the number of touch gestures made towards shelved items. It also delivers point-of-sale transaction time and conversion details. In addition, computer vision can provide retailers with insight into the dominant customer path, including zone-to-zone traffic patterns from shopper entry to exit.
With computer vision, retailers gain real-time insights for decision making so they can positively impact key metrics like in-store (and back-of-house) operations, labor planning and allocation and, critically, overall consumer experiences. Retailers can do this through more effective product merchandising and marketing, staff optimization and much more — driving in-store conversions, satisfied customers, and significant cost savings.
Computer vision helps level the playing field between brick-and-mortar and online retail. With the opportunity to gain insights once only available in the e-commerce world, can your business afford to miss out?
Rohan Sanil is the CEO and co-founder of Deep North, a computer software company that enables enterprises to understand consumer behavior in physical environments using deep learning and AI-based video analytics.
Rohan Sanil is CEO and Co-Founder of Deep North. He has over two decades of product, business, entrepreneurial leadership in the video analytics space. He previously founded Akiira Media Systems, Atstream Networks and Tri-Cad, where he was instrumental in product development, business development, and raising capital. Rohan holds an M.S. Degree in Management Science from the University of Dayton, Ohio, and a B.S in Mechanical Engineering from Karnataka University, India.