The retail industry is an essential part of the world economy. “It can be categorized by different levels of service, and generally fall into one of the following sections. Discount department shops, wholesale organizations, supercenters, hyper marts, and so-called category killers” (different kinds of Stores).
The retail industry is in continuous transformation, and there is a necessity to reduce costs and increase efficiency in operations. In addition, shoppers become more demanding regarding product quality and have an extra obligation to beat the overall customer experience.
The retail industry has been one of the fastest-growing businesses worldwide, primarily over the last few years. Though initially, the retail industry was mostly confused and disorganized, however with the change of taste and preferences of customers, the industry is getting more popular these days and getting organized. In addition, these industries utilize different new technologies and applications to improve their business growth and customer experience and stand in their competitive market.
According to a report, Retail sales will be projected to be nearly 27 trillion dollars in 2022. And 80% of the retailers revealed that pandemics had created the most influential technology pressure for their business that they have ever experienced.
As a retailer, you should know what major challenges you’re facing and the effective way to solve those problems, etc. To help retail industry managers improve their businesses, we’ve gathered this list of the top 5 challenges in 2022 for retail industries and explained how to overcome them using AI technology.
The customer experience is the set of observations that customers feel while purchasing products and even after the deal close. For example, it is the buyer’s entire shopping experience in a retail store, from entering the parking lot to back in their vehicle. Thus, the customer shopping experience is about the shopper’s feelings, emotions, and sensations.
This method transforms retailers’ approach from “I want customers to buy from my store” to “I want to understand customers.”. By creating a significant shopping experience for your customers, you are considering coming to closing your next sale. With online demand growing every year, retailers with stores are discovering new technologies to improve the entire customers’ shopping experience.
Store products alone are not sufficient to create brand identification or to gain a competitive position. Competitors can deliver the product to the customer the same as your products regarding quality and prices. What makes you stand out from your competitive world is the customer experience you provide to your shoppers.
Retail industries deal with both products and services. While products are consumed, services are experienced. This distinction is simple but critical to understand to be successful in today’s retail industry world. The buyer is the essential piece of the retail business puzzle. Providing the best customer shopping experience is how you can gain loyal consumers.
Customer loyalty is nothing but the backbone to the retail businesses to survive in your competitive world. Plain and simple, providing the best customer experience will lead to good profits.
Implementing an enhanced in-store customer experience with personalized recommendations, offers, and product support — as per age, gender, location, and behavior of customers is the next big thing in retail. Artificial intelligence (AI) technology is providing more opportunities to retailers to achieve this goal. Here are five ways to use AI solutions in retail stores to enhance the in-store customer experience.
Imagine a consumer is checking a specific item at a shop, and you suggest similar product on their mobile. Then how customer feels about your store and recommendations just think about it. AI can find the shopper store visited list and trace those visits and the products purchased in the past. Recommender engines use this data to suggest good personalized rewards like discounts, loyalty points, etc.
For example :
Sephora store uses this technique to find the perfect makeup shade without wearing anything on their face to their customer. Instead, their scanning machine, i.e., Color IQ, scans a customer’s face and offers recommendations for personalized foundation and concealer shades. In contrast, Lip IQ does the same to help find the perfect shade of lipstick. It’s a massive help to shoppers who know the stress of discovering the perfect shade by trial and error.
Another example of personalized recommendations is Olay consumers can get customized skincare treatment without consulting a dermatologist. With Olay’s Skin Advisor, clients take a selfie of their plain face, and AI-based models will tell the actual age of their skin. Then, the app evaluates skin health and makes recommendations for problem areas with personalized skincare regimen recommendations.
An excellent way to save customers time and find the perfect outfit for us – within a span of minutes!. For example, a virtual fitting room system will scan customers in 20 seconds and measure 200,000 points of their bodies. As a result, companies like Levi’s, Gap, Brooks Brothers, Old Navy, and others installed these scanners in their stores and received massive sales increases.
The benefits of virtual fitting rooms in retail stores are evident during the pandemic. That is a lower risk of vulnerability to COVID-19 and the ease of getting fit from home. According to a report, the Augmented Reality market is projecting its growth from $3 million to $6.5 million by 2025. That’s a compound annual extension rate of 13.44%.
Zeekit is an Israel-based fashion technology firm that has already partnered with brands like Macy’s, Adidas, and Modcloth to help shoppers get an idea of what a garment looks like on their body without physically trying it on using virtual fitting rooms. On the Zeekit app, customers can upload a full-body photo, try branded clothes, and make purchases facilitated through the app.
Another famous retail shop Walmart acquired Zeekit to reduce significant issues, such as unmet expectations and fitting, faced by their customers while shopping for clothes online. The latter will help the business virtually try out clothes before purchasing them by either choosing from various models or uploading their photos on the platform.
Third Love retail store also launched The Fitting Room, its latest interactive online fitting test or quiz. It would allow shoppers to find the best fit for their carefully selected skirts, shirts, and other varieties of clothes.
In today’s retail world, AI-based visual search has the power to execute the shopping experience much easier and quicker for consumers—and that can generate some profound benefits for retailers and store owners. Shoppers who have an easier time finding the products/items they’re watching for are already one step closer to causing a purchase.
Image recognition is the most rapidly expanding area in AI. The main benefit for retailers here is to enable customers to search for items in the store without having to walk around. For example, an AI-powered kiosk can search for a stock in-store using an image given by the shoppers and notify similar products with their exact spot in the store.
Luxury department store Neiman Marcus utilizes AI to make it easier for customers to find products or items Through Snap – Find – Shop. The app lets users take photos of products they see while out and about and then search Neiman Marcus inventory to find the same or a similar thing. Instead of using vague search terms to find an item, the photos usually find a similar match.
AI-based Self-checkout aims to boost revenues as well as streamline all purchasing methods. This innovative technology allows shoppers to scan products/items, pay, and receive the receipt quickly and without queues.
Self-checkout systems with intelligent image scanning can predict the bar code of the item in the frame. If a customer passes an item through the frame and the predicted bar code is not processed, but another item’s barcode is scanned repeatedly, the system can alert staff to intervene.
Another solution for the self-checkout is that the buyer uses a scanning machine to scan purchased products and puts them in a bag. The bag weight is estimated to confirm that the item scanned is the one added to the bag. Then, buyers can pay for the things using cash or a card.
AI-based in-store navigation made for retails to provide more benefits, including better customer experiences. And while many expert reports will tell you that the longer shoppers are in your retail shop, the more your customers will spend, AR is transforming this declaration on its head.
Augmented reality (AI) can reduce time spent in-store while also increasing the number of purchases made. Unfortunately, sometimes more time spent in-store means more time getting lost or confused while finding products. Through AR technology, retailers can execute the purchase process more comfortably for their customers.
One example of such an application is utilized in Lowe’s stores. The in-Store Navigation app utilizes the Augmented Reality (AR) technique to produce a mobile experience that guides customers through the shop. For example, customers form a shopping list, and the app recognizes the quickest route possible to check off all the products. Retailers can find success by combining the two to help buyers navigate their stores. In addition, retailers can arm associates with the technology to better assist shoppers.
For medium to large retail industries and wholesalers, inventory management comes with additional complexity. However, retail inventory management is the method of ensuring you carry stock that shoppers want, with neither too small store nor too much on hand. By managing better inventory, retailers meet consumer demand without running out of stock or carrying excess stock. In practice, powerful retail inventory planning outcomes in lower costs and a greater understanding of your store sales patterns.
From a strategic point of view, better retail inventory management planning increases efficiency and profits by avoiding overstock and minimizing expenses. The best inventory management practice will give the below advantages:
Retail company owners have a lot to consider––but getting products to their customers on time is probably their top priority. Staying on top of product inventory helps retail owners to meet customer demand and remain profitable. But as their business expands and customer demand fluctuates, managing inventory can become challenging. As a result, scaling their retail inventory planning solution can be one of the biggest growth challenges to retail businesses.
In today’s retail world, out-of-stocks can cost 4% of retail sales. In addition, up to 20% of advertisements may go to waste. To avoid problems like these, retailers need excellent in-store execution. Using advanced AI-based Retail Shelf Management applications helps retailers to prevent stocking and promotion problems. You can quickly analyze and understand any concerns, make informed decisions and act immediately to achieve excellent results.
Measuring the fulfilling shelving standards is a more complex job than following more significant market data sources to gather information. With Artificial intelligence technology, retailers can now understand their marketplace and react in real-time effectively. The latest advancement in AI technology and deep learning algorithms are changing the retail industry. For example, companies can now leverage AI to better monitor their shelf presence with thousands of shelf products images.
Shelf monitoring will help identify product conditions on store shelves such as availability, varieties, space, pricing, promotions, and many more. As a result, it will enable retail businesses to make immediate improvements.
Traditionally, for stock replenishment, manual checking is performed by store staff, using a task known as planogram compliance monitoring. With AI-based image recognition abilities, in-store CCTVs can leverage to recognize and count front-facing products. For example, at the category, brand or SKU level to support the store staff in this tedious process. This innovation takes individual error and processing scalability out of the equation.
The shelf monitoring application aims to drive perfect store execution. It will enable Retailers to get in-store insights from their store shelf images that help them achieve a superior customer experience and optimize their marketing spend.
For example, Walmart store uses AI-powered cameras to determine which items customers are buying in order to charge them automatically. However, it still has traditional checkout stations. So instead, shelf monitoring system will monitor inventory levels to determine whether the stock is present or not on the respective shelves. For example, store staff will get a notification message if staff needs to bring out more products from the backroom to restock the shelves with the correct products or if some fresh food items or fruits have been sitting too long on the shelf and need to be pulled out.
AI is extending into virtually all industries and businesses, and retail is no exception. We have already discussed a few applications in retail to improve the customer shopping experience and better inventory management. In fact, specialists are calculating that the spending on AI in retail will be $15 billion by 2024. The reasons for this are pretty simple: AI offers a boost in efficiency for retailers while lowering costs. In addition, the customers are experiencing a greater level of convenience since AI streamlines many processes, allowing people to find the products they are looking for faster and expediting the checkout process. However, to make all cutting-edge applications like the AI solutions mentioned earlier for each challenge in the retail industry, a lot of data annotation work will require training the Artificial intelligence algorithms to function correctly.
Data annotation for retail has many purposes. Data annotation and image tagging help offline retailers improve the app’s image search functions or enhance inventory management to keep shop shelves stocked with the shoppers’ products. For example, Most of the Retail applications are well fit to Bounding Box annotations which are cheap. Additionally, there are usually many bounding boxes per image which also lowers the price. All the items on the shelves will annotate with the correct product name to recognize which item is present and picked. This system is also used for managing the stock of the items.
the importance of the data annotation process in one of the retail challenges i.e, shelf monitoring and automated shelf restock is that the annotation team labels the images of shelves, prices, brands, and products. So companies can track shelf management, identify items that have been misplacing, and quickly conduct price checking by integrating AI-based solutions. That means an AI-based shelf monitoring system will learn patterns from those tagging images and apply them accurately to detect products and brands, and categories, giving better audits with less time.
Like this, data annotation or labeling process will impact every AI application, which is explained above as AI solutions at customer shopping experience challenges such as personalized in-store recommendations, virtual fitting rooms, visual search, self-checkout, or cashier-free stores, in-store navigation. Suppose retail owners want to integrate or someone wants to build better accurate AI-based solutions like the earlier mentioned examples. In that case, you definitely have a well and high-quality annotated data set to achieve your goals.