The traditional retail industry is facing challenges as the rapid development and continuous improvement of AI tools and techniques ushers in the era of New Retail. Many once-successful brick and mortar shops are at risk of disappearing altogether if they fail to adapt to the changing marketplace.
As the concept of “New Retail” spreads globally, many Fortune Global 500 retail companies are implementing AI technologies such as computer vision and NLP in unmanned stores and warehouses or virtual stores, and are accelerating supply chain upgrades to better position themselves for the future of shopping.
Global Retail Industry Market Size
The steady growth of the global economy and per capita disposable income have triggered a surge in the retail industry over the past few years. The global retail industry market was valued at nearly US $23.5 trillion in 2017, a 5.3 percent year-on increase. Although the Internet retail share tripled over the previous three years, it still accounted for just 1.5 percent of the total, and is the main area for projected future growth. Mordor Intelligence expects the global retail market to surpass US$31.8 trillion by 2023.
AI Technologies Supporting the Retail Industry
2D/3D Computer Vision (CV): CV is widely applied in unmanned warehouses, unmanned stores, shelving and virtual display. It is also crucial for data acquisition from offline stores. Also, 2D or 3D CV are often combined with other advanced technologies in New Retail spaces.
Natural Language Processing (NLP): Communication is one of the key components in retailing. With the promotion and widespread of human-computer interaction, NLP has become a hot research topic among giant retailers, supporting machine translation, text data mining, semantic computing, text searching, recommendation systems, etc.
Robotics: Robotics is one of the core elements that enables an unmanned, fast and intelligent environment for New Retail. By leveraging AI technologies such as deep learning and CV, robotics can be applied in package receiving, package storage, order picking, packaging, etc.
Augmented Reality (AR) and Virtual Reality (VR): Powered by AI technologies such as machine learning and computer vision, AR and VR have been deployed for various New Retail applications, e.g., AR try-ons and 3D virtual shopping.
Sensors: Sensors are the main devices for event detection and information collection in the retail industry. Bundled with deep learning and machine learning algorithms, sensors allow retailers to perform high-dimensional big data analytics to boost sales and customer satisfaction in New Retail.
AI Applications in the Retail Industry
AI Use Cases in Fortune Global 500 Retail Companies
Amazon: Leveraging technologies such as deep learning, CV, sensor fusion and cloud computing, Amazon launched its first unmanned store “Amazon Go” in Seattle, Washington in January 2018. The company has also invested in a large number of real-time multi-dimensional sensing systems for tracking and collecting customer feedback information based on facial expressions and conversations. Through feedback analytics, Amazon hopes to optimize its business performance for both online and offline retail.
Alibaba: The company’s Alipay mobile payment solution has been strengthened with the tech giant’s patented technologies based on deep learning, biometric facial recognition, eye vein pattern recognition and so on, allowing customers to forego cash and simply scan and pay for items with their smartphones.
JD.com: JD has made several AI technology breakthroughs since launching its logistics and automation lab “JD-X” in 2016. The company rolled out a delivery robot product that integrates cutting-edge technologies like 360-degree view monitoring and LiDAR detection to provide low-cost logistics services in over 20 urban areas in China. Moreover, its delivery robots can effectively verify the identity of a package receiver using facial recognition.
Walmart: Based onblockchain, big data, and machine learning technologies, Walmart collaborated with Microsoft, IBM, JD.com, and Tsinghua University to develop a tracing solution for all its New Retail customers and suppliers, to ensure that all relevant information about products anywhere on the supply chain is trackable in real time.
Limitations of AI in the Retail Industry
Feasibility Problem: Due to the high uncertainty regarding adoption costs for AI and other cutting-edge technologies in different scenarios, retailers face challenges determining the feasibility of New Retail solutions before they invest time and money.
Consumer Acceptance: Retailers who adopt innovative New Retail AI solutions and products are aiming to provide an improved shopping experience for their customers. However, it is hard to predict the time and money required to gain an acceptable improvement in consumer appreciation and profit from such transformations.
Compatibility:Retails must consider the compatibility problem before they implement AI solutions in different scenarios and areas of the retail industry. If any link along the entire supply chain cannot adapt to the change, retailers could potentially see an overall decline in performance.
AI’s Future in the Retail Industry
New Business Model Emergence: As traditional retailers seek to transform themselves to keep up with consumers, a new customer to manufactory (C2M) business model has emerged in the retail industry. C2M leverages the capability of big data and AI to enable manufacturers to optimize their products in accordance to customer demands through e-commerce platforms.
Seamless Connection between Online and Offline Retail: Based on technologies like AI, big data and sensors, retailers are reshaping the shopping experience for customers both online and in brick and mortar shops. For example the growing “buy online, pick up in-store” (BOPUS) strategy is changing consumer shopping habits.
Application of Robotics and Automation Technologies: Advanced AI, computer vision and sensor technologies enable the launch of robots and unmanned systems in retail scenarios, greatly reducing production and operational costs and improving customer shopping experience.
Acknowledgement: The author would like to thank JD’s X Division and GodBag Founder Jian Li for their helpful advice in this report.
Source: Synced China
Author: Tingting Cao | Editor: Michael Sarazen