The use of AI and machine learning in the retail industry is growing at an exponential rate. Retailers have been able to see the results delivered by artificial intelligence systems instantly. AI is expected to become pervasive across customer journeys, merchandising, marketing, and supply networks as it can provide detailed insights to optimize the retail operations. Big data and machine learning have been successfully used by several retailers to achieve a substantial increase in their operating margins. Such technologies can enable retailers to deliver personalized experiences to the customer in order to increase loyalty and spending. There are various use cases for AI in the retail industry that can change the way this sector operates.
Uses of AI in the Retail industry
Sales and CRM Applications
In 2010, Japan’s SoftBank telecom developed a humanoid robot, Pepper, which can interact with customers and perceive human emotions. The robot was used as a customer service greeter and representative in over 140 stores. The company later reported up to 70% increase in footfall in multiple stores. Additionally, an American company developed AI-powered sales-assistant software, Conversica, which identifies and converses with internet leads to enhance sales. The customized sales assistant software is also used for cross-selling and re-engaging existing leads.
Product recommendation tools are adding significant revenues to e-commerce businesses. IBM Watson is one of the most advanced AI systems that exhibits order management and customer engagement capabilities. IBM Watson uses personality insights taking into account users personal information, browsing history, past transaction data, and other dynamic data including weather, location, time, and items in cart to develop its recommendation engine. By calculating the respective personality profile, IBM Watson can accurately suggest brands and products users are most likely to buy. For instance, North Face has used IBM Watson’s cognitive computing technology to suggest jackets for the customers based on variables like gender and location.
To know more about the uses of AI in the retail industry, machine learning, and automation, request a FREE demo.
Long after automation revolutionized the manufacturing industry, AI is set to be the next wave of change in this sector. AI can help companies keep inventories lean and reduce the cost. For instance, GE’s Brilliant Manufacturing software enables manufacturers to predict, adapt, and react more effectively by incorporating SCADA, MES, and analytics. It empowers decision-makers with deep operational intelligence and real-time visibility to reduce unplanned downtime and inventory.
Logistics and delivery
Domino’s Robotic Unit (DRU) has developed a prototype delivery robot that can keep food and drinks at an appropriate temperature. Its sensor also helps the device to navigate the best travel path for delivery. Alongside DRU, Amazon’s Prime Air is expected to be the future of delivery systems. Such drones can deliver parcels up to five pounds in weight in less than 30 minutes. The autonomous delivery of goods can significantly improve the performance of the retail industry and increase customer satisfaction.
With a view to reign in the retail industry, Amazon introduced its brick-and-mortar store, Amazon Go which enables check-out free technology that allows customers to shop and leave. Their check-out free shopping experience uses the same kind of AI technology used in autonomous cars including computer vision, sensor fusion, and deep learning. It automatically detects when the products are taken from or returned to shelf keeping track of it in a virtual cart. When customers are done with their shopping, they simply check out of the store, and Amazon will deduct the amount from their Amazon account. With regards to payment services, AI is also showing its potential in payments fraud. Payment fraud is a matter of concern in the e-commerce space, where fraudsters are using stolen accounts to make purchases. AI technologies can study thousands of purchase patterns to differentiate between the payment made by the genuine user and a fraudster.