The retail industry continues to be a hotbed of patent innovation. Activity is driven by personalized product recommendations, and advanced technologies, and the growing importance of technologies such as cloud computing, chatbots, big data, machine learning, and artificial intelligence (AI). In the last three years alone, there have been over 156,000 patents filed and granted in the retail industry, according to GlobalData’s report on Artificial intelligence in retail: personalized recommendation AI. Buy the report here.
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However, not all innovations are equal and nor do they follow a constant upward trend. Instead, their evolution takes the form of an S-shaped curve that reflects their typical lifecycle from early emergence to accelerating adoption, before finally stabilizing and reaching maturity.
Identifying where a particular innovation is on this journey, especially those that are in the emerging and accelerating stages, is essential for understanding their current level of adoption and the likely future trajectory and impact they will have.
50+ innovations will shape the retail industry
According to GlobalData’s Technology Foresights, which plots the S-curve for the retail industry using innovation intensity models built on over 115,000 patents, there are 50+ innovation areas that will shape the future of the industry.
Within the emerging innovation stage, cosmetics recommenders, 3D garment modeling, and in-store theft monitoring are disruptive technologies that are in the early stages of application and should be tracked closely. In-store product recognition, personalized recommendation AI, and smart checkout are some of the accelerating innovation areas, where adoption has been steadily increasing. Among maturing innovation areas is automated object recognition, which is now well established in the industry.
Innovation S-curve for artificial intelligence in the retail industry
Personalized recommendation AI is a key innovation area in artificial intelligence
Personalized recommendation AI is an advanced technology employing algorithms and machine learning techniques to deliver tailor-made suggestions to users. These recommendations are rooted in users' unique preferences, behaviors, and demographic information.
By scrutinizing extensive datasets encompassing user interactions, purchase records, and social media engagement, this technology gains insights into individual tastes and can make precise forecasts regarding users' preferences for diverse content, products, or services. The integration of personalized recommendation AI holds the potential for businesses to elevate customer satisfaction, boost engagement levels, and drive sales, all by catering to users on a highly individualized basis.
GlobalData’s analysis also uncovers the companies at the forefront of each innovation area and assesses the potential reach and impact of their patenting activity across different applications and geographies. According to GlobalData, there are 270+ companies, spanning technology vendors, established retail companies, and up-and-coming start-ups engaged in the development and application of personalized recommendation AI.
Key players in personalized recommendation AI – a disruptive innovation in the retail industry
‘Application diversity’ measures the number of applications identified for each patent. It broadly splits companies into either ‘niche’ or ‘diversified’ innovators.
‘Geographic reach’ refers to the number of countries each patent is registered in. It reflects the breadth of geographic application intended, ranging from ‘global’ to ‘local’.
Patent volumes related to personalized recommendation AI
Source: GlobalData Patent Analytics
Leading patent filers in the personalized recommendation AI include Alphabet, Nant, Microsoft, Cox Enterprises, and Meta Platforms. An American technology company Meta Platforms is gearing up to introduce AI-powered chatbots, each with its unique personality. These chatbots are designed to enhance user engagement by providing personalized recommendations for products, content, and advertisements. Through this innovation, Meta aims to gather user data, which will, in turn, empower the company to deliver even more personalized content and advertisements, further enriching the user experience.
In terms of application diversity, Memjet Technology is the leading company in the AI-based personalized recommendation space, while Cox Enterprises and DNA nudge are in the second and third positions, respectively.
In terms of geographic reach, Memjet Technology leads the pack, followed by Nant and Cox Enterprises.
The major advantages of personalized recommendation AI include increased user engagement, higher conversion rates, and improved revenue for businesses. The technology excels in improving customer retention and optimizing marketing efforts. In today's data-rich landscape, personalized recommendation AI continues to lead AI innovation, reshaping industries such as e-commerce, entertainment, and content streaming for more effective and enjoyable user interactions.
To further understand the key themes and technologies disrupting the retail industry, access GlobalData’s latest thematic research report on Artificial Intelligence (AI) in Retail and Apparel.
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GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.
GlobalData’s Patent Analytics tracks patent filings and grants from official offices around the world. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
