Online retail has one key advantage — customer experience. No queues, no delays, and little movement to make a purchase. According to a research from Forrester, 72% of U.S. retail sales will still occur in bricks-and-mortar stores because people want to interact with a product before buying, or simply don’t want to wait for delivery.
The idea of checkout-free shopping in venues crystalized as Amazon Go, Tesco, Walmart, and many more. The idea of using fully-automated checkout with computer vision is a successful example of retail automation. But, a few store owners want to build a whole new outlet to run their business offline. As it requires an integrated software infrastructure, as well as imposes development and financial challenges we will discuss today.
In this article, we’ll analyze how any brick and mortar store can be automated with computer vision systems. Here we’ll look at how it works, what the options for checkout automation are, and what challenges are out there.
Computer vision checkout automation for brick and mortar retail
The majority of in-store operations like shelf management, checkout, or product weighing require human supervision. Human productivity is basically a performance marker for the retailer, and it often becomes a bottleneck, as well as becoming a customer frustration factor.
Namely, checkout queues are the pain point both for customers and retailers. But it’s not only the queues, since actual human effort costs money. So how does computer vision apply to these operations?
Computer vision (CV) is a technology under the hood of artificial intelligence that enables machines to extract meaningful information from the image. At its core, computer vision aims at mimicking human sight. So analogically to an eye, CV relies on camera sensors that capture the environment. In its turn, an underlying neural network, it’s brain, will recognize objects, their position in the frame, or some other specific properties (such as differing a Pepsi can from Dr. Pepper can).
That’s our ground base for understanding how computer vision can fit brick and mortar retail tasks, as it can recognize products situated in the frame. These products can be placed on the shelves, or carried by the customers. Which allows us to exclude barcode scanning, cash register operation, or self-checkout machines.
Although implementations of computer vision significantly differ by complexity and budgeting, there are two common scenarios of how it can be used for retail automation. So first let’s look at how full store automation can be built.
AI-powered autonomous retail checkout: full store automation
Autonomous checkout is called by different names: “cashierless”, “grab-and-go”, “checkout-free”, etc. In the shopping experience of Amazon, Tesco, and even Walmart, such stores check the products during the shopping, and charge for them when you walk out. Sounds simple, and that’s how it works in a basic scenario.
Shopping session start. Shops like Amazon use turnstiles to initiate shopping via scanning a QR code. At this point, the system matches the Amazon profile and digital wallet with the actual person entering the store.
Person detection. This is the recognition and tracking of people and objects done via computer vision cameras. Simply, cameras remember who the person is, and once they take a product from the shelf, the system places it into a virtual shopping cart. Some shops use hundreds of cameras to view from different angles and cover all the store zones.
Product recognition. Once the person grabs something from the shelf, and takes it with them, cameras capture this action. Matching the product image on video with the actual product in the retailer’s database, the store places an item into a virtual shopping cart.
Checkout. As the product list is finished, the person may just walk out. When the person leaves a zone covered by cameras, computer vision considers this as the end of a shopping session. This triggers the system to calculate the total sum, and charge it from the customer’s digital wallet.
From the customer standpoint, such a system represents a similar shopping experience as it is in the online stores, except you don’t need to checkout. Enter, find what you want, grab it, and leave. Although, to provide customers with full autonomy, and cover all the edge cases, we’ll need to solve a large number of problems technically. So what’s so complex about autonomous checkout?
AI Application Development Guide For Business Owners
The challenges of AI-powered autonomous retail stores
Customer behavior can be unpredictable, as we are going to automate checkout for dozens of people that check and buy thousands of products at the same time. This imposes a number of challenges for computer vision:
Continuous person tracking
As the customer enters the store, the system should be able to continuously track them along shopping routes. We need to know that it’s the same person who took this or that item in different parts of the store. In a crowded store, continuous tracking might be difficult. As long as it’s not allowed to use face recognition, the model should recognize people by their appearance. So what will happen if somebody takes off his coat, or carries a child on shoulders?
To enable continuous tracking, we’ll need to provide 100% coverage for cameras to detect people passing from zone to zone. Placing cameras at different angles, we also need sensors to communicate their precise location, so we can use this data to track objects more accurately.
The “Who took what?” problem
Then, we have to remember there are also products, right? And customers’ shopping process is not linear. They move items, smell them, put them back, and go to another shelf. Especially when there are multiple people at one shelf, it becomes difficult for a model to recognize who took what, and if they actually took the product to buy.
Amazon, for example, solved this problem by implementing human pose estimation and human activity analysis. Basically, that’s another layer of artificial intelligence coupled with computer vision. What it does is it measures the position and movement of a person, to predict what he or she grabs, and if the product was taken to be purchased.
This solves the problem with multiple customers at a shelf, and helps to denote who took this specific product even if the camera was blocked by somebody.
Identifying similar products
Concerning products, we’ll also need to deal with similar packages. Some products have minor differences in their look, which makes it harder for the model to fetch all the detail. Especially if there is some obstruction going on in the frame, or the object is moving fast. We can address this issue through training the model to spot little details, and use cameras with higher resolution and frame rate.
While it looks beneficial to use autonomous checkout, the complexity of such a system can be onerous. For a tech-first company, this is not a problem. But for the usual retailer, the burden brought by artificial intelligence lowers the value of such automation. That’s why partial store automation with computer vision can be more suitable.
Smart vending machines: partial store automation
When it comes to vending machines, they can be placed in-store, or moved out to other indoor and outdoor locations. And this can be an elegant solution to the problem imposed by tracking the whole store. Vending machines can be represented by shelves with glass doors or regular fridges using computer vision cameras to operate purchase processes. Installing a QR code scanner, we can minimize the checkout procedure to the location of a single fridge. So the idea is quite simple:
Shopping session start. The session starts once a person approaches the fridge and opens it up. This can be done via scanning a QR via mobile app if it’s a door-closed fridge. In the case of a usual shelf, cameras can track what’s grabbed from it to initiate the session.
Creating a virtual shopping cart. As the person scans the QR code, it’s a signal for a system to create a shopping cart for this specific user.
Product recognition. The cameras might be installed inside or outside of the vending machine. The internal cameras should be able to track the taken/put back products. External cameras might track manipulations within an open fridge, just like with a regular shelf. Both types of cameras capture the products and put them into a shopping cart.
As the person might examine multiple items and move from side to side, CV cameras can also track the person in the frame. This will help us verify that it’s a single person making a purchase, and not another one standing nearby.
Verifying products. When the product is taken, the system sends this data to compare the image of the product with the one in the database and extract the price. Additionally, we can update availability automatically in our inventory management system.
Editing product list. Once the products are taken, they will be sent to the user’s shopping cart available on their smartphone, or tablet on the fridge. Here, the customer can modify items, and proceed to the payment.
Checkout. In case of a mobile application and QR code scanning, closing the fridge might be a trigger point to complete a purchase and charge a sum from a digital wallet. But, there might also be a POS terminal installed to allow credit card payment. At this point, the purchase is done, and the person can leave the store.
While it looks like a relatively weak alternative to the autonomous checkout system, vending machines can be scaled easily to automate the whole store. Which makes a little difference in terms of customer experience, but requires less engineering effort and budgeting.
The same concept of modular automation can be applied to numerous other cases. Except for supermarkets and grocery stores, computer-vision kiosks can also be installed in food service venues or coffee shops.
Checkout free food service
Restaurants, cafes, and canteens often use a buffet serving system like a sideboard with portioned dishes customers can choose from. Customers place dishes on trays, then need to check out their order, which can potentially be handled by a computer vision kiosk.
A machine learning model sitting on the backend can be trained to recognize dishes and other products placed on the tray to launch the checkout process. This idea can be implemented as a checkout kiosk where a set of cameras will scan the order. The actual payment can be completed via a usual POS terminal, or using a mobile application and a digital wallet.
The concept of cashierless operations can be taken to extremes like with Starbucks. Using Amazon’s system, Starbucks became the first of a kind grab & go coffee shop. Customers can place an order via a mobile application and come for their coffee without any checkout similar to Amazon GO. However, handling computer vision projects requires knowledge of a subject matter. Specifically, data science and machine learning expertise.
So now let’s talk a bit of what you should know to approach computer vision-based checkout automation.
How to approach AI-based checkout?
Based on our experience, let’s examine the steps it takes to create a computer vision system for automation in retail. We’ll focus on the smart fridge case as the most approachable and versatile one.
First of all we need to understand our business case in detail:
Preferred automation method. Choosing between smart fridges or other types of dispenser machines might require less global modifications to the store, while maintaining a scalable approach. Full store automation will mostly require changes to the venue layout, and additional hardware like turnstiles, which can be a con for the majority of the store owners.
Store size. Vending machines can be installed in basically any number, to cover all of the store’s inventory and product diversity. So the store size will determine how many vending machines you’ll need, and what will be the store layout using smart fridges for some part of products.
Quantity of products for recognition. As any other machine learning project, a computer vision system requires training before it can recognize anything. A single fridge might contain 20 to 50 different products. So we should consider those numbers as it will determine how long the training phase will take.
Existing infrastructure. In most cases, physical stores don’t have enough integration between inventory management, point of sale, and accounting. Although, computer vision systems will require access to the store data to automate sales updates and product availability. So examining your existing infrastructure is another point to understand when considering the requirements of this project.
So let’s say a single fridge can contain 35 items and we’ll focus on those numbers.
Computer vision is an artificial intelligence technology. Which means, we need data so it can recognize objects. The data is used for model training to identify different products in the frame, as well as identify people and what they grab.
The optimal way to collect data for object recognition is basically to record each product on video from different angles and lightning conditions. It is important to have these videos categorized by product, so the labeling (what product is in the frame) will be done automatically. General recommendations for gathering the data are that it should be as close as possible to how it will look for real users.
Once we implement a working model to automate checkout, we’ll need 60 frames per second. This is required to guarantee fast operation of the model. The higher the frame rate, the smoother the image is, and the more detail we can extract from it.
The next step is training. Once we collect all the video recordings, a machine learning expert will prepare them for model training. This process can be split into two tasks.
- Preparing data means we need to split all the video frames into separate images, and label the products we need to detect. Put simply, we extract 60 photos out of a minute long video, and draw bounding boxes around our target objects.
- Choosing an algorithm. An algorithm is a mathematical model that learns patterns from the given data to make predictions. For tasks like object recognition, there are existing working algorithms that can be applied for building a model. So our task here is to choose a suitable one, and feed it with our data.
The process of training may take several weeks, as we struggle to get decent accuracy.
If any products are added or swapped in the process, the model needs to be retrained. This is because prediction results will differ depending on the data input. This means that each time a store obtains new items for sales, and places them into a computer vision fridge — we’ll need to launch a new training phase for the model to learn new items.
Given that, we’ll need retraining to recognize, say, Pringles cans on the image if there weren’t any Pringles before. Although, this becomes easier as soon as we implement cameras in the fridge because we can use live recordings to make annotations and launch training again.
The existing infrastructure in the store is usually represented by a server that processes inventory updates, and records sales volume via POS terminals. To implement a machine learning model, we’ll need to add several components:
- Cameras to record and pass the visual data.
- Video processing unit. This can be a video card or a single board computer like the Nvidia Jetson that includes a GPU optimized for computer vision needs.
- QR scanner. This sticker is placed on a turnstile, or a fridge the user scans to identify the person and launch the shopping process.
- Model server. As we’re talking about real time video processing, implementing a hardware server at the store will guarantee more stable results. Basically, as a person grabs something from a fridge, the reaction of the system should be noteless so that hardware components can respond fast enough.
All of those components should be interconnected, as there has to be data flow between each unit. As for the cameras, we also want to make sure the store has a stable and fast bandwidth. Since cameras will process live streams of data in the real time, there has to be no delay for the model to function properly. On the other hand, the customer will expect a fast reaction of the vending machine, which depends on how quickly the model receives and processes the data.
Among other questions that might concern both retailers and customers is privacy. Since computer vision is designed to detect and track objects on video, recording and storing such data may violate the privacy laws in some countries.
Although, in the US it’s generally legal to use surveillance cameras in stores. As long as customers are tracked with random IDs just for the sake of the checkout task, no other technologies like face recognition are required. And even if the camera captures a person’s face it could be blurred using AI to sustain confidentiality.
Is AI self-checkout for every retailer?
All with all systems, autonomous checkout may seem like a pricey and bulky thing to implement. Customers are still willing to use more convenient checkout methods, however. That’s noted in a Retail Customer Experience report 2021 that 60% of consumers would choose self-checkout over interaction with a cashier.
That being said, vending machines might be an affordable option for the retail industry, as it brings a lot of benefits for a reasonable cost. Additionally, such systems can be customized to serve the specific needs of a given retailer due to flexibility of machine learning models. Basically, any type of product can be recognized with proper training. So convenience stores are not the only ones who can benefit from computer vision applications.
Want to start implementing AI-driven autonomous checkout? Contact our AI team to discuss your project.
AI-powered self-checkout solutions use advanced algorithms to analyze images of products scanned by customers in order to identify them and calculate the total cost of their purchase. These systems are able to detect errors such as incorrect item selection or incorrect billing amount almost instantly.How do supermarket self-checkouts work? ›
It's simple. Self-service machines are only really different from traditional checkout tills in that they aren't staffed; customers themselves perform the checkout process. Customers in a grocery store or other retail business use a barcode scanner to scan the items they want to buy at a point of sale (or POS).Does Amazon go store use AI? ›
Dubbed Amazon Go, shoppers could walk in, pick up items and leave without going to checkout. An intuitive system of cameras and AI would determine what they purchased and charge it to their card.How much does it cost to implement self-checkout machines? ›
According to a study by MIT, a typical 4-lane self-service checkout setup costs $125,000, although an open-source platform is available that can reduce the costs by nearly 10 times.How do I use Walmart self-checkout? ›
Select your payment method.
All self-checkout registers accept cash, but they also accept Walmart gift cards, most major credit cards, debit cards, and EBT cards. If using a check, go through a regular checkout lane, since there is no option for check payment at the self-checkout.
The retail giant uses a chatbot developed by Mountain View, California-based Pactum AI Inc., whose software helps large companies automate vendor negotiations. Walmart tells the software its budgets and needs. Then the AI, rather than a buying team, communicates with human sellers to close each deal.How can AI be used in supermarkets? ›
With AI, grocery stores can analyze data on customer purchasing habits and use it to predict which products will be in high demand. This helps stores to order the right amount of products and avoid overstocking, which can lead to waste.How retailers can use AI? ›
Artificial Intelligence can help retailers improve their customer experience by providing personalized recommendations and improving inventory management. It can also help retailers optimize their pricing strategies and reduce costs by automating tasks such as demand forecasting and supply chain management.What is one disadvantage of using a supermarket using self-service checkouts? ›
The most notorious issue with self-checkouts is an increased risk of theft. This is specific to retail stores and has become a problem in large grocery stores and general merchandise retailers like Target and Walmart.
While retailers claim to install them to improve customer service, we also know these machines can help them redeploy their cashiers. A typical four-lane setup costs $125,000* to install - they certainly don't come cheap!
High customer engagement: buyers need to scan product barcodes and place products into a bagging area, and sometimes even weigh products. An obvious disadvantage of self-checkout in general is shoplifting. A store that has 55 -60% of transactions going through self-checkout can expect losses to be 31% higher.What is Amazon AI platform called? ›
Amazon Lex is a fully managed artificial intelligence (AI) service to design, build, test, and deploy conversational interfaces into any application using voice and text.What is Amazon's AI called? ›
Amazon Lex enables developers to build chatbots using conversational AI in applications. It uses automatic speech recognition to convert speech to text and natural language processing (NLP) to understand spoken instruction.What is the Amazon AI system called? ›
Amazon (AMZN) said that it will provide artificial intelligence (AI) language models, called Amazon Bedrock, through its AWS platform.What is the future of self-checkout? ›
Self-Checkouts are not new to the retail sector, and decades of innovation and implementation of technology has seen the usage increase year on year. In 2021 the industry saw a significant 11% growth, and yet this is low compared to the forecasted 13.3% compound annual growth rate predicted through to 2030.How long does it take to install a self-checkout kiosk? ›
How long does it take to install a kiosk? While the physical installation of a tablet kiosk generally only takes an hour or so, planning and executing a self-service kiosk project often takes several months.Do stores save money with self-checkout? ›
One of the biggest benefits of self-checkout kiosks is that they save you money on labor costs. With a self-checkout option, one cashier can oversee six transactions at once. You don't need as many cashiers to fully staff your checkout lanes — but you don't have to sacrifice efficient customer service, either.Why is Walmart only doing self-checkout? ›
Yes, it's likely that Walmart will save money by going to cashier-less checkouts, but the primary reason for the change, according to Walmart, is to speed up checkout times, give customers more choice, and give them more control over their shopping experience.Can you only use Walmart pay at self-checkout? ›
Walmart Pay works at both staffed and self-checkout registers and you can use it at any point in a transaction – before, while or after you scan your items.Can you use the Walmart app for self-checkout? ›
Then, hit the blue Check Out button in the app and head over to a self-checkout machine. Scan the QR code on the screen of the self-checkout machine. Confirm your payment method.
In 2022, Amazon closed its divide in terms of total revenue, as it generated over $513 billion in revenue, compared to over $572 billion in revenue from Walmart.What is the new AI technology at Walmart? ›
Walmart's shopping app also uses AI technology
This is not the first time Walmart has embraced artificial intelligence. The company rolled out a new text-to-shop tool in December 2022 that allows customers to communicate with a robot and to text the retailer the names of products they want to purchase.
- Improving customer service.
- Providing product recommendations.
- Segmenting audiences.
- Analyzing customer satisfaction.
- Identifying fraud.
- Optimizing supply chain operations.
Discover the leading AI companies in the food industry
Amongst the leading vendors of the AI food industry are AVEVA, Black Swan Data, Blue Yonder, C3.ai, Dorabot, Eversight, Futurmaster, HelloAva, ImpactVision, and Journey Foods.
- Demand forecasting. Predicting demand is so important for retailers because it informs all other retail planning functions. ...
- Personalized shopping. ...
- Supply chain optimization. ...
- Inventory management. ...
- Customer service.
Grocery store automation can also help prevent stock shortages and delays. The more you automate a store, the more data you'll collect on its operations. Automated warehouse or restocking solutions provide information about stock levels. You can then create more timely and accurate ordering schedules.What is an example of an AI shopping system? ›
Amazon's Purchase Recommendations and Voice Shopping
Additionally, the company's checkout-free grocery store, Amazon Go, uses AI to distinguish the items being chosen as well as track and estimate customer behavior. It has active U.S. locations in Chicago, New York, San Francisco and Seattle.
Common examples of automation in the retail industry include ERP for billing, chatbots for answering service queries, automated checkouts, automated email campaigns, POS (point of sale) software, and inventory software.What is an example of generative AI retail? ›
Generative AI would allow retailers to generate personalized product images for each customer simply based on text descriptions and historical image data. For example, an athletic apparel retailer could automatically generate an image of a college student wearing a sports jersey for a 19-year-old customer.Why don t more people use self-checkout? ›
The machines are expensive to install, often break down and can lead to customers purchasing fewer items. Stores also incur higher losses and more shoplifting at self-checkouts than at traditional checkout lanes with human cashiers.
- Confusing navigation. Self-service platforms should be simple and intuitive to navigate across channels. ...
- Lack of attention. ...
- Inflexibility. ...
- Doesn't incorporate feedback. ...
- Constrains users. ...
- Creates extra work. ...
- Lacks human interaction. ...
- Makes personalization difficult.
Self-checkout allows employees to maintain adequate social distance from their customers, and allows for more checkouts without adding more employees to your store's capacity. Self-checkout is the most efficient way to stay safe, while also boosting sales.Will self-checkout replace cashiers? ›
Self-checkouts cause some people to lose their jobs, but most cashiers who are replaced by self-checkout are simply offered a position doing something else. The cashier position is one of many roles available in a supermarket, and cashiers are often asked to perform other types of customer assistance instead.What percentage of people like self-checkout? ›
What percentage of shoppers prefer using self-checkout over traditional cashier-assisted checkout? Approximately 73% of shoppers prefer using self-checkout.Why doesn t Walmart self-checkout take cash? ›
This was largely forced by the change (coin) shortage. This is not really a lack of coins but due to how money is handled and stored and the impact of COVID lock downs the standard circulation of coins was disrupted and as things opened back up there was a shortage of available coins.Are self-checkouts ethical? ›
Even from a consumer standpoint, the ethics behind self-checkout technology is questionable. Disguised as a customer service initiative, the machines transfer the cost of paid labour onto paying customers. “They aren't there to benefit shoppers,” Tattrie says.What is the main reason why retailers are adopting self-service checkout systems? ›
Self-checkout kiosks allow overflow check-out traffic to be automated, keeping stores from having too many staff on-hand past peak times, or requiring customers to wait in long, frustrating lines due to a lack of available staff to assist them.Why are retailers going to self-checkout? ›
Self-checkout lanes are becoming more popular due to social distancing measures sparked by the pandemic and the availability of automation technology, the firm said. A few retailers, such as Walmart, Kroger and Dollar General, have even started testing self-checkout-only stores, per CNN reporting cited by the firm.What is Google's AI machine called? ›
That is the Shakespearean question an Associated Press reporter sought to answer while testing out Google's artificially intelligent chatbot. The recently rolled-out bot dubbed Bard is the internet search giant's answer to the ChatGPT tool that Microsoft has been melding into its Bing search engine and other software.What is Amazon bot? ›
Amazonbot is Amazon's web crawler used to improve our services, such as enabling Alexa to answer even more questions for customers. Amazonbot respects standard robots.
Amazon is the AI stock to watch
Amazon is a leader in both e-commerce and its cloud computing business. The use of AI could further cement its position in these industries. In e-commerce, AI is already transforming customer experience and improving operational efficiency.
Since then, people have been far from blown away by Siri and competing assistants that are powered by artificial intelligence, like Amazon's Alexa and Google Assistant.Is Alexa considered AI? ›
Are Alexa and Siri considered AI? Yes. Alexa and Siri are applications powered by artificial intelligence. They rely on natural language processing and machine learning, two subsets of AI, to improve performance over time.What is Google's new AI? ›
The generative AI tool lets you “try on” clothes from hundreds of brands by displaying them on models of a wide array of sizes and skin colors.How does Netflix use AI? ›
It provides users with personalized movie suggestions. To accomplish this, Netflix employs ML/AI/Data to analyze a specific user's watch history and compare it to the movie preferences of others with similar movie tastes. As a result, Netflix has the best selection of shows and movies that you might enjoy watching.Does Amazon have a chatbot? ›
You can customize your caller's experience with the Amazon Lex chatbot, to fetch information from a scheduling system or database with customer or account information.How does Google use AI? ›
The AI behind Google Maps analyzes data to provide up-to-date information about traffic conditions and delays — sometimes helping you avoid a traffic jam altogether. It also automatically updates things like business hours and speed limits so you can see the latest information about your world every single day.When was self-checkout implemented? ›
The first self-service checkout machines then called automated checkout machines or ACM, were set up in a Kroger store in Atlanta in July 1986. One of the hottest innovations in retail had been born.How is checkout process done? ›
- Initiate checkout. Checkout begins when the customer leaves the shopping cart to proceed to checkout. ...
- (Optional) login or signup. ...
- Billing information. ...
- Shipping information. ...
- Shipping method. ...
- Preview order. ...
- Payment confirmation.
There are several types of self checkout systems out there. Smart carts and mobile scanners, a portable self checkout system where users scan items while they shop. RFID scanners — users walk through a gate that scans items packaged with RFID tags, and then simply have to pay. No checkout necessary.
The checkout process is a series of steps that a consumer must go through when purchasing items on an online shopping platform. It consists of several phases, including applying promotions and coupon codes, calculating taxes, selecting shipping and payment methods, and so on.Is Walmart self-checkout only? ›
If you prefer checking out with a cashier, we will continue to have that option available for customers who prefer that method. Additionally, Walmart+ members have the option to use our mobile Scan & Go feature, which gives you a contact-free checkout experience using your phone to scan items as you shop.Why does Walmart have self-checkout? ›
Walmart first tested self-checkout in the late 1990s. “The rationale was economics based, and not focused on the customer,” Charlebois said. “From the get go, customers detested them.”Does America have self-checkouts? ›
When the first self-checkout kiosks were rolled out in American stores more than three decades ago, they were presented as technology that could help stores cut costs, save customers time, and even prevent theft.What is automated checkout? ›
Also called a "self-scanning checkout," customers pay for and bag their own merchandise without interacting with a human cashier, although a support person is typically nearby and available.How does Google Checkout work? ›
Once a customer has set up their account, they simply click on the Google Checkout button on a merchant's checkout page and voila – their transaction is processed. Google collects all the customer payments and delivers them to merchants on a weekly or daily basis – your choice.Is self-checkout considered automation? ›
I'm not just put off by the technology as a customer but also as a professor who specializes in the field of automation, and for one particular reason: even though self-checkouts are labeled as “automation,” they're actually not.How does smart checkout work? ›
There is no need to download an app or find and scan barcodes: shoppers simply put items down, pay as they normally would and are on their way in as little as 10 seconds – eight times faster than traditional self-checkout.”What is the difference between checkout and cart? ›
The most important takeaway here is that shopping carts are better suited for robust, product-based e-commerce shopping experiences, while checkout pages are better suited for minimal, solution or service-based e-commerce purchases.How does checkout Free technology work? ›
Checkout free systems are cashierless checkout systems that detect shoppers' product preferences and bill them when they close their shopping trip. QR codes, smart shopping carts, RFID tags, and machine vision are the solutions that process transactions, invoices and billing.