Ecommerce is becoming more and more popular these days! After all, there’s nothing more convenient than ordering something online and just getting it at your home in a few days. And if you don’t like it, you can always return it. While online shopping has all these advantages, it doesn’t mean that shopping at real-world stores has become any less popular. There’s nothing better than seeing what you are buying first hand, and getting a real-life human experience of shopping. In fact, there is some satisfaction in shopping at a large store that is just not there online. However, the retail sector can be improved a lot than its current form to provide a better experience for customers. And what better way than to use Computer Vision for this?
Computer vision is a very complex field that allows computers to obtain information from images or videos. This is a multidisciplinary field that combines Artificial Intelligence and Machine Learning to analyze images and obtain insights. Now imagine the applications of computer vision in retail. There are already a lot of cameras in a retail store and using them along with machine learning can enhance the customer experience in ways that were not possible until now. So let’s see some of these applications of computer vision in the retail sector.
1. Automated Payments
What if you could enter a store, pick up something you want to buy, and just leave?!! You don’t need to pay at the till and you don’t need to stand in line to wait for the cashier. And don’t worry, you are not stealing the item either. The amount for whatever you have picked will be automatically deducted from your bank account. This sounds impossible and far-fetched but it could actually become a reality using computer vision. Stores could monitor all their products using a combination of sensors and computer vision so that they know when a product is picked up. They can also recognize the customer who has selected the product and automatically deduct the money from the customer after they leave the store. This seems to be a hi-tech fantasy than reality but it is already available in the AmazonGo stores in America. Automated payments will help in reducing the customer bottlenecks and lines at cashiers, especially during busy times like the holiday season.
2. Customer Data Management
When you go to a store where you have previously gone, chances are that they have your information like purchase history. And if you are an exclusive member, then obviously they have more details about you. Most retailers collect extensive customer data to make sure that they know their customers and what they want so that they can have targeted advertisements, more stock in the inventory for popular products, etc. But this collection of customer data can be much enhanced using computer vision. Retailers can use it to identify different customer demographics and their distinct purchase patterns and then provide targeted advertising to gain more insights. Retailers can also understand the products in their stores that catch the customer’s eyes more often and then create the stores in such a way to provide maximum visibility to these products. For example, they could provide the popular items at eye level in the store and shove the other items at the highest or lowest shelves.
3. In-Store Advertising
The world is filled with advertising these days! Anywhere you go, where it be hoardings or metro pillars, all you see are ads. In fact, this is true for the online world as well, whether it be Facebook, Instagram, YouTube, etc. But what about in-store advertising? That’s right, retailers also advertise themselves in-store but it is much more subtle. For example, you may enter a certain store and automatically get a message with a 15% discount from that store? How did that happen? Magic?!! No, it’s a technology called geofencing where retailers mark a geographical boundary and customers automatically get some messages when they enter this boundary. Computer vision can be used to enhance geofencing so that certain customers are identified when they enter a store and they get special discounts. Or they can also get suggestions about what products to buy depending on their previous purchase history. The options really are endless!
4. Personalized Service
Suppose that you are a regular customer at a store. It would be great if you could get special discounts or some perks. Wouldn’t that make it more likely that you would visit the store again? This brand loyalty is a great idea for stores to retain their customers and help improve their bottom line! But how to do this easily? Computer vision can help a lot with this. It can help stores to instantly identify their regular customers using facial recognition and provide them discounts or free items. After all, there are so many cameras already in a store so why not put them to good use! In addition to this, computer vision can also help a store to understand the type of customers they service and what each demographic of customers actually want. Then they can provide discounts or lucrative offers on popular products and gain even more loyal customer following. And this will increase their long term profit as well.
5. Theft Prevention
What is a major concern for retail stores that is not faced so often by Ecommerce sites? It’s theft of course! There are hundreds of cases of shoplifting in stores all over the world, a problem that is easily avoided online. After all, thieves cannot exactly walk out of an eCommerce site without paying for the product! Most retail stores are monitored by cameras as well as security personnel to ensure that nothing is stolen. But these are not full proof methods especially during busier times when it is easy to shoplift. Computer vision can be a big help here. Machine Learning algorithms can automatically monitor a store via the cameras with computer vision and alert a security guard if there is any theft occurring. These algorithms can also be trained to identify repeat offenders and keep an eye on them so they don’t steal anything again. All in all, they can greatly reduce the number of thefts that occur in retail stores all over the world.
6. Shelf Management
It is very important for retailers to know what they have on their shelves and what products are selling out the fastest. After all, it’s never a good idea when a customer wants a product and they see the shelves are empty and the product is sold out! This can be prevented using computer vision. Retails can keep a real-time eye on their products and immediately be notified if any products are running low or they are out of stock. In this way, shelf management is much easier and faster than if employees were occasionally checking the shelves and manually restocking any products that were running low. Apart from just checking the stock levels on the shelves, computer vision in shelf management can even help retailers understand how they should organize their shelves. Which brands of similar products they should keep next to each other and so on. All these things don’t sound important but they make a big difference in the psyche of the customers and can ultimately increase the profits for the retailers.
7. Employee Performance
Whatever advances are possible because of machines, there is no doubt that employees are a critical factor in the retail sector. After all, customer service is the most important thing and it can make or break the business of any retailer. This is the reason that computer vision can also play a very important role in employee performance to understand how the employees are providing customer service. Machine Learning Algorithms can be used in conjugation with security cameras to understand the level of satisfaction of the customers in response to the employee performance and the wrestling feedback can help improve the employees. Which will only help in improving the overall reputation of the store.
8. Automated checkout:
Automated checkout is a technology that uses computer vision and machine learning algorithms to enable customers to scan and pay for their purchases without the need for a cashier or traditional checkout line. Automated checkout systems typically use a combination of sensors, cameras, and software to identify and track items as they are added to a customer’s shopping cart, and then automatically charge the customer for their purchases when they leave the store.
One of the key benefits of automated checkout systems is that they can save time for customers by eliminating the need to wait in line at a traditional checkout. This can help to improve the overall shopping experience and reduce frustration for customers, particularly during peak shopping periods.
Automated checkout systems can also be more efficient for retailers, as they reduce the need for cashiers and can help to minimize errors in pricing or inventory management. Additionally, automated checkout systems can help retailers to gather more data about customer behavior and purchasing patterns, which can be used to improve marketing and promotional efforts.
There are several different approaches to automated checkout, including stationary systems that require customers to place their items on a conveyor belt, and mobile systems that use handheld scanners or smartphone apps. Some retailers are also experimenting with checkout-free stores, where customers simply walk out of the store with their purchases and are automatically charged for them.
9. Inventory management:
Inventory management is a critical function for retailers, as it involves the tracking and control of inventory levels to ensure that products are available when customers want them. Inventory management can be a complex process, particularly for retailers with large product catalogs or multiple store locations. Computer vision technology is being used to simplify and streamline the inventory management process, making it more efficient and accurate.
One way that computer vision is being used for inventory management is through the use of automated systems that can monitor inventory levels in real time. These systems use cameras and sensors to track the movement of products within a store, and can automatically update inventory levels when products are sold or restocked. This can help retailers to avoid stockouts and overstocks, which can result in lost sales and excess inventory costs.
Computer vision is also being used to improve the accuracy of inventory data by automating the process of product recognition. Computer vision algorithms can analyze product images to identify specific products, even if they are partially obscured or presented in different orientations. This can help to ensure that inventory data is accurate and up-to-date, which is critical for effective inventory management.
Another way that computer vision is being used for inventory management is through the use of predictive analytics. By analyzing historical sales data and other relevant factors, computer vision algorithms can make predictions about future demand for specific products. This can help retailers to optimize their inventory levels and ensure that they have the right products in stock at the right time.
10. Loss prevention
Loss prevention is a critical concern for retailers, as theft and other forms of retail shrinkage can result in significant financial losses. Computer vision technology is being used to help retailers prevent losses and reduce the incidence of theft and other forms of shrinkage.
One way that computer vision is being used for loss prevention is through the use of video surveillance systems. These systems use cameras to monitor activity within a store and can alert security personnel if suspicious behavior is detected. Computer vision algorithms can be used to analyze video footage and identify unusual activity, such as people moving in unusual patterns, or products being moved to unusual locations.
Computer vision technology can also be used to identify potential shoplifters. For example, computer vision algorithms can analyze video footage to identify people who are carrying large bags or backpacks, or who are wearing loose clothing that could be used to conceal stolen items. Retailers can then take steps to prevent theft, such as increasing security in certain areas of the store or approaching potential shoplifters to deter them from stealing.
In addition to theft prevention, computer vision can also be used to prevent other forms of retail shrinkage, such as errors in pricing or inventory management. For example, computer vision algorithms can be used to monitor pricing and ensure that products are being sold at the correct price. This can help to prevent errors that could result in lost revenue or increased costs.
11. Customer engagement:
Customer engagement is a critical aspect of the retail industry, as it involves building a relationship with customers and creating a positive shopping experience. Computer vision technology is being used to enhance customer engagement by providing personalized recommendations and interactive experiences.
One way that computer vision is being used for customer engagement is through the use of personalized recommendations. Computer vision algorithms can analyze customer data, such as past purchases and browsing history, to make personalized product recommendations. This can help to improve the shopping experience for customers by suggesting products that are relevant to their interests and needs.
Computer vision technology can also be used to create interactive experiences that engage customers and make shopping more fun and engaging. For example, some retailers are using augmented reality technology to create interactive product displays that allow customers to see how products will look in their homes before making a purchase. Others are using virtual reality technology to create immersive shopping experiences that allow customers to explore products and interact with them in a virtual environment.
Another way that computer vision is being used for customer engagement is through the use of interactive displays and kiosks. These displays can use computer vision technology to detect customer movements and gestures, allowing customers to interact with the display in a natural and intuitive way. This can help to create a more engaging and interactive shopping experience and can help to keep customers in the store for longer periods of time.
Conclusion: All of these applications of computer vision in the retail sector show the advantages of integrating technology in fields that it was never used in. After all, who would have thought that shopping might require sophisticated techs like machine learning and computer vision but it can help in many ways like automating payments, in-store advertising, theft prevention, customer data management, etc? In turn, all these advancements allow companies to provide a better retail experience to their customers and also increase their profits. It’s a win-win situation for all!
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