Google Cloud is launching four new and updated AI tools to provide customers with a smoother online shopping experience and assist retailers with in-store inventory management. These features can support eCommerce sites to transform the digital window shopping experience, and assist customers with modern browsing capabilities, personalized shopping experiences, and better product recommendations. Along with relying on affordable digital marketing services, retailers can use these tools to transform their in-store shelf checking processes and enhance their ecommerce sites with more fluid and natural online shopping experiences for customers.
The Four New and Updated AI Technologies
- AI-powered in-store shelves checking solutions
- Personalized search and browsing experiences
- An AI-driven product recommendation system
- Machine learning to arrange products on websites
“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, VP of Retail and Consumer, 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.”
- Shelf-checking AI
An AI-powered solution for checking in-store shelves has been introduced, which improves the retailer’s product availability. Built on Google Cloud’s Vertex AI Vision, these shelf-checking AI solutions utilize Google’s database of facts about people, places and things, giving retailers the ability to recognize billions of products to ensure that in-store shelves are correctly-sized and well-stocked.
This tool can simplify retailer’s job, by –
- providing better visibility into what their shelves actually look like
- helping them understand where restocks are needed
Google Cloud’s press release explains that, “Built on Google Cloud’s Vertex AI Vision and powered by two machine learning models-a product recognizer and tag recognizer-the shelf checking AI enables retailers to solve a very difficult problem: how to identify products of all types, at scale, based solely on the visual and text features of a product, and then translate that data into actionable insights.”
With this tool, retailers can save their time, effort, and investment on data collection and training their own AI models. It can easily identify products from a variety of image types taken at different angles and vantage points, which is an otherwise hectic task. This technology is expected to be generally available to retailers globally in the coming months. Retailer’s imagery and data remains their own and they can use AI for the identification of products and price tags.
- Personalized search and browsing experience for online stores
Customers are often confused as to what to buy, for which they window shop or browse websites, looking for inspiration. Another new AI-powered browse feature in Google Cloud’s Discovery AI is designed to help retailers make their online browsing and product discovery experience more modern, faster, intuitive, and fulfilling for shoppers. This feature makes use of machine learning technologies to choose the optimal ordering of products on a retailer’s ecommerce site once shoppers choose a category. This feature is ideal for diverse ecommerce site pages – browse, brand, and landing pages, to navigation and collection pages.
Unlike the traditional ways of sorting product results based on either category bestseller lists or human-written rules, this technology takes a whole new approach – self-curating, learning from experience, and requiring no manual intervention.
Generally available to retailers worldwide supporting 72 languages, this feature can-
- drive significant improvements in revenue per visit, and
- save retailers the time and expense of manually curating multiple ecommerce pages
- Machine learning for more personalized search and browsing results
Another efficient AI tool uses machine learning to arrange products on websites. Based on Google Cloud’s research, it has been found that –
- 75% of shoppers prefer brands that personalize interactions and outreach to them, and
- 86% want a brand that understands their interests and preferences
Google Cloud’s new AI-driven personalization capability helps retailers to create more fluid and intuitive online shopping experiences. This feature not only customizes the results when consumers search and browse a retailer’s website, but also turbo-charges the capabilities of Google Cloud’s new browse offering and existing Retail Search solution. This product-pattern recognizer uses a customer’s behavior such as their clicks, cart, purchases, and other information on an online store, to determine shopper taste and preferences.
Those preferences are then matched up in search rankings for getting personalized results. This personalized search and browse results are based solely on their interactions on that specific retailer’s ecommerce site, and has got nothing to do with their Google account activities.
Generally available to retailers worldwide, this capability also allows customers to own and control their data. Information on customer preferences will stay with the retailer.
- AI-driven, better product recommendation system
Product recommendation systems now play a critical role in creating effective ecommerce SEO strategies. Google Cloud’s Recommendations AI solution uses machine learning to help online businesses bring product recommendations to their shoppers. With this tool, retailers can get rid of difficulties in determining which panels to display on their websites, how to effectively arrange them, and how to coordinate content that is both relevant and personalized. This can make a retailer’s ecommerce properties more tailored, lively and helpful for individual customers.
New upgrades to Recommendations AI are –
- Page-level optimization that minimizes the need for resource intensive user experience testing, and can improve user engagement and conversion rates.
- Revenue optimization that offers better product recommendations that can lift revenue per user session on any ecommerce sites. Google explains that, “A machine learning model, built in collaboration with DeepMind, combines an ecommerce site’s product categories, item prices, and customer clicks and conversions to find the right balance between long-term satisfaction for shoppers and revenue lift for retailers. “
- Buy-it-again model that uses a customer’s shopping history to provide personalized recommendations for potential repeat purchases.
All these upgrades are now globally available to retailers. Compared to conventional recommendation systems, Recommendations AI has shown increased conversion and click-through rates in experiments controlled by retailers using the technology.
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