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Google debuts 4 AI tools for retailers

Shelf checking, online window shopping and product recommendations are the focus areas.

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Photo by Alex Dudar on Unsplash

The NRF Big Show arrives in New York on Jan. 14, and that means companies will be rolling out their latest technologies and partnerships to start off 2023 strong.

Google is getting a head start. Just ahead of the event, the company’s cloud division shared details on four new AI-powered releases for retailers on Friday. The technologies are designed to advance in-store shelf checking, as well as ecommerce browsing and recommendations.

The launches show how Google Cloud is expanding a suite of products for others. This is distinct from Google Shopping through the search giant’s own site, which has also gotten plenty of upgrades over the last year.

"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, VP of retail and consumer at Google Cloud, in a statement. "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."

For those looking to see how AI will play a role in commerce, Google’s technologies offer a few use cases, from store operations to personalized online shopping.

The technologies include:

​A shelf-checking solution

This aims to ensure that shelves in stores don’t become low or empty. Built on Google’s Vertex AI Vision, this uses Google’s database of people, places and things to help recognize products. It uses two AI models: a product recognizer and tag recognizer.

The goal is to solve a particular 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.”

The AI tool can identify products based on images taken from a variety of different angles. Currently in preview, the product allows retailers to retain their imagery and data.

​An online window shopping tool

This feature is designed for browsing and product discovery.

Here’s how it works: Shoppers choose a category, such as “women’s jackets.” Then machine learning selects optimal ordering of products in the results. The system, which is powered by Discovery AI, learns the ideal order on each page, and improves over time with more data.

The feature can be used on browsing, brand and landing pages.

Typically, products are ordered based on best-selling items, or human-written rules. The AI tool essentially self-curates a selection. For retailers, the potential benefits are more revenue per visit and team time saved.

Personalized search and browsing results

This is designed to show customers products that align with their tastes and interests.

AI is employed to analyze a customer’s behavior on an ecommerce site, such as their clicks, cart, purchases and more. This helps to determine shopper tastes and interests, and AI then moves up products that match them in results.

Google said the results are based solely on an interaction on a specific retailer’s site through an account or first-party cookie, and not linked with Google more widely.

Upgrades to Recommendations AI

This is designed to help retailers arrange and coordinate recommendation panels that appear on ecommerce sites. New capabilities include:

  • Page-level optimization to help an ecommerce site dynamically decide what product recommendation panels to show shoppers.
  • Revenue optimization uses machine learning that helps to identify recommendations that will increase revenue per user session.
  • A third tool, built with DeepMind, uses machine learning to analyze product categories, item prices, and customer clicks and conversions. It finds recommendations that balance customer satisfaction and revenue lift.
  • Buy-it-again, which provides recommendations for repeat purchases based on a shopper’s history.

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