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Artificial intelligence (AI) has been at the forefront of technical innovation in fields ranging from football to e-commerce. By 2025, the worldwide AI market is estimated to be worth $60 billion. 

Of all the disciplines covered by AI, one is being actively monitored by techies and industry titans: computer vision. It is a subset of Machine Learning that enables computers and digital systems to comprehend and extract meaningful information from various types of visual input, such as images and videos. 

"AI and computer vision are ready to alter how retail organizations function, with retailers expected to spend more than $12 billion on machine learning by 2023." 

As far-fetched and complex as it may sound, the applications of such a technology are numerous. Let's look at computer vision and how it can be used in the retail industry. 

What is Computer Vision in Retail? 

It is a branch of artificial intelligence (AI) that teaches machines to detect and interpret real-world objects. 

The 'eyes' of the machine are cameras. It has a 'brain' that can process photos or video information. And the algorithm begins to assess the environment and respond to what it sees when the camera is turned on. 

Pattern recognition is an important word when discussing computer vision: the act of teaching a computer to understand visual input by giving it millions of generated images — while combining manual feature engineering with a set of established rules. 

Engineers can label the photographs and tell the algorithm what to look for. Consider we want to teach a computer to differentiate between jeans and skirts. We can successfully define the rules allowing the computer to detect patterns. 

Computer vision is being employed in a variety of applications. 

We see AI-powered cameras searching for criminals in public, comparing their images to a database, and alerting authorities if a match is found. We see software functioning as a quality checker on assembly lines, detecting flaws and preventing defective products from reaching customers. 

However, we generally see computer vision software assisting businesses in automating routine procedures and increasing operational efficiency. 

Rise of Computer Vision in Retail 

The retail industry is constantly increasing and progressing around the world. In 2021, total retail sales in the United States were $5.15 trillion. The retail industry is continuously on the outlook for new strategies to attract customers, successfully sell items, improve customer experience, and boost efficiency. This is made possible by AI, machine learning, computer vision, and deep learning technology that is constantly growing. 

The advancement of computer vision is changing how organizations function and has sped up the development of creative apps that promote growth and efficiency. 

Applications of Computer Vision in Retail 

The pandemic has significantly impacted retail experiences, and AI-powered solutions such as Computer Vision can assist retailers in creating a more streamlined and customer-centric retail experience. The uses of Computer Vision for the retail business are numerous, ranging from theft prevention and inventory management to insights into the marketing strategy. Let's take a look. 

Creating Effective Adds 

What is the most effective approach to getting your audience's attention? Making eye-catching advertisements. Businesses of all sizes and types compete to generate engaging, relevant, and eye-catching commercials for product marketing and brand promotion. 

Visual material is a powerful component of an effective advertisement. For a good reason, 50-80% of all brands will use visual content and image-based marketing in the coming years. According to studies, individuals are significantly more likely to engage with an advertisement with a solid visual foundation. 

This usually entails using complex software such as Adobe Photoshop or Illustrator to create and edit designs for corporations. On the other hand, businesses may easily modify photos and scale graphics for different platforms on autopilot using contemporary AI-powered background removers and image editing tools that leverage computer vision. 

Also, consider visual listening. AI may constantly analyze visual content and mentions across the web to comprehend customer preferences and attitudes based on their reactions to a company's visual content and, thereby, the brand itself. 

In this case, computer vision assists brands and marketers in focusing on effective social listening, allowing them to generate better commercials and goods tailored to their target audience's tastes and demands. 

Customer Tracking 

Any retail giant would swear to the significance of tracking customer statistics. Tracking audience analytics offers far-reaching benefits, whether it is tracking the most popular places in a store or average buy time. According to reports, correctly tracking consumer data using ML and DL-based algorithms increases ROI by 115% and overall profit by 93%. 

Computer vision in retail can assist in tracking consumer behavior insights: 

• "Which section of the store has the most foot traffic?" 
• "How long does it take an average client to make a purchase?" 
• "Which product is most popular at X time of day?" 
• "And which products result in the highest put-backs?" 

are all questions that computer vision can assist in answering. 

This data is collected via 2D and 3D-based Computer Vision (CV), and valuable insights into consumer behavior can be obtained through data segregation and proper statistical approaches. 

Cashier-less Stores 

As revolutionary as they may sound, cashier-less stores are paving the way for a more streamlined, AI-assisted shopping experience in stores worldwide. Computer vision in retail businesses is replacing the requirement for human workers in the following functions: 

• Tracking the products selected by a user and automatically adding the total to their bill 
• Monitoring critical client metrics such as put-backs and average buy-time 
• Identifying any pain areas that a customer may encounter when shopping. 
• Identifying demographic-based purchase habits and product preferences to help enhance overall Customer experience (CX) 
• Allowing customers to self-checkout at predetermined intervals of time 

People Counting and Crowd Analysis 

COVID-19 has significantly impacted customer retail experience, particularly regarding footfall in retailers. Many countries restrict the number of individuals permitted in a store at one time because of pandemic restrictions. 

Enforcing these requirements can be time-consuming and potentially dangerous, as store employees are continually exposed to hundreds of people daily. 

Computer vision is being utilized to identify and track the number of users in the store at any given time, as well as to inform personnel to help enforce policies. 

A store can readily detect the heaviest traffic in a day, the number of people currently in the store, masked/unmasked persons, and basic user profiles based on CCTV cameras based on demographics or purchase interest. 

Shopper Measurement 

Computer Visions are also used to track other store customer activity areas, which adds to this data. Here are some additional metrics that store managers may collect using ML-based models to improve customer experience, track store performance, and focus on constructing a more functional retail space. 

• Footfall - Identifying the busiest store times, once again, can assist retailers in focusing on staff availability, stock and inventory management, and compliance with COVID-19 criteria. 
• Interactions - Tracking and recognizing client interactions, complaints, and inquiries can assist stores in detecting holes in their purchasing process, establishing more assistance stations, and gaining information about product performance and overall customer happiness. 
• Passers-by/traffic - While it may seem unusual, tracking window shoppers, passersby, and general inquiries from non-buyers all help retailers assess their conversion rate. The visitor-to-customer conversion rate can aid in developing more effective ad campaigns, possibilities to attract more customers, and discounting strategies depending on passerby demographics. 

Theft Detection 

A teen seeking retail fun enters a high-end store in search of empty aisles and expensive things to shoplift. While he might get a chance at a store with all-human employees, he has no chance in a store with CV-based surveillance. 

CV uses cutting-edge face recognition and tracking shifty/suspicious customer behavior to assist retailers in preventing future shoplifting situations. Many retailers have also deployed "seeing" technology to determine whether customers have paid for an item in their shopping basket or are simply shoplifting. 

Waiting Time Analytics 

With Computer Vision, you can reduce the amount of unhappy, bored shoppers and long lines at the cashier. As previously said, tracking customer analytics (such as footfall and waiting time) can assist retailers in improving facilities such as available personnel, counters, inventory, and ease of access to various products. 

Waiting-time analytics can also assist stores in answering the following CX-related questions: 

• When do clients spend the most time during the purchasing process? 
• Is this due to reduced employees or a lack of inventory/products? 
• Are Customers taking longer in cashier lines because of less active counters? 

Inventory Management 

Here are some statistics. Over 64% of businesses intend to adopt computer vision-based technologies to improve inventory management. 

Automating inventory orders and self-checkouts can assist retail establishments in keeping a close eye on their product cycles and real-time product stock to help build a robust and smooth retail experience that is never out of stock. 

How to Harness the Potential of Computer Vision in Your Retail Business? 

Computer vision can alter how shops provide value to their customers while also enhancing back-end processes. To properly implement and employ such technology, retailers must guarantee that each component of the transition, from cameras to sensors to software, works in conjunction with the others. 

Juniper Research forecasts that global spending on retail AI would more than quadruple from $2 billion in 2018 to $7.3 billion in 2022. 

Retailers aim to acquire an early-mover advantage by investing in cutting-edge technology and providing distinctive, technology-driven customer experiences ahead of their competitors. 

Benefits of Computer Vision in Retail 

Businesses are constantly looking for ways to better understand the consumer purchasing experience to retain existing consumers and attract new ones. They may collect insights from camera feeds across each store site using computer vision technologies. This broad visibility offers a fresh perspective on the consumer experience, allowing management to examine and expand the organization while improving overall engagement depending on consumer behaviors. 

Deep learning advancements in recent years have helped computer vision, and state-of-the-art algorithms can now equal or even outperform human performance on numerous tasks. 

Brick-and-mortar retailers can use computer vision to improve in-store analytics, cut losses, retain more consumers, and many other things. Ecommerce organizations can use retail AI to control image quality and augment photographs for improved search. Computer vision can increase your retail operation's efficiency and profitability. 

Management can get various benefits from the capability of computer vision, including the ability to: 

• Increase sales by improving store layouts. 
• Optimize item placement throughout the storefronts. 
• Improve the personalization of client interactions 
• Customize loyalty programs as well. 
• Improve interactions with frequent and VIP consumers. 

A Case Study: Walk Out Shopping by Amazon Go 

Amazon Go has introduced its customers to a cutting-edge "Just Walk Out" purchasing experience. Cash registers are no longer used at the brand's outlets. Customers must have the Amazon app to enter the store. They enter, select the required products, and walk out without going through a checkout counter or queue. Amazon then sends them an invoice and charges their credit card. 

A computer vision algorithm recognizes which goods were picked up or returned to the shelf behind the scenes of this novel approach. A virtual cart tracks all goods in the customer's trolley and bills them when they leave the store. This is made feasible by combining technologies such as computer vision, deep learning, and sensor fusion. The same technology underpins self-driving automobiles. 

Conclusion 

Computer vision technologies are projected to increase in use in the following years due to their capacity to generate efficiencies, save time and money, improve accuracy and outcomes, and improve safety. Businesses of all sizes should look for a dependable technology partner to help them through this process and assure the success of their AI projects. 

Nonetheless, and despite all obstacles, there is hope that the technology will be widely implemented, in some form or another, within the next ten years. Not because it will increase profit (though it will), but because it will, perhaps counter-intuitively, make retailing a more human experience.  

As a result, all of these improvements enable businesses to give a better retail experience to their clients while also increasing earnings. It's a win-win situation for everyone!