Sam’s Club clearly feels the need for speed, especially in the checkout process. At the Sam’s Club Now in Dallas’ Lower Greenville neighborhood, the Arkansas-based division of Walmart is testing quicker checkout technology with a revamp to its popular Scan & Go service.
Sam’s Club, which has an innovation lab in the West End area of downtown Dallas, has filed a patent for the new technology.
Originally, Sam’s Club Now stores operated with a Scan & Go system that allowed shoppers to use the Sam’s Club Now app. Customers could scan barcodes with their smartphone, and then the phone was scanned when the customer walked out.
But, with the new technology—which will be tested this spring—machine learning and computer vision eliminates the need for a barcode scan. Instead, the product is identified through a photograph that automatically adds it to a customer’s digital shopping cart upon recognition.
In November, we originally told you about Sam’s Club Now, a “future of retail” concept that was being prototyped. The smaller-than-usual, mobile-first location in Lower Greenville was being called an “epicenter of innovation” for its tech being deployed. Sam’s club said Greenville was the ideal location as many local customers are both tech-savvy and time-pressed.
Sam’s Club Now takes on new testing role
According to Eddie Garcia, vice president of product and member experience operations, Sam’s Club Now will be a technology lab that doubles as a live, retail club. He said the big box retailer plans to incubate, test, and refine technologies in the store to help define the future of retail.
In a blog post on the company’s website, Garcia said in one instance, the new technology took 3.4 seconds to add a 40-pack of Member’s Mark water bottles to a cart. That compares to the roughly 9.3 seconds it takes to ID the water bottles via barcode.
“Since launching our Scan & Go two years ago, we’ve seen its popularity continue to increase,” Garcia said. “With the upcoming test, we’ll enhance the experience with the launch of a technology using computer vision and machine learning.”
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