Loss Prevention and Beyond
The power of AI-powered video analytics to prevent retail shrinkage is clear. AI-enabled cameras can keep an eye on an entire store, alerting personnel to issues in real time.
Computer vision can help retailers pay special attention to areas where loss may be more likely to occur. For example, CDW’s Andrew Cadwell, vice president of strategic enterprise, said AI can help retailers spot issues when customers are making returns. “Perhaps a customer at the return desk is fidgety or looking suspicious. A manager can be called, and they can take over that transaction,” he said.
In addition to preventing inventory shrinkage, computer-vision solutions provide valuable data about shoppers that helps stores operate better. For example, effective analysis of this data enables them to enhance product placements and schedule workers more efficiently.
“How shoppers behave, what they’re looking at, what they’re buying,” Falcão said. “All of this helps retailers get a better understanding of their business.”
Keeping an Eye on Checkout
Checkout is another area where computer vision can provide a major boost for retailers. Real-time data analytics can help keep customer purchases moving, whether it’s at cashier-assisted checkout, express checkout, self-checkout, or even at stores where camera systems eliminate the need for checkout.
For example, self-checkout has become a widely adopted way for stores to reduce costs while improving the customer experience. However, issues during self-checkout can have the opposite effect, leading to shrinkage — either intentional or unintentional — and detracting from the customer experience.
Computer vision systems can help stores better manage self-checkout areas. “How can we reduce loss at checkout without adding friction?” said Joshua Mora, chief product officer at RocketBoots. “We need to understand what’s happening and enable store associates to act on that information.”
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Infrastructure Needs
Retailers considering computer vision as a way to enhance their loss prevention efforts should understand the infrastructure needs for these solutions. High-definition cameras are an obvious requirement, but many stores are able to use the surveillance hardware they have in place to feed computer vision systems the necessary data. Additionally, a robust network is required to support the data flowing from cameras to data analytics tools and then to users.
In many cases, edge servers can provide performance benefits for these systems. “One of the biggest challenges of computer vision is the data,” Falcao said. “Having a server onsite is key.” Further, keeping data onsite may help stores avoid compliance and privacy concerns.
Ultimately, computer vision holds the potential to offer a variety of improvements for retailers. A clearer understanding of what’s going on in a store not only prevent losses but also helps stores run more efficiently and serve customers better. “What we’re doing is transforming video into operational improvement,” Mora said.
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