For decades, traditional analytics has served the data-driven retail industry well. On the other hand, Artificial Intelligence (AI) and Machine Learning (ML) have introduced a whole new level of data processing that leads to more in-depth business insights. By identifying anomalies and correlations from hundreds of Artificial Intelligence/Machine Learning models.
According to CB Insights, artificial intelligence firms raised $1.8 billion in 374 deals between 2013 and 2018. Amazon can take credit for these excellent results because it convinced business leaders to reconsider using AI in retail – physical stores and e-commerce methods – to stay ahead of the competition. Over 28% of merchants had already implemented Artificial Intelligence/Machine Learning solutions, a sevenfold increase from 2016 when only 4% did. Take a peek at Google Trends to see how popular “AI in Retail” is:
Advantages of AI in Retail Business
Because we have real experience, we at SPD Group understand how AI solutions can improve retail enterprises. We created a product recommendation system based on a customer’s location and behaviours in a store. Its goal was to increase sales for shop owners while improving the customer experience by making intelligent recommendations.
The goal of this project was to improve the CRM in a supermarket. We wanted to integrate a customer identity system into the existing CRM process that didn’t require physical identification cards. We had to evaluate video from cameras, identify customers in frames, and track each customer’s position in the store to correlate it to the products’ location to accomplish this. This information is gathered to discover which products each consumer wants to tailor future offers to them. In addition, the system needed to warn employees when a customer was standing in one place for too long so that staff could assist them if necessary.
When someone wants to track many items, they use the Tracklist Association technique. This solution began with using the store’s existing security cameras and a few additional cameras. Because of its efficiency in identifying persons, we had employed the YOLO model with pre-trained weights.
This method uses the network flow graph to process and somewhat improve the information from YOLO. The distinct similarities of the same visitor (appearance embedding) generate tracks and group them. To establish the extent of the cameras, we calculated their geometry. Our system can now get 2D coordinates on the location of specific consumers after implementing the perspective conversion. Our system currently has the capability of interacting with many consumers.
The proprietors gained a new level of knowledge when this system was placed in the store. They can forecast demand for a given product using all of this information about consumer preferences stored in the CRM. Furthermore, business managers can create far more successful personalized and promotional offers for various consumer groups by adjusting price tactics. Physical gift cards were eliminated, which improved the purchasing experience and consumer happiness. Personnel can now provide specific discounts or inquire about the customer’s previous buying experience, making the buyer feel even more welcome.
That was our experience, but what are the global trends in smart retail?
The current state of AI in Retail
So, here we were in 2021, and Artificial Intelligence solutions still have a long way to go. However, we can already show you some real-world AI applications that have demonstrated business value. Here’s how AI is affecting the retail industry.
Top 5 applications of AI in Retail
Stores may be able to function without employing a cashier
Amazon wants to open more AI-powered stores, such as Amazon Go, where only six to twenty human employees are required. Queues will be reduced, the number of human employees will be diminished, and operating costs will be reduced. Amazon AI has already implemented Checkout-free stores. The Amazon Go and Walk Out Shopping systems react when you take something from the shelf or put it back. When you leave the store with your purchases, your Amazon account will debit your account.
Chatbots will aid customer assistance
AI chatbots boost consumer service by improving search, sending notifications about new collections, and suggesting related products. If a buyer has already purchased a black hoodie, a chatbot can recommend a snapback to complete the appearance. Eighty per cent of brands worldwide are now utilizing AI chatbots or plan to do so shortly. To assist its clients in navigating their collections, Tommy Hilfiger and Burberry have launched chatbots.
In-store support is available
Retailers also invest in technologies that assist customers and store employees during the shopping experience. Kroger Edge technology has replaced paper price tags with smart shelf tags in their stores. This technology also gives video commercials, nutritional information, and promotions on the displays. Lowe boat, a Lowe’s autonomous in-store robot, assists consumers in finding what they need in the store in various languages. At the same time, thanks to real-time monitoring features, it aids inventory management.
Adjustments to the price
AI applications in retail outlets could aid in the pricing of products by showing the anticipated results of various pricing schemes. Business owners can showcase the best deals to attract new clients and increase revenue. Systems collect information on other items, promotional efforts, sales numbers, and other data to carry out this task. eBay and Kroger both use Artificial Intelligence to optimize their pricing and remain flexible in their capacity to modify rates and promotions based on the data they collect.
Price forecasts
The price of a product is forecasted based on demand, seasonal trends, characteristics, the release date of new models of the same item, and other factors. Its most obvious application is in the travel business, but it might also be retail. Consider developing an app or service that allows customers to see how a product’s pricing may vary in the future. This is possible and simple to implement with Artificial Intelligence. Client retention could be aided with a pricing prediction feature. However, predictive analytics and machine learning could achieve much more than just a pricing prediction in the retail industry.
Conclusion
Artificial Intelligence (AI) and Machine Learning (ML)-based solutions can aid in the expansion of your retail business. Maintain relevancy and outperform your market competitors! Increased income will arise from automated procedures, better business insights, and higher customer engagement. Chatbots, visual search, and voice search are examples of artificial intelligence retail solutions that substantially improve your bottom line.
Are you ready to use AI in the retail business? Contact the ONPASSIVE team.
