How computer vision is changing retail

Computer vision in retail enables computers to see and analyze critical data and gain understanding from it. When applied to the retail process, it can create a paradigm shift in the way traditional retail works.

If artificial intelligence enables computers to think, then computer vision enables computers to see, analyze and understand. As a subset of artificial intelligence, computer vision allows computers and systems to extract meaningful information from digitized images, videos, and any other visual aids.

It provides recommendations and takes certain actions based on the data it gets. Owing to its revolutionary solutions, the global computer vision market is expected to reach USD 73.7 billion by 2027. In recent years, more and more retail businesses have planned to incorporate computer vision into their operations.

By 2028, the market size of artificial intelligence in the global retail industry is expected to reach US$31.18 billion. From analyzing consumer behavior to monitoring in-store health, computer vision in retail can help improve retailers’ revenue and customers’ overall shopping experience.

Advantages of Computer Vision in Retail

Computer vision in retail has the potential to improve the overall shopping experience for customers and ROI for retailers to change this industry.

1. Heat map mapping in the retail industry

A heat map is a graphical representation of data, using different colors to represent different values. It can help visualize density. In the retail industry, heat maps will help users identify and understand consumer behavior and store functionality. Heat mapping technology in retail provides real-time imaging to help monitor activity and assign different colors to consumer traffic on each floor or area. Industry giants such as Sephora, Samsonite, and ATU Duty Free have deployed heat maps in their stores to test new sales strategies, experiment with layouts, and understand customer activity in the store.

2. Virtual Mirror

The virtual mirror is a two-way mirror that displays an electronic display behind the glass. Most of these mirrors are equipped with computer vision that can monitor and analyze visual patterns. The virtual mirror uses sensors, cameras and displays equipped with computer vision to provide customers with different outfit suggestions based on current trends and collected data.

Giving shoppers the option to view and virtually try on several items of clothing helps save them time, avoid waiting in line, and improves the overall shopping experience. Cameras deployed with computer vision will help capture the shape and size of the user and based on this, provide them with various options based on fashion trends. Implementing virtual mirrors in retail stores can help reduce sales staff workload and also enhance the brand experience.

3. In-store traffic detection

Computer vision cameras and sensors for customer analytics help detect and identify in-store traffic and data patterns. This allows separation of buyer routes throughout the store and capture of pass-through traffic rates. This helps retailers identify which promotions are driving user engagement and which promotions are doing poorly.

AI retail analytics also includes employee and customer interactions and is not limited to observing shoppers’ purchasing behavior. It provides real-time visibility into in-store service engagement and helps drive personalized messaging and marketing campaigns.

Samsung, for example, uses computer vision to help quantify customers’ in-store behavior. It uses several in-store cameras and advanced computer vision algorithms to collect traffic, demographic and dwell time data, giving them a clear understanding of the store's performance and preliminary measures of performance.

4. Loss prevention

Computer vision is known as the eyes of the computer and is therefore crucial in preventing theft losses in retail stores. Machine learning algorithms in computer vision help observe customer behavior, detect and identify patterns, and make required decisions based on these inputs. This helps identify any suspicious activity from shoppers.

Problems such as employees giving away free or discounted products to people they know have been reduced after implementing computer vision. Because the technology can identify every item in the checkout area and tie it to a transaction, computer vision can help prevent any attempts by employees to steal items.

5. Image recognition

Computer vision-driven image recognition technology is being widely used by retail and e-commerce companies. This benefits both consumers and retailers. By using deep learning in image recognition, it can help retailers by providing features such as personalized search, customer or shopper profiling, counterfeit detection, fashion trend analysis, and more.

With the data collected through image recognition, retailers can implement it, design effective marketing campaigns, and improve return on investment. It can also enhance the in-store experience, as the technology can help retailers retain sales from customers who prefer to compare prices online via smartphones, or other devices.

6. Strengthen inventory management

The inventory management system in the retail industry is to meet the needs of customers and supply products without storing too many products. These products It can end up expired or wasted in warehouses, or conversely, depleted of inventory.

Availability of a product on the shelf refers to its visibility on the shelf to the customer at the right place, time and price. Poor management of on-shelf availability results in losses for everyone as customers can leave a specific retail store and go to another retail store, which results in long-term loss of customer loyalty and sales.

Using computer vision and machine learning can help curb availability mismanagement on the shelves by monitoring and granting opportunities by viewing inventory at any time. Computer vision provides real-time data collection through video and images collected from mobile phones, robots and/or fixed cameras placed in warehouses and shelves. Software powered by computer vision helps detect defects in mislabeled items, track inventory, predict off-peak and peak demand for specific products, and provide orders to suppliers.

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