Given that many retail operations require visual feedback and generate large amounts of data, the growing interest in computer vision among retail companies shouldn’t be surprising. According to the 29th Annual Retail Technology Study by RIS, only 3% of retailers have already implemented computer vision technology, with 40% planning to implement it within the next two years.
Computer vision is posed to tackle many retail store pain points and can potentially transform both customer and employee experiences. For example, customer journeys can be redefined by making store layout improvement decisions based on real data rather than intuition.
With machine learning consulting and a holistic approach to computer vision implementation, digital retail transformation becomes much more attainable. Let’s look at the top five applications of computer vision in retail.
Self-Checkout
Self-checkout has already solidified its importance for brick-and-mortar stores. Since customer service automation is becoming a priority, companies need to update their processes to make them more efficient.
In most stores, self-checkout implies that customers manually scan barcodes of individual items, which is a notably daunting experience. Computer vision-powered cameras can recognize products without barcode scanning, significantly improving customer experience and security while also speeding up the checkout process overall.
Inventory Management
Computer vision has also found its way into retail inventory management. According to RIS’s 30th Annual Retail Technology Study, 64% of retailers are looking to deploy various data-driven solutions, computer vision included, to optimize inventory in the next two years.
For example, Shelfie uses computer vision cameras mounted on standard retail shelves to alert staff about incorrectly placed products and gaps on shelves. This frees up shopping floor staff to shift their focus to customer service more. At the same time, fully-stocked shelves dramatically increase consumers’ propensity to purchase. Moreover, with real-time data analytics, retail stores can also use Shelfie to dynamically reposition products and foster data-driven decision-making on the spot.
Other companies take a somewhat different approach and build all-in-one inspection systems. For example, Tally, a mobile robot designed by Simbe Robotics, captures visual data from more than 12 high-resolution cameras. Besides notifying staff about out-of-stock products, Tally can also detect damaged packaging and inaccurate pricing.
Another innovative solution by TEMPO Process Automation’s VIMS software deploys computer vision through re-installed camera systems. Unlike other similar solutions, VIMS doesn’t require any infrastructure changes or investments in shelf cameras and robots. TEMPO assures that by reducing out-of-stock items by 20% and ensuring perfect product layout, retailers can return their investments in a matter of months.
Store Layout Improvement
By installing computer vision cameras, retailers can track customers’ movements across the store to identify their purchase patterns, ‘hot areas,’ and behavior in relation to certain products. With this information at hand, retailers can make informed decisions about product placement, store layout, and staffing.
For example, Samsung used computer vision and data analytics to optimize its pop-up store dedicated to the pre-launch of its flagship mobile phone Galaxy S9. When it comes to such short-term marketing campaigns, being able to optimize performance on the fly is critical. With a number of in-store computer vision cameras installed across the store, Samsung gathered footprint, dwell time, product interaction, and demographic data in different store zones.
The end goal of this marketing campaign was to convert each visitor to be a potential buyer and social advocate of the product by engaging them in different zones within the store. By gathering vast amounts of data, Samsung managed to identify what zones had the most impact on the conversion, when the store was understaffed, and what messaging was the most effective for attracting passers-by. Such insights enabled Samsung to adjust store layout in real-time, deriving maximum value from its marketing campaign.
In a similar example, a high-end Serbian fashion retailer, Legend World Wide, collaborated with Deloitte to build a ‘connected store.’ The company installed computer vision-equipped cameras and sensors across its physical store to track customer journeys, better understand how products move around the store and gain better product-related insights overall.
By analyzing customer movement heatmaps, Legend discovered that store layout was its biggest bottleneck. When entering the store, most men quickly skimmed through the clothes on the left, immediately realizing that they were browsing women’s clothes. This made most male customers turn around and leave the store. Legend solved this problem by simply installing clear navigation signs indicating that the men’s section is upstairs.
Similarly, in a grocery store environment, these ‘hot areas’ can be used to place promotional products and increase promotional sales.
Taking a holistic approach
Far too often, retail store owners are deterred from adopting new technologies owing to budgetary constraints. Technology marketers often provide a solution as an ‘all or nothing’ package that may not be needed for all retail fronts. However, investing in a niche software that is focused on solving a particular business problem can help alleviate financial pressure and kick-start your journey towards complete automation. Most computer vision software available in the market is reliant on complete automation requiring investments in peripheral hardware systems to work efficiently. VIMS, on the other hand, allows you to leverage your current infrastructure and build your vision-based automation system from there.
The software’s AI and machine learning capability offers immense flexibility and scalability where you begin by targeting a single business problem and grow by allowing the software to do much more.
