Computer Vision Can Enhance The In-Store Experience In 5 Critical Ways
By Rohan Sanil, Deep North
Over the last decade, traditional retailers have struggled to increase foot traffic and customer interaction in their physical storefronts. The internet and the advent of merchants like Amazon, along with the outbreak of the pandemic, have radically altered the retail scene.
However, now things are changing. In fact, the e-commerce industry is taking a hit, with consumers cutting back on online spending. Adding to that, total retail sales in the U.S., excluding vehicle purchases, increased 7.2 percent over the previous year, according to a study recently released by MasterCard SpendingPulse. According to the report, e-commerce transactions decreased by 1.8 percent, while in-store sales increased by 10%.
With shoppers returning to brick-and-mortar stores, how can retailers effectively compete not only with their online counterparts but also with other brick-and-mortar businesses? Delivering personalized service, convenience, and other engagement characteristics to encourage sales and loyalty is a requirement.
But, unlike their internet competitors, physical retailers lack clear visibility into consumers' browsing and shopping activities. That might entail anything from how long a customer has to wait in line before being able to purchase to a shopper's in-store path-to-purchase.
To address this, retailers can use computer vision to more effectively utilize their in-store settings, allowing them to better satisfy customers' demands and run their stores more efficiently. In a nutshell, computer vision is a branch of artificial intelligence that focuses on simulating the strong capabilities of human vision. It teaches computers to perceive and grasp the visual environment in the same manner that people do.
Computer vision combined with existing store camera technology can help merchants learn who their customers are and how they act while maintaining customer privacy. The following are five applications for computer vision:
- Checkout queue management and fraud prevention. The number of individuals in line for checkout and the average time spent in line before reaching checkout can both be determined using computer vision. It also can help with self-checkout and checkout shrinkage.
- Analysis of footfall. Retailers can use computer vision to calculate metrics such as 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 any given time.
- Understanding of customers' trips. Retailers can use this technology to comprehend heatmaps and the number of entrances into a zone. The functionality also can determine how much time was spent in the store and how much time was spent in each store zone.
- Demographics of customers and return visitors. Customers' age range, gender, and the number of people that visited the store more than once in a single day can all be determined using computer vision.
- Analytics in-store. This technology allows merchants to track shelf engagement, including the number of touch motions directed at items on shelves. It also provides information on POS transaction time and conversions. In addition, computer vision may give shops information on the most common customer path, such as zone-to-zone traffic patterns from shopper entry to leave.
Retailers may utilize computer vision to acquire real-time insights on crucial variables including in-store (and back-of-house) operations, labor distribution, and, most importantly, overall consumer experiences. They can then use these understandings to improve product merchandising and marketing, plan and allocate appropriate staffing, and much more, resulting in increased in-store conversions, delighted customers, and significant cost savings.
Computer vision helps to level the playing field between in-store and internet shopping. Can your company afford to miss out on insights that were previously only available in the e-commerce world?
About The Author
Rohan Sanil is CEO and Co-founder of Deep North.