JAKARTA - Google Cloud introduced four new artificial intelligence (AI) innovations to help retailers transform store store store store store window checks, and also enhance their e-commerce sites with a smooth online shopping experience for consumers.
AI checking new storefronts helps retailers increase product availability
Retailers have been trying various storefront checking technologies over the years, but their effectiveness is still limited by the resources needed to create reliable AI models for detecting and distinguishing products.
Therefore, Google Cloud now provides a preview of new AI-supported storefront checking solutions globally, which can help retailers increase product availability in storefronts, provide better visibility about actual storefront views, and help them understand which parts require reduction.
"Developed in Google Cloud's Vertex AI Vision and supported by two machine learning (ML) models, product identification and tag recognition. The AI for storefront examining allows retailers to identify products of all types, on a large scale," Google said in a statement received, Monday, March 20.
With this technology, retailers no longer have to spend time, energy, and investment on data collection and training their own AI models, and will also have a high level of flexibility in the type of image they can give to AI checking storefronts.
This technology is expected to be publicly available to retailers globally in the coming months.
AI has changed the experience of digital window shopping
To help retailers make online search experience and product discovery more intuitive and satisfying for buyers, Google Cloud introduces a new browsing feature supported by AI through Discovery AI for retailers.
The sophistication of this tool uses ML technology to optimize product order (for example, which product the buyer first sees) on the retailer's e-commerce site after the buyer chooses the category, such as "female jacket" or "kitchen equipment".
This feature can be used on various e-commerce site pages, from search, brand, and direction pages, to navigation pages and collections.
"In addition to encouraging a significant increase in revenue per visit, this can also save time and retailer fees to create several pages manually. This new tool supports 72 languages including Indonesian, Malay, Thai, Mandarin Simplified and Traditional, as well as Vietnamese and is now available in general for retailers around the world," explained Google.
More customized search results with Machine Learning
Research conducted by Google Cloud found that 75 percent of buyers prefer brands that provide customized interactions and reach.
To help retailers create a smoother and more intuitive online shopping experience, Google Cloud also introduces new AI-based customization capabilities that customize the results that customers get when they browse and explore retailer sites. This technology improves the capabilities of Google Cloud's new browsing features and existing Retail Search solutions.
AI that supports the new customization capabilities is product pattern identification that uses customer behavior on e-commerce sites, such as clicks, baskets, purchases, and other information, to determine shopper tastes and preferences.
Then, AI will raise products that match those preferences in search and browse rankings for customized results. The results of searches and searches customized by buyers are based solely on their interactions on certain e-commerce sites owned by retailers and are not linked to their Google account activities. This technology is now publicly available for retailers around the world.
AI increases retailer profits with better recommendations
According to Google, retailers have had difficulty determining which panels will be displayed on their websites for a long time, how to customize them effectively, and how to coordinate relevant and personalized content.
Google Cloud's AI Recommendations solution uses ML to help retailers provide product recommendations to their buyers.
The new upgrade to AI Recommendations can make retailer e-commerce properties more personal, dynamic, and beneficial for individual customers. For example, the new page-level optimization feature now allows e-commerce sites to dynamically decide the product recommendation panel to be displayed to buyers.
The ML model, created in collaboration with DeepMind, combines the product categories of e-commerce sites, goods prices, and clicks and customer conversions to find the right balance between long-term satisfaction for buyers and an increase in revenue for retailers.
The English, Chinese, Japanese, Arabic, and French versions are automatically generated by the AI. So there may still be inaccuracies in translating, please always see Indonesian as our main language. (system supported by DigitalSiber.id)
