Businesses around the world were rocked two years ago by the beginning of the COVID-19 pandemic. However, with disruption comes great innovation in both physical and digital storefronts. It’s important to consider how technological advancements in the retail industry can affect large enterprises and smaller businesses alike.
In addition to selling products in brick and mortar stores, many storefronts are now offering buy online pick up in store (BOPIS) services, same day delivery, and online shipping. With so many new ways to order goods, point of sale systems need to be updated in an intelligent manner. When a guest orders a product online, how does that affect the store’s sales floor quantity in the database? If a product is on hold for a pick up order, does that reduce the count on the sales floor? These are all questions a modern POS system must be able to answer.
Not all businesses were able to survive the shift in emphasis to digital retail options. Microsoft’s physical stores were shut down around the country. However, those who were able to adapt to these changes, like Walmart and Target, were able to thrive.
The key to this is more than just having systems in place to handle both online and in-store purchases, but a unified and connected system that integrates with other technologies the business uses. Integrating online and offline transactions, inventory, and promotions across all in-person locations and online stores are some of the greatest hurdles that software engineers seek to overcome with developing PoS systems for retail businesses.
Indoor positioning systems (IPS) have been a trend of interest for a few years now for helping users navigate indoor areas where GPS is not available or not accurate enough. However, there are many more applications for this technology than first meet the eye.
On the surface, indoor navigation does have a great deal of potential for user navigation that has been realized in stores, office buildings, airports, and hospitals. Hong Kong International Airport uses this technology in their “HKG My Flight” mobile application. In the retail industry, Lowes and Target are utilizing this technology as well.
The challenge comes from implementing in-store navigation solutions for your business. The first step is hardware. Having the right infrastructure in place is essential, whether it be Bluetooth beacons, Wi-Fi RTT, or ultra wideband (UWB). Target chose to implement Bluetooth IoT lighting systems at many of its locations. When guests shop with the Target app on their phone in stores, they can get access to a map that helps them find their position in the store and find the items that they need.
This particular solution placed the Bluetooth beacons in the lighting systems above the sales floor. However, other solutions may be possible based on the layout and size of the store. After the infrastructure is set up, all that’s left is to develop the software that can take advantage of it.
IPS systems have much more potential than just helping guests shop. Having data on where guests shop in the store has three beneficial implications: targeted suggestions, tracking customer traffic, and item tracking.
When guests are in a particular part of the store using the store’s official app, the IPS system can track where they are and offer them targeted suggestions on products and deals on their smartphone. This can improve conversions as guests shop.
Perhaps more profoundly, data on where shoppers are in the store can lend itself useful for improving the placement of products on shelves. Where do guests go when they enter the store? Do they tend to prefer one location in the store over another? Are there any locations in the store that guests tend to avoid? These are all questions that IPS tracking can answer. Of course, anonymized data is important to protect user privacy, but these analytics could be crucial for helping business owners make decisions on where to place certain products.
In fact, this data could be a valuable bargaining chip when negotiating with vendors on how much they should pay to have their product included in a particular high-traffic area of the store.
RFID technology used for tracking down products in retail locations is useful but relatively short range. As these technologies improve, IPS can offer more detailed data on the locations of products on the sales floor. This can help not only to improve the efforts of assets protection but it can also make it easier to return moved items to their correct locations.
IoT devices are becoming smaller and cheaper as time goes on, but for right now this technology may need to be limited to a few high-value items if it’s to be implemented. It can also be used to help track work equipment like personal devices.
For tasks like order fulfillment, retail workers can use their work devices to find locations in the store using IPS maps to find the item’s location. If the item has IPS tracking set up, they could navigate to the item instead. This would be useful in instances where the item has been moved from its original location by mistake.
Augmented reality shopping experiences are becoming the new norm in 2022. With virtual fitting rooms, enhanced in-store AR navigation, and other AR experiences spreading across the market, those without these features are falling behind. This is one of the key technological advancements bridging the gap between digital ecommerce storefronts and brick and mortar shopping.
Some of the most useful applications of VR that help digital customers in the retail industry are the ‘try before you buy’ applications. One of the best examples of this is virtual fitting room technology. By using AR technologies, shoppers can see how they look when trying on different kinds of products on themselves. Similarly, they can also see what other kinds of products look like, such as furniture. Virtual fitting room technology is already being implemented by stores like Sephora, Target, Ikea, and more.
In fact, Ikea’s version of this lets you do much more than just see the size and shape of furniture. Ikea Studio can use the unique LiDAR technology on iPhones to display rooms, measurements, windows, doorways, and more to visualize interior design like never before.
Although indoor positioning was already a trend we discussed, when combined with AR it can enrich the experiences of customers and workers even further. Augmented reality-based indoor navigation technology can help shoppers find the items that they need in store just by following directions on their phone’s screen. This can be very engaging for shoppers if implemented correctly and can provide for more opportunities to show guests targeted suggestions on-screen.
AR navigation can help retail workers too. Some of the most popular devices for workers in the industry, Zebra’s TC52, TC57, and TC77 portable computers are ARCore-capable. With the potential of these devices to use their cameras for AR navigation, order fulfillment workers can simply look for directions on their screen to find the aisle that they’re looking for.
Augmented reality developers can help retail workers visualize shelves before they are set up. By using a planogram as a base, AR-enabled enterprise devices can display a 3D planogram on the sales floor to serve as a guide for the setup process. This can make it easier for workers to set pegs, shelves, and fixtures in their appropriate locations.
Augmented reality could also help workers identify problems with a shelf. Combining AR with AI-based object detection technology, a worker could hold their device’s camera up to a shelf to find areas for improvement. By comparing the shelf with its corresponding planogram, AR rendering can show the worker which parts of the shelf do not align with the planogram and need to be fixed. This could be out of place product, improperly zoned items, and even identification of damaged products.
In nearly every one of the previous trends that have been discussed so far, artificial intelligence has played a role in some fashion. Modern AR is powered by AI scene analysis. However, AI can serve much more benefit in the retail industry than this. AI can be used in inventory management and other kinds of analysis on consumer behavior.
The retail industry is driven by computers and rich databases more than ever before in 2022. However, the complexity of these systems has resulted in a great deal of inefficiencies from errors. Retail workers may find that the system has incorrect sales floor quantities of certain SKUs. Tackling these issues is critical to saving time and money for businesses.
Artificial intelligence can help us manage these inventories more effectively with a number of different methods. For example, AI can measure consumer spending analytics to predict when certain types of items may be more likely to move around on the sales floor. This can drive the system to require manual auditing of certain areas over others. While auditing the entire store is a challenging task, auditing only the areas that need attention is much more effective.
Amazon takes it a step further with its Amazon Go Grocery model stores powered by Just Walk Out. The project uses computer vision, sensor fusion, and deep learning. The store’s rich network of cameras and IoT sensors can detect when a consumer has taken an item off the shelf and placed it into their cart. When the guest leaves the store, their credit card is charged for the items that they took.
This requires not only a deep understanding of what the guest has placed into their cart but also where items are in the store. If an item moves away from its shelf, the store must still be able to keep track of how much of it that it has on hand.
If a more advanced network of cameras isn’t the solution, perhaps it can be done manually while still using machine learning algorithms. Instead of auditing empty spots on a shelf one by one, a retail worker could take a photo of each shelf section with their device. The image could be compared to a planogram with object recognition deep learning software to see which items are missing, speeding up the process greatly.
Earlier we touched on this with analysis of in-store foot traffic with indoor positioning systems. However, the potential of analyzing consumer behavior doesn’t stop there. In addition to their spending habits, we can measure their interest in what they’re spending money on alongside their demographics with artificial intelligence. This can give businesses a better idea of how they should be marketing to their audiences.
The focus so far has been analyzing consumers that are already in the store, but what about drawing in new ones? A store using Kimola’s platform decided to remodel a store for soccer fans using data analyzed by AI. By using social media accounts as a base for the data, the algorithm was able to determine the most optimal location for the store.
This idea extends to e-commerce as well. A user’s behavior can be tracked while they visit an online storefront. What items their cursor hovers over, how much time they spend on a particular page, as well as where they come from. This type of information is familiar to any web marketer using Google Analytics.
However, behind that technology is powerful artificial intelligence that can search for connections and trends faster than we can on our own.
This is a powerful tool used by some of the largest brands in the world. A few years ago it became apparent that many businesses weren’t ready for the sudden panic buying that resulted from the beginning of the COVID-19 pandemic. As products flew off the shelves, stores that were able to adapt and get their hands on high-demand items were able to thrive.
To evolve to serve the needs of the public during the pandemic, demand forecasting powered by machine learning took off. Amazon is again another example of a company that leverages the power of machine learning for this purpose. This technology can also improve inventory planning, relationship management for both customers and suppliers, logistics, manufacturing, and marketing.
Demand forecasting can also lead to more sustainable consumption and production. When demand is predicted much more accurately, items can be produced and ordered only according to how much is needed by the consumer.
ML-based demand forecasting approaches are much more versatile and adaptive than their traditional counterparts. Since machine learning can be implemented much more quickly, it can better follow customer demand trends.
Read the case study to see an example of how machine learning-based sales prediction feature was implemented in the ERP system.
Two years into the pandemic, demand forecasting technology continues to thrive. With ever shifting customer behaviors, it’s more important than ever that your business is deeply interested in data on customer spending trends. Technologies to analyze that data to get meaningful insights are just as important for maintaining a competitive foothold in the market.
Closely related to artificial intelligence is the hardware of robotics. This has a multitude of applications, such as delivery, inventory management, and customer service.
As artificial intelligence improves, so too have autonomous vehicles. Delivery is evolving in 2022 and autonomous delivery is becoming the new norm. The Safeway cart, developed by Tortoise, was introduced last year as an autonomous delivery vehicle. Serve Robotics, formerly called Postmates X, is also making a delivery robot for Uber. With Grubhub successfully rolling out autonomous food delivery robots to college campuses like the Ohio State University, their potential cannot be understated.
Drone retail delivery is also being explored. Verizon and UPS Flight Forward announced last year that they were working to leverage 5G technologies to improve drone delivery in Florida.
Customer service robotics have also been explored. In January of last year, Hyundai introduced a robot called DAL-e that would aid in automotive showrooms. The robot can greet customers and help them find the vehicle that meets their needs. The robot also uses facial recognition and AI to detect if the customer is wearing a mask and advise them appropriately about masking rules.
Special hardware can sometimes aid with inventory management where ceiling and shelf-mounted cameras cannot. Machines like SmartSight can automate the process of identifying misplaced items on shelves and sales floor quantities and alert workers when certain items are running low.
As artificial intelligence improves in 2022, so does natural language processing (NLP). Smart assistants like Google Assistant, Alexa, Siri, and Bixby are becoming more and more advanced in their voice recognition and responsiveness. Their ability to serve customers in the retail industry has improved tremendously. There is a great deal of nuance to be explored with the process of ordering products from home with your voice with or without a screen to aid you visually.
Smart home devices are expected to be used in more than 77 million homes in the United States by 2025, meaning that this is a huge opportunity that businesses can take advantage of. Other kinds of IoT devices in customers’ homes can prove valuable too, such as AR-capable smart mirrors. The most important part of the whole process is making sure that these devices are not intrusive and are genuinely helpful for the consumer.
Walmart’s Voice Ordering is a great example of this technology in action. By asking smart speakers to add products to a cart, they can place orders entirely by voice. Those items can then be made available for pickup by Walmart’s workers.
Future of the Retail Industry: Embracing, but Managing, Technology
As technological advancements thrive in the retail industry, legacy technologies like older PoS systems, inventory management solutions, etc are being replaced. However, during these transitions many hiccups tend to bog down the process. There are sometimes oversights and unforeseen issues that require special attention. More importantly, it is critical to manage these new systems to ensure that they are suiting the business correctly.
Problems can be seen as opportunities, and 2022’s challenges for the retail industry are no different. Companies must adapt and use retail technology innovations to succeed in the market.
As technology advances, there are many new niches waiting to be monetized and opportunities to be taken. Digital transformation and data-centric cultures will continue to disrupt businesses that cannot adapt to rapidly changing customer preferences, new technologies, and supply chains. We must bring consumers experiences that they find engaging, accessible, and valuable.
The most important part about introducing new technologies into the retail space is the relationship between the business and the vendor developing the technology. How they work together to develop and maintain the solution determines the success of the product, no matter if the technology is intended for the consumer directly or if it’s intended to aid the business’s own tasks first and foremost.