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Cameras are everywhere in shops, department stores, and other retail locations—offering retailers a potential gold mine of visual data to use in AI applications. By feeding camera data into computer vision-powered analytics systems, retail businesses can optimize operations, gain deeper insights into their customers, and make better strategic decisions.

“The use cases in retail are truly extensive,” says Pranita Palekar, CEO and Co-Founder at Aurify, an AI and video analytics systems specialist for retail and other sectors. “Computer vision can be used to create detailed profiles of customers, analyze in-store shopper behavior, manage employees, monitor shelves, prevent loss, and support smart digital signage.”

Of course, it can be challenging to analyze and operationalize massive amounts of raw video data, especially if real-time processing or extensive, multi-location deployments are required. But powerful edge devices and mature AI model deployment tools make it possible to get computer vision solutions into stores—and innovative retailers already take advantage of the opportunity.

In-Store Video Analytics Deliver Business Outcomes

Take, for example, two Aurify retail business implementations in India.

In one deployment, a leading shop-in-shop retailer with 150+ locations wanted to gain greater insight into its customers to improve marketing and sales efforts. Leadership believed that current business processes were inaccurate and inefficient due to reliance on staff members manually counting and observing shoppers—but they worried that a high-tech alternative might be cost-prohibitive.

In a second implementation, a nationwide fashion chain faced similar challenges on an even larger scale. The management team was concerned that it lacked centralized visibility into its network of 700+ stores, resulting in an inability to make timely, data-driven decisions about operational and sales strategies.

Aurify developed customized solutions for both companies based on its StoreScript AI video analytics platform for retail. Existing CCTV infrastructure was used to collect data, which was then analyzed to provide a clear picture of customer demographics and real-time foot traffic in stores. At the fashion retailer, point-of-sale (POS) monitoring and queue management were included to help streamline operations, manage inventory, and gain additional insights into customer buying behaviors. The newly available data led to a major shift in merchandising strategy, resulting in significant sales growth.

For both businesses, the result was a completely automated video analytics system that eliminated cumbersome manual processes and delivered the desired insights—all with minimal capital expenditure.

Other customers also benefited by implementing use cases such as: calculating dwell time compared to conversions, group counting, heat map generation, operational hours auto-tracking, and tracking the number of customers in the store at any given time.

In the cost-sensitive #retail sector, decision-makers are always on the lookout for solutions that can be implemented quickly and efficiently. @AurifySystems via @insightdottech

Flexible Tech Stack Means Retrofits, Not Rip-and-Replace

In the cost-sensitive retail sector, decision-makers are always on the lookout for solutions that can be implemented quickly and efficiently. For this reason, a flexible computing platform is key. The Aurify StoreScript solution is camera brand agnostic and can also use video data from different camera types like HD or IP. In this regard, the company’s technology partnership with Intel has been crucial.

“Our AI video analytics solutions are based on Intel processors, which deliver excellent performance and stability for computer vision at the edge workloads,” says Rishi Palekar, Managing Director and Co-Founder. “This allows us to use raw camera data from existing CCTV sources, minimizing hardware costs for our customers and speeding deployment.”

Aurify uses the Intel^® OpenVINO^™ toolkit extensively to optimize, customize, and deploy deep learning models from the edge to the cloud. This enables StoreScript to be adapted to multiple use cases—and support on-premises, cloud, or hybrid deployment models.

The upshot is that retailers can implement and customize an in-store AI analytics solution to suit their unique needs—without having to make massive investments in new infrastructure.

Beyond Retail: AI Video Analytics in Diverse Sectors

The flexibility of retail AI platforms will no doubt make them attractive to numerous retail businesses. But it also means that these solutions can be adapted to other sectors as well.

Aurify is already developing video analytics solutions for a wide variety of industries. In manufacturing, they can be used to amplify video data from cameras trained on industrial equipment to detect abnormal vibrations and enable predictive maintenance. Building and construction firms can use AI video analytics to perform automated quality control, ensure that workers follow proper safety procedures, and detect hazards and accidents in real time. And smart cities can use computer vision at the edge for traffic management, public safety, and critical infrastructure monitoring.

“Advances in AI, on both the software and the processing side, make it possible for all kinds of industries to deploy robust, scalable computer vision solutions,” says Palekar. “This will unlock tangible benefits for enterprises in many sectors in the form of increased growth, reduced spending, and greater profitability.”

This article was edited by Georganne Benesch, Editorial Director for insight.tech.