🛍️ Retail's New Era: How AI is Revolutionizing the Industry
The retail industry is on the brink of a new era defined by technological advancement. With the industry expected to spend $7.3 billion per year on artificial intelligence by the year 2022, retailers that are looking to stay one step ahead are increasingly turning to artificial intelligence to help them understand the millions of data points in-store and turn them into insightful information that they can use to improve the speed and effectiveness of their business decisions. In this article, we will examine five use cases for artificial intelligence in the world of retail.
🛒 Use Case 1: Cashier-Less Checkouts
Cashier-less checkouts allow customers to enter the store, choose what they want, pay, and leave the store without having to queue or deal with any shop assistant. The various ways retailers are trying to implement it may surprise you, from smart carts to apps to camera surveillance. A number of things are required to make your shop smart, including hundreds, sometimes thousands of cameras, internet of things sensors, and a computer vision system. This allows retailers to detect and understand customer interactions, monitor the movement of products, and automatically detect the prices to charge.
When implemented correctly, it creates a win-win-win for customers. They no longer have to waste their time queuing or scanning their items, while for retailers, they can reduce their spending on staff. There is a number of companies trying to make this the new norm. Firstly, Caper AI is a startup that builds smart carts and cashier-less checkout technology. The cart automatically detects items instantly with its computer vision-enabled basket cameras. Shoppers just toss items into the cart naturally, and no barcode scanning is necessary. Once an item enters the cart, it's added to the digital shopping list automatically. In 2021, Instacart confirmed that they paid $350 million for Caper AI. Another well-known example is Amazon Go. These cashier-less stores harness sensor fusion and deep learning algorithms to identify what items a shopper has and how much.
📦 Use Case 2: Inventory Management
The pandemic has shown how important inventory management is. Although some supply chain issues can be out of retailers' control, AI can play a huge part in optimizing it and allows retailers to know what's needed in what quantities at what time and what the cost is. Harnessing the power of computer vision systems, they can deploy object detection software to track inventory on shelves and report where in the store needs replenishment. It can also boost the layout of the store, speed up A/B testing, produce heat maps to show where in-store customers flock, and forecast how much time a product will remain on the shelf before it's sold. In 2017, Walmart entered into a contract with Boston Over Robotics. The idea being that robots would use machine learning to identify which products were on the verge of selling out. However, for some reasons that were undisclosed, the partnership didn't materialize.
🔍 Use Case 3: Detecting Suspicious Activity
Shoplifting continues to be a huge problem around the world, costing retailers billions of dollars a year in the United States alone. Most shops deploy CCTV for security measures, but AI can go one step further. It allows retailers to identify suspicious behavior in-store, and if a crime is committed, it can send an alert to a member of staff to review the cameras. Utilizing activity recognition, object tracking, and pose estimation, they can recognize peculiar behavior in-store. Tokyo-based company VAC has created such software. They say test and local convenience stores show that their system can slash shoplifting losses by 77%.
🤖 Use Case 4: Customer Experience
Retailers are exploring ways AI can let them better understand shopper behavior in-store, directly helping them serve their customers better. By analyzing dwell time and how long a person stares at the item, it can give retailers a deeper insight into what can boost conversion and lead to larger purchases. AI can also be used to personalize the in-store experience, and according to Accenture, 83% of customers are willing to share their data if it will result in a more personalized experience. Cali Burger has implemented self-ordering kiosks that use face recognition software to identify existing customers. Once recognized, it gives them access to their loyalty program information, their order history, and even recommends meals to them. The company started testing the technology at the end of 2017 and has plans to roll out the technology worldwide.
📊 Use Case 5: Improving Product Placement
A planogram defines the location and quantity of products to be placed on display. It's essentially a blueprint for all merchandise in a store. Using image recognition and object detection techniques, AI can help take the guesswork out of product placement, helping retailers make the necessary amendments to better store layout, which for the retailer means more sales. Another great bonus of this technology is the quick ability to identify empty shelves, which for years have been the bane of both retailers and consumers. Using fixed cameras along with object detection, retailers can instantly alert staff each time an empty shelf is found. Tracks an AI planogram bot is used to audit planograms, allowing retailers to optimize in-store displays, and they can do it overnight while the store is closed and have the information ready for staff in the morning when they come in.
In conclusion, the future of retail lies with AI. By adding AI capabilities to stores, it helps retailers turn their data into powerful new insights. These data-centric solutions result in a better shopping experience for the customer, with streamlined purchasing, a highly personalized experience, and better store layout. While retailers enjoy more accurate forecasts, inventory efficiencies, and a smarter business overall.
Pros and Cons
Pros:
- Streamlined purchasing
- Highly personalized experience
- Better store layout
- More accurate forecasts
- Inventory efficiencies
- Smarter business overall
Cons:
- High initial investment cost
- Potential job loss for staff
🌟 Highlights
- AI can help retailers understand the millions of data points in-store and turn them into insightful information.
- Cashier-less checkouts allow customers to enter the store, choose what they want, pay, and leave the store without having to queue or deal with any shop assistant.
- AI can help take the guesswork out of product placement, helping retailers make the necessary amendments to better store layout, which for the retailer means more sales.
- The future of retail lies with AI. By adding AI capabilities to stores, it helps retailers turn their data into powerful new insights.
🙋♀️ FAQ
Q: What is a planogram?
A: A planogram defines the location and quantity of products to be placed on display. It's essentially a blueprint for all merchandise in a store.
Q: What is the benefit of using AI in retail?
A: AI can help retailers understand the millions of data points in-store and turn them into insightful information. It can also streamline purchasing, provide a highly personalized experience, and optimize store layout.
Q: What is the potential downside of using AI in retail?
A: The high initial investment cost and potential job loss for staff are potential downsides of using AI in retail.
Resources:
- https://www.voc.ai/product/ai-chatbot
