To assist retailers in modernising their in-store shelf checking procedures and enhancing e-commerce sites with more fluid and natural online shopping experiences for customers, Google Cloud unveiled four new and updated AI technologies. NRF 2023 is the largest event for the retail industry.
By utilising Google’s library of facts about people, places, and things, a new shelf-checking AI solution, developed on Google Cloud’s Vertex AI Vision, shops can recognise billions of products to guarantee that in-store shelves are the proper size and well filled. Google Cloud also added a new customization AI capability and a new browsing feature to its Discovery AI solutions as an update to help retailers modernise their digital storefronts with more dynamic and user-friendly buying experiences. Finally, the Recommendations AI solution from Google Cloud has introduced new machine learning features that enable retailers to dynamically optimise their product ordering and recommendations panels on their e-commerce pages and provide tailored recommendations for repeat purchases.
According to Carrie Tharp, VP of Retail and Consumer at Google Cloud, “upheavals over the past five years have altered the retail industry and the tools merchants need to be more efficient, more engaging to their customers, and less subject to future shocks.” “The retail sector has a tonne of opportunities in spite of the uncertainties. The leaders of tomorrow will be those who use the most cutting-edge technological tools, like artificial intelligence and machine learning, to handle today’s most urgent in-store and online concerns.”
Retailers are helped to increase product availability by new shelf checking AI.
Retailers are concerned about the issue of little or no inventory on in-store shelves. A NielsenIQ investigation of on-shelf availability found that missing sales from bare shelves cost U.S. retailers $82 billion in 2021 alone. While retailers have experimented with various shelf-checking technologies for years, their efficacy has frequently been constrained by the time and money required to develop trustworthy AI models to detect and distinguish products, from the various flavours of jam and jelly to the numerous varieties of toothbrushes.
The new Google Cloud AI-powered shelf checking solution is now accessible in worldwide preview and can assist retailers in increasing the availability of products on their shelves, giving them a better understanding of how their shelves actually look, and determining where restocks are required. The shelf checking AI, which is based on Google Cloud’s Vertex AI Vision and powered by two machine learning models—a product recognizer and tag recognizer—allows retailers to overcome a very challenging challenge: how to identify products of all types at scale using only the visual and text features of a product, and then transform that data into useful insights.
Retailers don’t have to invest money, time, or effort into gathering data and building their own AI models. Google Cloud’s shelf checking AI can recognise objects from a variety of image formats shot at different angles and viewpoint points by drawing on Google’s database of billions of distinct entities—a particularly challenging assignment. The types of pictures that retailers can provide to the AI for shelf inspection will be quite flexible. A merchant might employ images from a camera mounted on the ceiling, a worker’s smartphone, or a robot patrolling the store checking out the shelves.
This technology is now under preview, but it will soon be generally accessible to shops around the world. A retailer’s data and photos remain their property, and the AI can only be used to identify products and price tags.
AI changes the experience of doing online window shopping.
Sometimes people are unsure about what they desire. They do this in an effort to find inspiration as they peruse websites or window shop.
Google Cloud today unveiled a new AI-powered browse feature in its Discovery AI solutions for shops in an effort to assist merchants in making the online browsing and product discovery experience for customers more contemporary, swifter, intuitive, and satisfying. Once customers choose a category, such as “women’s jackets” or “kitchenware,” the feature employs machine learning to choose the best ordering of products on a retailer’s e-commerce site.
Using past data, the AI gradually learns the best product ordering for each page of an e-commerce site, improving the products that are displayed for accuracy, relevancy, and the possibility of a sale. The functionality can be used on a range of e-commerce website pages, including collection, explore, brand, and landing pages.
Ecommerce websites have traditionally categorised product results based on either category bestseller lists or guidelines that were manually created, such as selecting which clothing to spotlight based on seasonality. This innovative browsing technology eliminates the need for manual intervention, self-curates, learns from experience, and self-corrects. Additionally to generating appreciable increases in income per visit, it can spare businesses the labour and money associated with manually curating several ecommerce pages. Retailers around the world can now purchase the new tool generally, and it supports 72 languages.
Machine learning produces more individualised search and browsing outcomes.
According to research commissioned by Google Cloud, 86% of consumers want a company that is aware of their interests and preferences, and 75% of consumers prefer brands that tailor interactions and outreach to them.
Google Cloud today unveiled a new AI-driven personalisation tool that tailors the results a customer sees when they search and explore a retailer’s website in order to assist companies in creating more fluid and intuitive online buying experiences. The technology significantly enhances the capabilities of the current Retail Search solution and the new browse offering from Google Cloud.
The artificial intelligence (AI) powering the new customization capabilities is a product-pattern recognizer that analyses user behaviour on an e-commerce site, including clicks, cart contents, purchases, and other data, to identify the tastes and preferences of visitors. For a customised outcome, the AI then pushes products that match those tastes up in search and browsing rankings. The customised search and browse results that are shown to a customer are entirely determined by their activities on the e-commerce website of that particular merchant and are not associated with their Google account activity. The customer is recognised either by a first-party cookie on the website or by a profile they have set up on the retailer’s website.
Customers own and control their data, just like with all Google Cloud products; information about customer preferences remains with the store. Global retailers can now easily access this technology.
AI improves recommendations, which boosts the profit line for retailers.
For good reason, product suggestion algorithms are now a crucial part of every retailer’s ecommerce strategy: by 2026, online retail sales are predicted to surpass $8 trillion. Retailers have, however, long struggled to choose which panels to show on their websites, how to best order them, and how to coordinate content that is both personalised and relevant. Recommendations AI from Google Cloud employs machine learning to assist merchants in providing customers with product recommendations.
Today’s updates to Recommendations AI can enhance how tailored, dynamic, and beneficial a retailer’s ecommerce assets are for certain customers. An e-commerce site, for instance, can now decide dynamically which product recommendation panels to specifically display to a customer thanks to a new page-level optimization function. Additionally, page-level optimization can increase user engagement and conversion rates while reducing the need for resource-intensive user experience testing.
Furthermore, a recently included function for revenue optimization employs machine learning to provide better product recommendations that can increase income per user session on any e-commerce site. An ecommerce site’s product categories, item prices, and customer clicks and conversions are combined in a machine learning model developed in partnership with DeepMind to strike the ideal balance between long-term consumer pleasure and revenue boost for retailers. A new buy-it-again model, which uses a customer’s past purchases to tailor recommendations for future repeat purchases, is the last one.
Recommendations AI has demonstrated double-digit uplifts in conversion and clickthrough rates in studies controlled by retailers using the technology when compared to baseline recommendation systems utilised by Google Cloud clients. Retailers now have access to the new page-level optimization, revenue optimization, and buy-it-again models on a worldwide scale.
