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NRF is hosting NRF 2023, the largest event in the retail industry. It kicks off Monday at the Javits Convention Center in New York City. But today, ahead of the “retail extravaganza,” Google Cloud introduced a number of new and updated artificial intelligence (AI) technologies that will help retailers improve in-store shelf control, improve online shopping, provide a more personalized search, and make better recommendations. .
Amy Eschliman, general manager of retail solutions at Google Cloud, says that since the pandemic, online shoppers want a more natural and seamless experience.
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“Before the pandemic, 80% of transactions worldwide were in stores, but the shift to digital was happening all the time; COVID flipped the switch overnight,” he told VentureBeat in an email. “In-store shopping has definitely started again, but shoppers will never be the same again.”
More personalized and intuitive online shopping
Eschliman said the new AI-powered personalization feature personalizes the results a customer sees when searching for and browsing a retailer’s website. This is done to meet the new expectations of consumers.
Examine a customer’s clicks, cart, purchases, and other actions on an ecommerce site to find out what they like and don’t like. The AI then moves up the search and browse rankings for products that match those preferences. This makes the results more personal and useful.
“We know more than ever that shoppers want this kind of personalized experience,” he said. He also said research paid for by Google Cloud found that 75% of shoppers prefer brands that personalize interactions and communicate with them, and 86% want a brand that knows their interests and preferences.
Browse AI is a new part of Google Cloud’s Discovery AI solutions for retailers. It uses machine learning to place products in the best order on an e-commerce site after shoppers choose a category, like “women’s jackets” or “kitchenware.”
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In the past, e-commerce sites sorted product results by categories on best-seller lists or by human-written rules, like deciding by hand which clothes to feature depending on the season.
Browse AI takes a new approach by self-healing and learning from experience. This saves retailers the time and money of manually curating multiple eCommerce pages.
The new tool is now available to retailers around the world and can be used in 72 different languages.
Google Cloud AI-Powered Shelf Checkout
NielsenIQ did an analysis of what was on the shelves and found that empty shelves would cost US retailers $82 billion in sales in 2021 alone.
Built on Google Cloud’s Vertex AI Vision and powered by two machine learning models, a product recognizer and a label recognizer, Google Cloud’s new AI-powered shelf management solution is available globally in version preliminary. It also helps solve a difficult problem, which is how to find all sorts of products at scale based only on their visual and textual features, and then turn that data into useful information.
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Eschliman said the solution uses Google’s huge database of facts to allow retailers to identify billions of products and make sure their shelves are stocked. She said: “This large data set and Google Cloud’s cutting-edge AI can help retailers better manage their in-store stock.” And she solves “the age-old industry problem of retailers not knowing what’s on their shelves at any given time and where they need to restock.”
AI ups the retail recommendation game
Google Cloud has also announced enhancements to AI Recommendations, which will make e-commerce even more personalized and dynamic.
An e-commerce site can now dynamically choose which product recommendation boards to present to a customer thanks to a new page-level optimization feature. Page-level optimization also reduces the need for time-consuming user experience testing and has the potential to increase user engagement and conversion rates.
In addition, a recently added revenue optimization feature uses a machine learning model developed in collaboration with DeepMind that combines an e-commerce site’s product categories, item prices, and customer clicks and conversions to find the right right balance between long-term customer satisfaction and increased revenue for retailers. .
Finally, a new buy-again model uses a customer’s past transactions to make individualized recommendations for future repeat purchases.
Retailers can get buried in data
Eschliman said many retailers are still in the early stages of using their real-time customer, product and supply chain data to improve business operations and the customer experience.
“But the truth is that in retail it’s easy to get lost in all the data,” he said. “Artificial intelligence and machine learning are the best ways to solve the problems that retailers face today because they can process and analyze vast amounts of data in real time, detect patterns and trends, and make predictions and decisions that are more accurate. and reliable over time. ”
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