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Google Cloud intros new AI technologies for retailers

Google Cloud’s Discovery AI solutions has launched new features to power e-commerce sites with modern browsing capabilities, personalized shopping experiences and better product recommendations.
Sandra Levy
Senior Editor
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Google Cloud is introducing four new and updated AI technologies to help retailers transform their in-store shelf-checking processes and enhance their e-commerce sites with more fluid and natural online shopping experiences for customers.

A new shelf-checking AI solution, built on Google Cloud’s Vertex AI Vision, utilizes Google’s database of facts about people, places and things, giving retailers the ability to recognize billions of products to ensure in-store shelves are right-sized and well-stocked.

In an update to its Discovery AI solutions, Google Cloud is debuting a new 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 is launching new machine learning capabilities that empower retailers to dynamically optimize product ordering and recommendations panels on their e-commerce pages and deliver personalized suggestions for repeat purchases.

“Upheavals over the last few years have reshaped the retail landscape and the tools retailers need to be more efficient, more compelling to their customers and less exposed to future shocks,” said Carrie Tharp, vice president of retail and consumer at Google Cloud. “Despite uncertainty, the retail industry has enormous opportunity. The leaders of tomorrow will be those who address today’s most pressing in-store and online challenges with the newest technology tools, such as artificial intelligence and machine learning.”

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New shelf checking AI helps retailers improve product availability. The problem of low or no inventory on in-store shelves is a troubling one for retailers, Google Cloud said, citing a NielsenIQ analysis of on-shelf availability, which showed that empty shelves cost U.S. retailers $82 billion in missed sales in 2021 alone.

“While retailers have tried different shelf-checking technologies for years, their effectiveness has often been limited by the resources needed to create reliable AI models to detect and differentiate products—from the different flavors of jam and jelly to the dozens of types of toothbrushes,” the company stated.

Google Cloud’s new AI-powered shelf-checking solution can help retailers improve on-shelf product availability, provide better visibility into what their shelves actually look like and help them understand where restocks are needed. Built on Google Cloud’s Vertex AI Vision and powered by two machine learning models, the shelf-checking AI enables retailers to solve a very difficult problem: how to identify products of all types, at scale, based solely on the visual and text features of a product, and then translate that data into actionable insights.

“Retailers don’t have to expend time, effort and investment into data collection and training their own AI models. Leveraging Google’s database of billions of unique entities, Google Cloud’s shelf-checking AI can identify products from a variety of image types taken at different angles and vantage points—an especially difficult task. Retailers will have a high degree of flexibility in the types of imagery they can supply to the shelf-checking AI. For 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,” the company said.

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To help retailers make the online browsing and product discovery experience more modern, faster, intuitive and fulfilling for shoppers, Google Cloud introduced an AI-powered browse feature in its Discovery AI solutions for retailers, which uses machine learning to select the optimal ordering of products on a retailer’s e-commerce site once shoppers choose a category, like “women’s jackets” or “kitchenware.”

Over time, the AI learns the ideal product ordering for each page on an e-commerce site using historical data. The AI-powered browse feature optimizes how and what products are shown for accuracy, relevance and likelihood of making a sale. The feature can be used on a variety of e-commerce site pages, from browse, brand and landing pages, to navigation and collection pages.

Historically, e-commerce sites have sorted product results based on either category bestseller lists or human-written rules, like manually determining what clothing to highlight based on seasonality.  The AI-powered browse feature takes a whole new approach, self-curating, learning from experience and requiring no manual intervention. In addition to driving significant improvements in revenue per visit, it also can save retailers the time and expense of manually curating multiple e-commerce pages. The new tool is now generally available to retailers worldwide supporting 72 languages.