Google’s Shopping Graph is a dynamic, AI-enhanced model that understands a constantly-changing set of products, sellers, brands, reviews, and most importantly, the product information and inventory data received from brands and retailers directly.
With the Shopping Graph, Google can connect people with over 35 billion product listings from millions of merchants across the web. Here are some ways to optimize your online shopping with Google’s Shopping Graph:
Ways to optimize your online shopping with Google’s Shopping Graph
1. Use Google’s virtual try-on feature:
Google’s virtual try-on tool features real models and allows you to refine a product to find exactly what you want. You can refine using inputs like color, style, and pattern, and see options from stores across the web.
2. Use guided rfinements:
Google’s guided refinements can help you fine-tune products until you find the perfect piece. Thanks to machine learning and new visual matching algorithms, you can refine using inputs like color, style, and pattern.
3. Get personalized results:
Soon, you’ll see more personalized shopping results based on your previous shopping habits. You’ll also have the option to tell Google your preferences directly, plus controls to easily turn off personalized results if you’d like.
4. Browse and explore options:
Google’s Shopping Graph powers a comprehensive, real-time dataset of products, inventory, and merchants with more than 35 billion listings. This helps connect shoppers with the right products for them and helps millions of merchants and brands get discovered on Google every day.
5. Use Product Studio:
Product Studio is a new tool that helps merchants easily create unique product imagery using generative AI. This can help small businesses stand out and connect with new customers.
6. Connect with your customers:
Google is reimagining entirely new ways to help you connect with your customer at decision-making points in the journey – across Search, Shopping, and more.
4 Key Points About the Google Shopping Graph
Google’s Shopping Graph is a data set powered by machine learning that helps users find products when shopping online. Here are four key points about the Shopping Graph:
Ultimate shopping data set:
The Shopping Graph is a real-time data set that stores information about products and sellers. It contains billions of global product listings, including details like availability, reviews, pros and cons, materials, colors, and sizes. It is similar to Google’s Knowledge Graph, which contains facts about people, places, and things.
Massive number of product listings:
The Shopping Graph houses over 35 billion product listings and continues to grow. These listings are constantly updated through information shared by retailers via Google Merchant Center or from retailers and brands across the web. Each listing provides information about a specific product and its seller.
Specific product criteria:
The Shopping Graph helps users find products that meet specific criteria. For example, if someone is looking for a women’s red puffer coat that’s cropped, shiny, has a fleece hood, size medium, on sale with free shipping, suitable for extreme weather, lightweight, and made from sustainable materials, the Shopping Graph scans billions of listings and pulls relevant data from the web to narrow down the options.
Powers helpful shopping features:
The vast knowledge base of the Shopping Graph enables various shopping features. For instance, when searching for a puffer coat, the Graph can show popular ways to style it and suggest similar products. It also provides buying guides by synthesizing insights from trusted sources on the web, making it easier for users to make informed purchasing decisions.
Overall, the Shopping Graph leverages machine learning and a vast amount of product data to enhance the online shopping experience and help users find what they’re looking for.