Google Cloud said today it’s launching four new artificial intelligence technologies aimed at retailers, just ahead of the National Retail Federation’s annual conference, NRF 2023.
The new technologies are designed to help retailers transform in-store shelf checking processes and enhance e-commerce operations, providing more fluid and natural online shopping experiences, Google said.
They include a new “self-checking AI solution” that’s built on Vertex AI and leverages Google’s extensive databases to help retailers recognize “billions of products” and ensure that in-store shelves are well-stocked. In addition, there are new personalization AI capabilities and a new Browse AI feature to help retailers update their digital storefronts, plus a Recommendations AI tool for dynamically optimizing product ordering and recommendation panels on e-commerce websites.
Google Cloud’s new shelf checking tool is designed to ensure that retailer’s shelves are always well-stocked with a good selection of each kind of product so that consumers always have options, the company explained. It cited a study by NielsenIQ that shows how U.S. retailers suffered $82 billion in missed sales during 2021 as a result of failing to keep their shelves stocked. Clearly, consumers want to be able to find the items they’re looking for and will be frustrated if they’re not available, so retailers need a more reliable solution, Google explained.
The company believes it has created that solution, with its AI-powered shelf checking tool designed to provide more visibility into what their shelves actually look like in real time, so they can better understand when they need to be restocked. Available in preview now, the new tool is powered by two machine learning algorithms — a product recognition system and a tag recognizer — that make it able to identify almost any kind of product.
The basic premise is that retailers don’t have to assign employees to go around checking shelves to see which ones must be restocked. Instead, Google Cloud can accumulate this data for them in real time, and then explore it to provide insights on which products must be replaced. What’s more, Google Cloud said, the tool is extremely flexible with regard to the kinds of imagery it supports. Retailers can use in-store cameras to watch over their shelves, or else they can connect the system to an employee’s smartphone or even a store-roaming robot.
In addition to helping retailers in the physical world, Google Cloud also wants to boost the digital side of their business with the launch of Browse AI, a new capability within its suite of Discovery AI solutions. Browse AI uses machine learning algorithms to decide on the optimal order of products listed on a retailer’s e-commerce site for each category, be it kitchenware, women’s shirts or something else.
Browse AI, available now in 72 languages, is all about helping retailers to create a faster, more intuitive and fulfilling online shopping experience, Google said. Over time, the algorithm will learn the ideal order in which it should list each category of product, based on the consumer’s historical preferences, relevance in terms of what the consumer is looking for, and the likelihood of a sale being made.
“Historically, e-commerce sites have sorted product results based on either category bestseller lists or human-written rules, like manually determining what clothing to highlight based on seasonality,” Google explained. “Browse AI takes a whole new approach, self-curating, learning from experience, and requiring no manual intervention.”
Elsewhere, Google said its existing Retail Search solution, which embeds Google’s Search capabilities into e-commerce websites, is being updated with a new AI-powered personalization capability available now that customizes each user’s search results. Google said it’s based on a new product-pattern recognizer capability that draws on the customer’s historical behavior — the things they click on or purchase, for instance — to determine their personal preferences.
Using this information, the AI then prioritizes products matching those preferences in its search results. So each customer will get unique search results that are optimized to increase their likelihood of finding what they want and making a purchase, Google said.
Finally there’s Google’s new Recommendations AI tool, which does a similar thing to the search personalization AI, only for suggestions and recommendations. According to Google, product recommendation systems are a must have because online retail sales will top $8 trillion a year by 2026. Recommendations AI works by delivering personalized recommendations for each individual shopper.
For instance, there’s a new page-level optimization feature that allows e-commerce sites to dynamically decide which product recommendation panels will appear for each user. This, Google says, minimized the need for intensive user experience testing, and it can improve both user engagement and conversion rates.
Recommendations AI also comes with a revenue optimization feature that aims to increase “revenue per user session” by combining product categories, item prices and customer clicks and conversions to find the right balance between long-term satisfaction for customers and revenue lift for retailers. Finally, there’s a new buy-it-again model that looks at customers’ shopping history to provide a list of recommended repeat purchases.
Google says that in testing by retailers, Recommendations AI has shown a double-digit increase in conversion and click-through rates. The new tool is available globally now, it said.
“Upheavals over the last few years have reshaped the retail landscape and the tools retailers need to be more efficient, more compelling to their customers, and less exposed to future shocks,” said Carrie Tharp, vice president of retail and consumer at Google Cloud. “Despite uncertainty, the retail industry has enormous opportunity. The leaders of tomorrow will be those who address today’s most pressing in-store and online challenges with the newest technology tools, such as artificial intelligence and machine learning.”
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