Computer Vision in Retail: Benefits & 10 Popular Use Cases

Should You Use Computer Vision for Your Retail Store?

There’s a fact that computer vision has received much criticism about its accuracy and ethical issues in recent years. Many people doubt that the introduction of this technology to retail stores would increase dependency, leading to significant layoffs and a loss of human ability to handle operational problem-solving.

In some cases, CV can collect visual data from customers for personalized advertising or from in-store staff for employee monitoring. This can easily result in privacy violations or security risks if your store uses unapproved CV systems or implements weak data protection practices in place.

This raises a big question: is investing in computer vision worth those problems? The answer is yes. Despite the existing drawbacks, computer vision still brings transformative benefits to your retail store if harnessed properly. Here’s how it can make a big difference:

Increase Efficiency & Minimize Costs

Computer vision technology makes your retail operations more effective. It supports automated inventory management by inspecting stock levels and reordering to ensure shelves are always full.

Besides, CV systems help reduce shrinkage. In the retail industry, shrinkage means the loss of inventory that’s not sold for some reasons like misplaced items, theft, or incorrect pricing labels. CV systems can inspect on-shelf products in real-time through visual data analytics to limit this issue. This will decrease manual tasks, giving in-store staff more time to focus on customers and avoiding shrinkage-incurred costs.

Additionally, computer vision can analyze customer traffic patterns and determine peak shopping times. This helps you schedule employees effectively to avoid overstaffing (too many employees) or understaffing (too few employees) in the store. As a result, you can save labor costs while ensuring service quality.

Boost Customer Experience

Computer vision enhances the shopping experience in many ways. First, your store can offer self-checkout systems and smart carts to automatically scan items for billing. This helps your customers avoid long queues.

Second, computer vision techniques can analyze customer movements through a retail heat map to point out which sections are most frequented. This enables you to optimize store layouts for better traffic and product arrangement. Third, for more advanced setups, CV systems can identify customer emotions, types of membership, and even past purchases to personalize in-store advertising and product suggestions.

Assist Data-Driven Decisions

Computer vision collects real-time visual data from cameras. This data tells you about customer behavior, product popularity, and traffic patterns. So, you can make smarter decisions about inventory and marketing strategies. For example, if it shows which products are sold slowly, you can adjust store layouts to make them more visible or devise promotional programs to boost sales.

Reinforce Security

Computer vision adds a layer of security to your retail store by spotting fraudulent activities (like shoplifting) and alerting staff in real-time. This reduces inventory loss and makes stores safer for both employees and customers.

10 Common Applications of Computer Vision in Retail

To help you better understand these benefits, we now explain how computer vision systems are adopted in today’s retail stores. Here are ten popular applications of this advanced technology you should consider:

1. Inventory Management Automation

Computer vision is revolutionizing the way brick-and-mortar stores manage inventory. By using security cameras to constantly scan an entire store, it can automatically count inventory in real-time. If a CV system detects out-of-stock products or misplaced items on shelves, it’ll promptly alert store associates for restocking or reorganizing. This not only reduces manual checks but also keeps inventory always balanced.

Even when combined with predictive analytics, computer vision can forecast customer demands to optimize inventory levels. As such, you can understand what and when customers make purchases to stock items in the store better.

Understanding the growing importance of computer vision, various retailers like Home Depot and Auchan have integrated it into their stores. For example, Auchan used Neurolabs’s “Reshelf” technology to inspect on-shelf availability in real-time and alert staff employees about out-of-stock products.

Other retailers like SpartanNash or Schnuck Markets even utilize autonomous inventory robots powered by computer vision. These robots can roam store aisles and scan shelves to check inventory levels. For instance, Schnuck has deployed its grocery chain robot called Tally to automatically collect on-shelf data like inventory position, price accuracy, and even promotional execution. Tally can identify out-of-stocks 14x more effectively than manual checks and reduce the times items are unavailable on shelves by 20-30%.

2. Self-Checkout Systems & Smart Carts

Imagine this: your customers walk into the store, scan your store’s proprietary app, pick up items for automatic billing, and walk out. No more waiting in long lines in front of a cashier counter. Today, this isn’t a fictional idea. In 2018, Amazon introduced a “Just Walk Out” technology through its Amazon Go stores. This concept began the era of self-checkout systems and removed the traditional checkout process completely.

The idea behind Just Walk Out is quite simple. Customers first scan their app at the gate entrance. Amazon Go then uses computer vision, combined with sensor fusion and deep learning, to track what customers pick up, add these products to their virtual carts, and automatically charge their account when they exit.

Amazon continued to apply its self-checkout technology to replace physical carts. In some Amazon Fresh stores, smart carts, known as Amazon Dash Carts, are integrated with cameras and sensors to identify the price and automatically tally the bills of items that customers shop.

This self-checkout concept has encouraged a cashier-less shopping trend across many other retailers like Walmart, Family Mart, and Kroger. Accordingly, the global demand for self-checkout systems is predicted to grow by 10.4% annually from 2024 to 2034. These systems speed up the checkout process and eliminate inconveniences (e.g., long queues and manual scanning) that can dissatisfy the shopping experience.

3. Queue Management

In addition to self-checkout systems, various retail stores still maintain traditional checkout methods. So, what have they done to manage long lines at the cashier counters and boost customer experience?

Let’s look at the case study of Tesco – a British multinational retailer. The company uses the “Smartlane” system that includes thermal imaging cameras and infrared sensors installed on the ceiling to sense the number and behavior of customers at checkout.

Plus, the system can automatically predict queue length and waiting time, helping managers to decide whether to open additional registers. This proactive approach allows Tesco to handle queuing issues quickly, optimize staff allocation, and accelerate service. Additionally, the adoption of computer vision helps keep wait times shorter, improve the shopping experience, and encourage shoppers to return.

4. Customer Behavior Analysis

Computer vision, reinforced by deep learning algorithms, helps many retailers like Sephora, ATU Duty-Free, and Samsonite understand customer behavior in new ways.

First, a vision system can count how many people visit a store per hour in real-time. Take Sephora as an example. This French brand used AllGoVision’s vision solution to analyze video streams from the Axis cameras installed at the entrance and near product sections. The system counts the number of visitors crossing a virtual line drawn in the camera view and sends real-time reports directly to the PoS systems. With the data, Sephora can understand customer traffic, peak times, conversion rates, and product performance.

Second, computer vision can analyze foot traffic data collected from cameras or sensors to identify a shopper’s journey and high-traffic areas through a heat map or spaghetti diagram. It can also use techniques like image classification to recognize crucial customer behaviors within certain sections, like using trolleys or self-checkouts.

Third, CV algorithms can estimate dwell time, or how long they stay in specific areas. If customers go straight ahead to a specific area upon entering and linger near certain items, the system can indicate high interest. Accordingly, you can arrange less popular, yet relevant products on the way to that section to encourage sales.

Finally, CV can observe how customers engage with individual products, like picking up, examining, or placing them back. This gives you a better understanding of what appeals to customers (like packaging or free gifts). Also, machine vision can track how store associates interact with customers in real-time. This provides a better sense of in-store service quality. By analyzing these behaviors, you can modify product displays and improve customer service for a more engaging shopping experience.

5. Personalized Advertising

Computer vision can identify particular customers when they enter the store and provide personalized in-store adverts.

How? Particularly, cameras identify a customer’s important details like age, gender, or facial expressions. Deep learning algorithms then can classify and analyze customers based on these details.

Imagine a female customer standing near a camera in a skincare product section. Computer vision can estimate her age and evaluate whether she looks happy, dissatisfied, or neutral toward a particular product.

Based on sentiment analytics, the screen can show ads that fit her profile and mood in real-time, like displaying ads for trendy products or new arrivals if it identifies a young lady. This approach makes ads more relevant and appealing to the customer. Plus, it provides the customer with a unique shopping experience in the store.

6. Loss Prevention and Theft Detection

Recognizing suspicious activities like shoplifting or sweethearting is one of the key applications of computer vision in retail. It uses deep learning algorithms to monitor human behaviors, detect patterns, and make informed decisions based on the input data.

For example, when computer vision watches products being taken but not scanned at self-checkout counters, it’ll flag them as potential fraud and send real-time alerts to staff.

Machine vision can identify unauthorized access to restricted areas (e.g., in staff-only areas or behind the cashier counter). Even when your shop is closed, computer vision systems can use techniques like automated people detection to discover intrusion events.

With these capabilities, computer vision is increasingly adopted in brick-and-mortar retail stores to prevent fraudulent activities. Walmart has implemented a machine vision technology, known as Missed Scan Detection, to recognize which products move past checkout registers yet are not scanned for billing. The system uses cameras to watch items passing through the registers and signal unusual activities to store associates. This technology has enabled Walmart to decrease rates of theft, fraud, and scanning errors.

7. Quality Control

Computer vision helps your retail stores control product quality efficiently. It can check product surfaces through high-resolution cameras to identify visible signs of damage, like cracks, broken packaging, or dents.

This technology is especially useful in monitoring perishable products like fruits, vegetables, meats, or dairy. Reinforced by advanced algorithms, machine vision systems can analyze and detect shapes, colors, or textures that indicate signs of ripeness or spoilage. If products are visibly deteriorating or close to expiring, the systems may notify store employees to remove or discount to avoid waste.

Kroger, one of the US’s biggest supermarket chains, uses computer vision and analytics to recognize early signs of quality deterioration. This approach enables the company to ensure the high quality of goods and avoid potential health risks incurred by damaged or spoiled items.

8. Augmented Reality (AR) Mirrors

In 2012, Uniqlo made the whole retail industry “wow” with the world’s first virtual mirror in its San Francisco-based flagship store. It’s a traditional mirror with a digital display behind the glass.

Powered by computer vision and augmented reality (AR) technologies, the magic mirror allowed customers to try on up to 120 coats within a minute. This eliminates the need to wear physical clothes and reduces long waits for fitting rooms, fostering a shopping experience and personalization.

Uniqlo opened a new era of virtual mirrors across the retail industry. By using advanced computer vision techniques, AR mirrors can detect a shopper’s key details (e.g., body contours or facial features) in real-time to display the chosen item on the customer’s body or face in real-time.

Also through AR mirrors, retailers can collect meaningful data about customer preferences. For instance, your store can follow which items are most frequently tried on and modify inventory to meet demand.

9. Employee and Compliance Monitoring

Computer vision technology can support human managers in managing store employees and ensuring compliance with standard operating procedures (SOPs).

Particularly, vision systems analyze the images and videos of staff to identify whether they’re complying with internal policies, like greeting customers, wearing uniforms as required, or using face masks during the quarantine period.

The systems, reinforced by machine learning algorithms, can inspect adherence to SOPs. These SOPs may involve store opening and closing times, branding policies, cleanliness of premises, customer management, and more.

For instance, machine vision can detect whether your store is following the correct product placement, like displaying items of a particular brand at eye level. Or it can be used to identify if the shop’s floor, washrooms, and counters are cleaned after operating hours. When it discovers any policy violations, it’ll promptly notify store employees for fixing. With computer vision techniques, human operators have more time to focus on more critical and complex operating tasks.

10. In-Store & Outside Inspection

Smart security and surveillance cameras can be integrated with computer vision to continuously monitor in-store and outside events.

Inside your store, smart cameras can capture and understand analog controls and temperature displays on appliances like thermostats or air conditioners. Combined with machine learning algorithms, the vision system can compare readings from these devices to preset thresholds and notify employees to take corrective action.

It also automatically inspects whether fire extinguishers, sprinklers, and other safety equipment are continuously available. With vision-based cameras, your store is always kept safe from unexpected disasters like fires caused by AC overheating.

Further, the system can recognize obstacles stopping in-store customer flow (e.g., promotional materials, water spills, or misplaced boxes), and send camera-based alerts to store staff. It also helps you check if automatic doors work and open as expected.

Outside the store, vision systems can analyze whether parking spaces are available or whether any vehicles block the shop entrance for a long time. They can continuously inspect the emergency exits and signal store staff if any obstacles are blocking the way.

Final Thoughts

Once you’ve navigated to the end of this article, you might better understand how computer vision is adopted in different activities of retail stores. From automated inventory management and loss prevention to self-checkout systems and AR mirrors, this tech is changing the retail industry significantly. With these use cases, machine vision can boost in-store efficiency, reduce operational costs, and enhance customer experience. Considering this tech in your business strategies can give you a competitive edge in the retail industry. If you want to learn more about this amazing technology, subscribe to our blog and receive the latest news!