The Use of Computer Vision in Retail: Benefits and Application Scenarios
Modern retailers are increasingly turning to computer vision technologies to extract valuable data on customer behavior, optimize inventory management, and enhance customer experience. Let’s explore what computer vision is and the advantages it brings to business.
What is Computer Vision?
Computer vision is a branch of machine learning focused on the analysis of visual data, such as images and videos. It combines cameras, software, and artificial intelligence to give “machines” the ability to “see” and identify objects.
They use deep learning to “train” systems to analyze images. Once fully trained, computer vision models can recognize objects, detect and identify people, and even capture their movements.
Key Benefits of Computer Vision
As retail becomes more data-driven, the demand for computer vision technology is growing. Gartner research identifies computer vision as the only AI-based technology that is just months away from peak performance and ready for widespread adoption. Surprisingly, only 4% of retailers currently use computer vision in retail, despite the majority being highly interested in integrating it into their business operations, according to the 30th Annual Retail Technology Study by RIS.
When scaled, computer vision technology can provide retailers with significant competitive advantages and become an integral part of their digital transformation strategies.
Here are some of the key benefits of computer vision:
- Increased Efficiency: Computer vision automates repetitive tasks and enhances accuracy, improving efficiency across various industries.
- Automation: Computer vision can automate a wide range of tasks that previously required human intervention, such as quality control in manufacturing, inventory management, and security monitoring.
- Enhanced Accuracy: Computer vision algorithms analyze visual data with high precision, often surpassing human capabilities. This leads to more accurate and reliable results, improved product quality, reduced waste, and higher customer satisfaction.
- Cost Savings: Automation and increased efficiency can lead to significant cost savings in labor and operational expenses.
- Improved Security: Computer vision can be used for surveillance and safety purposes, detecting threats and preventing accidents.
- Better Customer Experience: Computer vision helps companies personalize their products and services based on customer preferences and behavior. This improves the quality of customer service and boosts loyalty.
- More Effective Decision-Making: Computer vision provides companies with real-time data and analytics, helping executives make more informed decisions. For example, retailers can use computer vision to track customer behavior and adjust their marketing strategies accordingly.
The Use of Computer Vision in Retail
Just 15 years ago, people were indispensable for all tasks related to visual perception. With the advent of computer vision, more processes can now be fully automated and thus improved. Delegating tasks to specialized algorithms often enhances productivity by optimizing processes, reducing errors, and lowering costs, freeing employees to focus on more critical tasks.
1. Seamless Shopping Experience
Both consumers and retailers strive for a smooth shopping experience, and computer vision serves as the technological foundation that makes this possible. Traditional checkout systems are often inconvenient, primarily due to long lines, manual scanning, and occasional human errors. Computer vision eliminates these bottlenecks, enabling cashier-less stores that redefine retail efficiency with greater predictability and accuracy.
For example, in the Amazon Go cashier-less store, computer vision is used alongside sensor fusion algorithms and deep learning to create a seamless shopping experience. Once a customer enters the store and scans their smartphone, they can freely take products off the shelves. Edge computing-enabled cameras with computer vision technology closely monitor these actions, adding items to a virtual cart as the shopping progresses. When exiting the store, the bill is automatically charged to the customer’s Amazon account, ensuring a hassle-free interaction without waiting in lines.
It’s not just industry giants reaping the benefits; smaller retailers are also embracing the opportunity. Computer vision companies offer similar technologies that can retrofit existing stores with surveillance cameras to facilitate automated checkout.
By removing the traditional challenges associated with shopping, computer vision is at the forefront of a new era in retail, prioritizing speed, accuracy, and convenience for customers.
2. Autonomous Inventory Management
Thanks to computer vision, retailers can now achieve automatic real-time tracking of every product on the shelves. The first point of contact is shelf monitoring. Using computer vision algorithms, intelligent cameras continuously scan the retail environment, instantly flagging out-of-stock or misplaced items.
Some retail giants employ shelf-scanning robots equipped with computer vision to move through aisles and detect gaps or inconsistencies. This real-time information enables immediate inventory replenishment, not only reducing lost sales but also increasing customer satisfaction by ensuring product availability.
In a 2021 pilot project at Schnuck Markets, store robots identified 14 times more out-of-stock items than manual scanning, reducing out-of-stock cases by 20-30%. Schnucks began testing its robot in 2017, with the machine scanning the store up to three times a day, collecting data on stock positioning, pricing accuracy, and promotional execution. This solution for product audits is fully autonomous, effectively eliminating common human errors with automated system rules. A similar robot was tested in Auchan stores in Portugal a few years ago.
When combined with predictive analytics, stores can analyze both historical and real-time data on customer behavior, seasonal trends, and external factors like weather. This technology allows retailers to forecast demand with remarkable precision, enabling smarter procurement strategies that minimize both surplus and shortages.
3. Category Management
Retailers design store layouts or planograms to achieve desired outcomes, such as increasing sales, improving category profitability, or enhancing the customer experience. Following these plans means ensuring that the right product is placed in the right spot with the right price and the right promotion.
Modern merchandising monitoring methods allow for display audits using stationary or dome cameras, as well as robotic technologies. An optimal and cost-effective solution is to use software like PowerPrice, which leverages computer vision (CV) technologies. Retailers and brand managers can take photos of store shelves with their smartphones, and these images are then analyzed and compared to pre-set planograms. This allows for timely adjustments: updating stock, rearranging products, or refreshing pricing. The system’s accuracy in identifying and classifying products exceeds 95%. For those interested in implementing PowerPrice, Napoleon IT experts can provide assistance.
4. Store Operations Optimization
Computer vision technology is well-suited for various retail operations, significantly reducing the need for a large human workforce. While inventory management focuses on supply chain integrity, broader store operations also benefit immensely from computer vision.
One major aspect is optimizing the customer journey within the physical space, from entry to exit. Heatmap analytics in retail use computer vision cameras to track customer movements, identify high-traffic areas, dead zones, and bottlenecks. Retailers like Tesco use heatmaps to adjust store layouts. For instance, if a promotional display isn’t attracting the expected attention, real-time heatmap data can suggest an immediate fix, allowing adjustments to signage or product placement.
Another application is queue management. Computer vision cameras can predict queue lengths and wait times, enabling stores to allocate staff more efficiently. Some retail stores and quick-service restaurants use this technology to assess queue lengths and dynamically adjust staffing at checkout points.
Beyond inventory management, task automation for staff is another advantage. Robots equipped with computer vision can perform various store servicing tasks such as cleaning floors, checking temperatures in refrigerated sections, or even verifying that store signage matches promotional offers. These use cases are part of the broader Internet of Things (IoT) ecosystem, where sensor networks generate data in diverse environments.
Machine learning algorithms tied to computer vision data can also provide additional insights into customer behavior. Stores have begun using these systems to analyze and manage store density.
5. Anti-Fraud Systems and Security
Traditional retail stores frequently face theft and fraud, costing billions annually. Here, machine learning adds a powerful layer of security that extends beyond the capabilities of conventional surveillance systems. Machine learning technology not only monitors but also analyzes and predicts behavior, making it a valuable asset in loss prevention strategies.
For example, Walmart uses computer vision cameras to detect potential shoplifting. These intelligent systems can recognize suspicious behavior patterns, such as lingering in aisles without making a purchase or visiting blind spots within the store. The technology also flags unscanned items at self-checkout points, providing an additional layer of accountability and theft prevention.
Remarkably, computer vision systems continually improve themselves. They learn from each interaction, refining their accuracy over time. This iterative nature not only makes the technology more reliable but also minimizes false positives, reducing the likelihood of wrongly accusing innocent customers.
6. Virtual Fitting Rooms with Augmented Reality
Computer vision combined with augmented reality (AR) creates virtual fitting rooms. This synergy is particularly valuable in sectors like fashion and cosmetics, where aesthetics are key. Both online and brick-and-mortar retailers use these technologies to reduce uncertainty in consumers’ decision-making process while providing a more engaging shopping experience.
For example, eyewear retailer Warby Parker uses computer vision and AR to display a user’s face and virtually overlay different glasses frames.
In cosmetics, L’Oréal uses AR-enabled smart mirrors or smartphone apps that allow customers to “apply” makeup virtually. The software uses computer vision algorithms to accurately identify facial features, providing realistic results. L’Oréal’s AR makeup experience has shown promising results, doubling website engagement and tripling conversion rates.
Similarly, smart fitting rooms equipped with computer vision can recognize items and display available sizes, colors, or even suggest complementary products using AI-based fashion recommendation systems, effectively serving as personal stylists.
Beyond individual use, these technologies have a significant impact on reducing returns, a long-standing issue in the retail sector. By improving the accuracy of online purchases, computer vision and AR effectively lower the likelihood of product mismatches, thereby reducing return rates and associated costs.
