Computer Vision in Retail
Part 2: Welcome to the store
Computer Vision (CV) is having a profound effect on almost every major industry, including retails. Retailers are finding that computer vision solutions are crucial components of the seamlessly blended digital-physical store experience that customers have been seeking. In particular, computer vision is helping retailers evolve the brick-and-mortar store environment to a more interactive, experiential one. In our previous part, we have known that with new types of retail store, customers are being offered wonderful experiences that they wouldn't receive when shopping online. CV is capable of making shopping less transactional, giving more context to products, and what else? Let's find the answer in our today's article.
Computer Vision-based Inventory Management
Two of America’s largest retailers are using robots as part of their inventory management. Over the summer of 2016, Lowe’s introduced its LoweBot in 11 stores throughout the San Francisco Bay Area. These autonomous retail robots can create real-time data by using computer vision and machine learning to scan inventory and look for patterns in product or price discrepancies. According to initial feedback from Kyle Nel, vice president of disruptive innovation and executive director of Lowe’s Innovation Labs, customers appreciate the convenience and efficiency of the bot, while employees love that LoweBot allows them more time to consult with customers on creative projects.
Several competing companies, including Shelfie Robot, have built computer vision powered solutions that notice when items are running low on the shelf and sends notifications to employees with exact item counts for restocking. This results in the reduction of restocking overhead while also driving revenues through high product availability. Another cool example is Simbe robot, which roams stores to collect data on stock, prices and visual placements of products. These robotic assistants are set to enhance the quality of shop stock management and visual merchandising in stores of the future.
With the help of computer visions inventory technology, stores can become smaller. Autonomous inventory can use optical recognition to bring shoppers the items they need. As inventory takes up less space, retailers are cutting down on square footage in exchange for a more intimate experience. Macy’s, Target, Warby Parker are all examples of retailers working on small-scale store concepts.
Enhancing store security
In retail security specific to groceries, Massachusetts-based StopLift claims to have developed a computer-vision system that could reduce theft and other losses at store chains. The company’s product, called ScanItAll, is a system that detects checkout errors or cashiers who avoid scanning, also called “sweethearting.” ScanItAll’s computer vision technology works with the grocery store’s existing ceiling-installed video cameras and point-of-sale (POS) systems. Through the camera, the software “watches” the cashier scan all products at the checkout counter. Any product that is not scanned at the POS is labeled as a “loss” by the software. After being notified of the loss, the company says it is up to management to take the next step to accost the staff and take measures to prevent similar incidents from happening in the future.
Using algorithms, Stoplift claims that ScanItAll can identify sweethearting behaviors such as covering the barcode, stacking items on top of one another, skipping the scanner and directly bagging the merchandise.
Shops can also use computer vision to alert managers when a known thief has entered the store. Of course, customer data privacy, opt-in rules, and legal implications could become an issue if such a system were implemented. It’s important to consider the ethics of using AI in such cases and how we address machine bias when identifying risks.
Reward Loyalty & Customising Experiences Using Facial Recognition
Computer vision is taking loyalty programming to the next level with facial recognition of regular customers. Lolli & Pops, a candy store based in the US with roughly 50 doors, is one such retailer experimenting with this. A proof of concept called Mobica, which is powered by Intel, was on show at National Retail Federation Big Show in New York this week. Using computer vision, it’s a facial recognition loyalty scheme designed to drive VIP consumer engagement.
The opt-in experience (shoppers literally have to register with their facial features to be a part of it), means anyone entering the store is recognized in real-time by an app the sales associates are using on their tablet devices. From there, they are able to tell the individual’s taste profile, for instance if they’re allergic to peanuts, and be able to personally recommend great products to them via AI-enhanced analytics accordingly. In the future, such loyalty programs will even remember your usual order at most cafes and restaurants.
Facial recognition of customers or using phones as beacons is the biggest trend influencing the store of the future. Nike’s experimental store in New York relies heavily on your phone’s Nike app to make the physical and digital seamless. Shoppers can book appointments with consultants, scan mannequins for product info, and have the items delivered to the dressing room through the app.
Improving Store Layouts & Customer Flow
Nordstrom’s new Local concept uses a similar consultation-based, highly customised approach to Nike. However, Nordstrom Local stores are very small and accommodate no inventory on-site. Instead, you visit the store, speak with a consultant about what you’re looking for, and the consultant orders the items for you to try on at a fitting appointment. You can also shop online and pickup in-store.
Even if stores don’t get rid of inventory entirely like Nordstrom, computer vision can help identify common paths and bottlenecks through the store. Aurora by RetailNext is one such technology using computer vision for layout optimization. Watching how shoppers normally move can help make layouts more efficient, decrease wait times, and make sure shoppers see certain promotions.
Reference: Logikk, Emerj, Chain Store Age, The current daily, Wikipedia