Google Cloud is set to launch four new, updated AI tools to grant customers a smoother online shopping experience as well assisting retailers with in-store inventory management.
These include the following:
- A personalised search and browsing experience for e-commerce sites.
- An AI-powered solution to support checking in-store shelves.
- An AI-driven product recommendation system.
- A tool powered by machine learning to arrange products on websites.
Let’s look into each of the new tools in more detail.
Providing Personalised Search & Browsing For eCommerce Sites
E-commerce owners are set to have the capability to personalise what customers see when they browse and search an eCommerce website.
The technology works to enhance the usage of Google Cloud’s existing Retail Search solution, granting a more seamless shopping experience for online users.
The AI system powering the new tool can identify customers’ preferences by analysing their shopping behaviour, for example, looking into the items they view, add to their cart, and purchase.
It will utilise this information to adjust the search results and set priority products for a personalised customer experience.
However, the personalisation is specific to the retailer’s website, and not the customer’s activity on Google.
AI-Based Product Sorting for eCommerce Sites
The second new launch comes in the form of an AI-powered tool to help eCommerce websites improve the browsing and product discovery process for shoppers.
Powered by machine learning, the feature allows the optimisation of products on a retail site when shoppers select a category.
eCommerce sites traditionally arrange product results based on either categorised bestseller lists or manually created collections, for example putting a spotlight on clothing based on the season.
Developed by Google, the AI-driven system adopts a new sorting strategy, using historical data to improve how products are displayed. This is designed to increase relevance, accuracy, and chances of a sale.
The tool is now available to retailers.
A-I Driven Product Recommendations
The updates to Google Cloud’s Recommendations AI can help to adapt eCommerce sites, granting a more personalised, dynamic and helpful experience for individual customers.
A new feature, page-level optimisation, allows the site to determine the product recommendations displayed to a shopper dynamically.
As a result, it can reduce the need for time-consuming user experience testing and lead to higher levels of user engagement and sales.
Alongside this, a new revenue optimisation features users’ machine learning to offer better-suited product recommendations, potentially increasing the revenue per user session.
This new machine-learning model, produced in collaboration with DeepMind, considers the factors of; product categories, item prices and user behaviour on an eCommerce site to identify the ideal balance between revenue growth and a great user experience.
Finally, a new ‘buy it again’ model is dedicated to the customer’s past purchases to recommend future follow-up purchases.
All of the tools above are now available to all retailers on Google Cloud.
A-I Powered Shelf Checking for Online Retail Stores
Shelf-checking technology has been used by retailers for a while now, but its success has always been limited by the resources required to develop AI models to categorise and distinguish products.
All that is set to change with Google Cloud’s new AI solution to shelf-checking by helping retailers identify all types of products at scale based only on their visual and text features.
The tool turns the data into actionable insights, which eCommerce retailers can use to improve product availability, and increase visibility into the current inventory, as well as identify where restocks are required. Making the user experience all the greater.
This new technology is currently in preview but is soon set to be accessible to retailers globally. Google in a new update noted that a retailer’s data and image will remain their property, and the AI is only used to recognise products and price tags.
Thanks for reading,
Myk Baxter, eCommerce Consultant
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