Google Cloud releases four new and updated AI tools to give customers a smoother online shopping experience and help retailers manage in-store inventory.
- A personalized search and browsing experience for e-commerce sites.
- An AI-powered solution for checking in-store shelves.
- AI-driven product recommendation system.
- A tool that uses machine learning to display products on a website.
Here’s a detailed description of each new tool.
Personalized search and browsing of e-commerce sites
Google Cloud has introduced AI-based tools that personalize what customers see as they search and browse ecommerce websites.
This technology enhances the existing Retail Search solution on Google Cloud to help create a more seamless and intuitive online shopping experience.
The AI system behind this new tool can identify customer preferences by analyzing behavior such as product viewing, cart additions, and purchases.
We use this information to tailor search results and prioritize products for a more personalized experience. Personalization is specific to the retailer’s website and is not tied to customer activity on Google.
AI-based product sorting for e-commerce sites
Google Cloud releases new AI-powered tools for e-commerce websites to improve the browsing and product discovery experience.
This feature uses machine learning to optimize product ordering on retailer sites when shoppers select a category.
Traditionally, e-commerce sites have sorted product results based on category bestseller lists or manually created collections, such as clothing selections to spotlight based on season.
An AI-driven system developed by Google employs a new strategy that uses historical data to improve how products are sorted. This increases relevance, accuracy, and sales potential.
The tool is now available as part of Google Cloud’s Discovery AI solution for retailers.
AI-driven product recommendations
An upgrade to Google Cloud’s Recommendations AI will make your ecommerce site more personalized, dynamic, and helpful to individual customers.
One of the new features, page-level optimization, allows sites to dynamically determine which product recommendations to display to shoppers.
This reduces the need for time-consuming user experience testing, leading to increased user engagement and sales.
Additionally, new features for revenue optimization use machine learning to deliver more effective product recommendations, potentially increasing revenue per user session.
Machine learning models, co-created with DeepMind, consider product categories, product prices, and user behavior on e-commerce sites to determine the ideal balance between customer satisfaction and revenue growth.
Finally, the new “repurchase” model leverages a customer’s past purchases to suggest future follow-up purchases.
These new tools are now available to all retailers using Google Cloud.
Retail store shelf check using AI
Retailers have used various shelf-checking techniques for some time, but their success has been hampered by the resources required to develop AI models to distinguish and classify products.
Google Cloud has launched an AI solution for checking shelves. This allows retailers to identify all kinds of products at scale based solely on visual and text features.
The tool transforms data into actionable insights to improve product availability, increase visibility into current inventory, and help identify where restock is needed.
The technology is currently in preview and will soon be available to retailers around the world. Google says retailer data and images remain the property of the retailer, and AI can only be used to recognize products and price tags.
Featured Image: Daniel Constante/Shutterstock
