Computer Vision in Retail: Real Use Cases and Examples

October 17, 2022
5 min read

Retail companies' interest in computer vision is fueled by the increasing ability to utilize  massive amounts of data. Computer vision technologies offer retail stores an opportunity to solve many retail challenges, which can transform employee and customer experiences. 

For businesses to remain relevant, they must provide excellent customer experiences. The use of computer vision enables a seamless customer experience and helps retailers manage their stores more efficiently. 

This article explores computer vision and its top use cases in the retail industry that can assist you in making better decisions. 

What is Computer Vision? 

Computer vision is a subfield of AI (Artificial Intelligence) that enables systems and computers to derive useful information from visual inputs such as videos and digital images and make recommendations based on them. 

Computer vision is more or less like human vision; the only difference is that human vision is more advanced because it can differentiate objects better. It teaches machines to use algorithms and data to analyze digital images to detect differences. 

How Does Computer Vision Work? 

Computer vision (“CV”) technology mimics how the human eye works; however, it’s more efficient than the human eye since it can simultaneously analyze thousands of digital images to distinguish them. The computer vision process can be summarized into three steps:

  • Image acquisition: Computers acquire large data sets or images in real-time through photos or videos for three-dimensional analysis.
  • Image processing: This process is automated using deep learning models, but the machines must be trained first by being fed numerous pre-identified images.
  • Object identification: This is the last step where the machine classifies the image. 

How Is Computer Vision Changing the Shopping Experience?

Even before the pandemic, the retail industry had to contend with the emergence of e-commerce stores taking market share. COVID-19 accelerated the growth of e-commerce, with more customers finding online shopping more convenient than in-store purchasing. 

Retailers leveraging CV are gaining big, offering a frictionless shopping experience, reducing operational inefficiencies, and preventing shoplifting. Queues  have become a thing of the past, as cameras can record all the items a customer picks out, calculate the total in real-time before they check out, and make payments using a mobile app. 

Computer vision systems eliminate the need for cashiers; thus, retailers can place their employees where they’re most needed — helping and enhancing the retail experience. 

An Overview of the Retail Industry: Why the Focus is on Customer Experience 

It’s becoming commonplace for customer experience in the retail industry to be cited as the most critical aspect when deciding whether to buy a product or return to a particular store. This is a sharp contrast to the traditional approach centered around the product. Research by Oracle indicates that 74% of senior executives believe that customer experience influences brand loyalty. 

Retailers that successfully implement a more customer-centric approach and focus on the most important aspects of cross-channel customer journeys can create substantial value. A study by Temkin Group found that businesses that make $1 billion every year can make, on average, an extra $700 million in 3 years of investing in the consumer experience.  

An omnichannel approach aimed at the main interaction model can enhance how customers take advantage of the self-service options, enhancing customer experience. This is backed by a study by American Express, which established that 86% of customers are willing to pay more for a great customer experience. In a nutshell, offering an excellent retail experience helps businesses upsell and cross-sell their products while retaining and gaining new customers. 

Best Use Cases of Computer Vision in Physical Retail

Up to this point, you can see why customer experience is the main focus for nearly all e-commerce businesses, making computer vision an advantageous tool  for  a successful retail business aiming to prioritize customer satisfaction. Below are some of the most common computer vision use cases in retail. 

1. Cashierless Stores

Most retailers are leaning towards customer service automation which has led to increased use of computer vision technology for cashier-less checkouts in physical stores. These systems enable in-store sensors and cameras to accurately track customers, shelves, and products. Based on AI and CV, the system charges the buyer for the products when they exit the store. 

Grabado and Amazon Go are great examples of how machine language techniques have been employed to create an innovative way of in-store shopping. After customers are authenticated on the respective mobile applications, they can proceed to pick up the products they require and pay when they leave the stores. 

2. Inventory Management

Retail stores have sensor systems and smart cameras to detect mislabeled or damaged goods and track stock. Besides this, computer vision systems are also used to monitor fast-moving goods that are seldom consumed. 

This allows management to decide how frequently they should refill their storage rooms and which items to focus on. For instance, Shelfie uses cameras with computer vision to alert employees about empty shelves or incorrectly placed items. 

Wakefern, a retail company based in the United States, has employed Tally (a robot) to detect incorrect pricing and damaged goods and notify staff about products that aren’t in stock. 

3. Retail Heat Maps

A retail heat map uses real-time digital data to track customer movement and assign colors corresponding to traffic volume in all sections. The data is usually created using cameras or sensors in the store that can track customer behavior and generate a heatmap. 

This allows management to see a store at a glance, identify busy and quiet areas, and compare the pattern at different times. Stores use the information generated to create in-store marketing campaigns. 

For instance, retailers like ATU Duty-Free, Samsonite, and Sephora use heatmaps to experiment with layouts, test new merchandising approaches, and to track customer activity. 

4. Virtual Mirrors

Even though virtual mirrors are an emerging technology, they’re already wildly popular in the retail industry. Virtual mirrors are a concept of Augmented Reality that shows customers how an outfit would look without trying them on. 

Virtual mirrors are the next frontier of improving customer experience and promoting personalization in retail operations. They are usually equipped with Augmented Reality, and computer vision cameras can show various contextual information, which helps consumers connect with the brand. 

For example, FindMine provides an in-store dressing room as part of its marketing services. This virtual dressing room uses algorithms to offer customers real-time outfit recommendations based on what they’re wearing. 

5. Shopper Measurement

Computer vision technology can assist retail outlets and study customer behavior. The technology is used to conduct customer footfall analysis — people counting — and compute the total time they spend with products. 

This way, retailers can better manage and understand queues, offering analytical data that assists retailers in improving store management. For instance, shopper measurement can show the manager which products are popular with a specific demographic.  

Amazon has partnered with SoftServe and VTech Lab to use computer vision systems to make in-store shopping safer with shopper counting and mask detecting solutions that alert them when safety protocols aren’t followed. Amazon also provides real-time social distancing feedback so employees and consumers can feel safe. 

6. Retail Loss Prevention

By tracking customer movements and products, computer vision technologies can help prevent loss in retail outlets. Retailers like Walmart are using artificial intelligence-supported software to prevent shoplifting. 

Integrating POS and product-level tracking with computer vision can help eliminate cashierless and manned register checkout fraud. This is done by adding cameras to existing check-outs, using artificial intelligence, and comparing the items scanned. The cameras are deployed to track the items as customers scan them at the POS. 

Once the customer scans the items, the POS and camera are connected, and if there are any discrepancies between the POS counts and the camera counts, a staff member is alerted. 

7. Improving Store Layout

Cameras with computer vision can help retail store managers monitor customer movements to determine purchase patterns. The managers can then use this information to identify hot areas and place fast-moving products to improve sales. 

The technologies also provide managers with details such as how long customers take to complete a purchase and which items have the most put-backs. 

An example of a company that used these technologies to improve layout is Samsung during the Galaxy S9 launch. Samsung used cameras with computer vision to get demographic data, product interaction, and footprint in a bid to convert every visitor into a prospective buyer. 

The insights helped Samsung change its layout, thus maximizing its marketing campaign and increasing sales. 

8. Price Automation And Optimization 

This is among the most cost-effective Artificial Intelligence applications in the retail industry. In this aspect, machine language algorithms are at the center of pricing. Pricing is an essential aspect of any business since it’s affected by demand dynamics, holiday seasons, and shopping trends

Retailers benefit from increased scalability and productivity when using computer vision technologies for price automation. Price optimization ensures that the prices are constantly adjusted so they don’t lose a chance to maximize returns. 

Amazon leverages AI to drive dynamic pricing, thus reducing prices to increase sales if needed and increasing the prices when there’s high demand. The company relies on data from customers who visit its site and uses customer behavior to adjust pricing. Though Amazon is able to do this at scale in an e-commerce world, using real world computer vision data to drive analogous insights is a tactic available to retail stores everywhere. 

9. Helps With Behavioral Analytics 

Marketers use CV to collect information and consumer behavior analytics, such as tracking eye movements. They then use this information to optimize displays, such as placing seasonal goods strategically or swapping out window displays based on the data.

Systems can track facial expressions to determine how customers feel; this can help marketers predict a product’s popularity. CV also analyzes patterns like how long consumers check out products and use the information to create targeted marketing campaigns. 

FAQs

What are some of the challenges in computer vision in the retail industry?

In today’s landscape, computer vision systems are important for the retail industry to meet consumers’ evolving expectations. However, there are challenges associated with using these systems, including being expensive to put in place, especially for small businesses, and obtaining data for training computer systems can be difficult. 

How is computer vision changing the shopping experience?

Computer vision technologies are making the shopping experience seamless by eliminating operational inefficiencies. 

Leverage the Power of Computer Vision Today 

The pandemic has significantly impacted retail experiences; AI tools like computer vision help stores build optimized and customer-centered experiences. From loss prevention and price automation to shopper measurement and inventory management, the applications of computer vision technologies in retail are boundless. 

To remain competitive, businesses must leverage the power of computer vision technologies; Check out curbFlow if you need help using insights such as people counting, testing the success of promotional displays, and understanding traffic flow.

written by Curbflow

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