Google Cloud on Friday announced four new and updated AI-technologies to help retailers streamline in-store shelf checking and create more natural online shopping experiences through enhanced personalization and browsing capabilities.
Retail media advertising involves the placement of ads on retailer apps, websites, and marketplaces. Spending on this platform in the U.S. grew from $1 billion to $30 billion in 2022 in just five years, according to CB Insights.
“For comparison, the social advertising market took 11 years to surpass the $30 billion, and search advertising took 14 years to do the same,” estimates CB Insights.
Google’s announcement comes ahead of the National Retail Federation conference scheduled to take place next week.
At NRF, Google Cloud will showcase AI innovations for retailers such as a shelf-checking AI solution built on Google Cloud’s Vertex AI Vision.
The shelf-checking AI feature -- which can identify billions of products -- relies on Google’s database of facts about people, places and things. The idea is to create more personalized search and better browsing through machine learning.
As an update to Discovery AI solutions, Google Cloud introduced a personalization AI capability and AI-powered browse feature to help retailers upgrade their digital storefronts with more dynamic and intuitive shopping experiences.
Google Cloud’s Recommendations AI solution also launched machine learning capabilities that empower retailers to dynamically optimize product ordering and recommendations on their ecommerce pages and deliver personalized suggestions for repeat purchases.
Google calls the AI technology that provides personalization capability “a product-pattern recognizer that uses a customer’s behavior on an ecommerce site, such as their clicks, cart, purchases, and other information, to determine shopper taste and preferences. The AI then moves certain products up in search and browse rankings for a personalized match.”
Shelf-checking AI helps retailers improve product availability.
NielsenIQ estimated that analysis of on-shelf availability -- mostly empty shelves -- cost U.S. retailers $82 billion in missed sales in 2021.
Retailers have tried different shelf-checking technologies, but its effectiveness has often been limited by the resources needed to create reliable AI models to detect and differentiate products.
Google Cloud’s shelf-checking AI can identify products from a variety of image types taken at different angles and vantage points.
Google provided this example: A retailer can use imagery from a ceiling-mounted camera, an associate’s mobile phone, or a store-roaming robot on shelf-checking duty. In preview mode, this technology is expected to be generally available to retailers globally in the coming months.
It’s important to note that a retailer’s imagery and data remains their own, and the AI can only be used for the identification of products and price tags.
And finally, better recommendations for shoppers are available through Google Cloud’s Recommendation AI, enables a page-level optimization feature for ecommerce sites that the company says can dynamically decide what product recommendation panels to uniquely show to a shopper.
A recently added revenue optimization feature, built in collaboration with Google's DeepMind, uses machine learning to offer better product recommendations mean to increase revenue per user session on any ecommerce site.
A machine learning model combines an ecommerce site’s product categories, item prices, and customer clicks and conversions to find the right balance between long-term satisfaction for shoppers and revenue lift for retailers.