In 2022, NielsenIQ launched an on-shelf availability (OSA) barometer in the U.S. The release of the OSA barometer followed supply chain shortages during the pandemic, allowing retailers to measure and benchmark out-of-stock to prevent supply chain disruptions and maintain on-shelf availability. With NielsenIQ’s unmatched data and technology, retailers can now pinpoint risk areas before they become lost sales.
In the same year, Google launched a preview of Vertex AI Vision. Today, Vertex AI Vision is a fully managed end-to-end application development environment that lets enterprise developers quickly build, deploy, and manage computer vision applications for their industry. Vertex AI Vision enables dev teams to reduce build time from weeks to hours at a fraction of the cost, spanning hundreds of industries, including retail, automotive, and manufacturing.
“On-shelf availability is a critical aspect of business intelligence for CPG retailers. Retailers who pay active attention to their OSA conditions and who track shopper behavior, including basket composition data, will be better positioned to recognize pantry-loading behaviors when they occur. They can then take proactive steps to preserve on-shelf availability and maintain service levels for shoppers.” – Jean-Baptiste Delabre, VP of Retail Analytics, NielsenIQ
The problem of low or no inventory on in-store shelves is a troubling one for retailers. According to a NielsenIQ analysis of on-shelf availability, empty shelves cost U.S. retailers $82 billion in missed sales in 2021 alone. While retailers have tried different shelf-checking technologies for years, their effectiveness has often been limited by the resources needed to create reliable AI models to detect and differentiate products—from the different flavors of jam and jelly, to the dozens of types of toothbrushes.
In 2023, NIQ’s Activate Platform became available available on Google Cloud and on the Google Cloud Marketplace. Together, the AI-powered shelf checking solution can help retailers improve on-shelf product availability, provide better visibility into what their shelves actually look like, and help them understand where restocks are needed. AI product recognition 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.
Retailers don’t have to expend time, effort, and investment into data collection and training their own AI models. Leveraging the latest AI product technology advancements, retailers can optimize inventory, manage costs, and maximize revenue by assuring the right product is in the right place at the right time. With Google’s Vertex AI Vision database of billions of unique entities, shelf checking AI can identify products from a variety of image types taken at different angles and vantage points—an especially difficult task. Retailers will have a high degree of flexibility in the types of imagery they can supply to the shelf checking AI. For example, a retailer can use imagery from a ceiling-mounted camera, an associate’s mobile phone, or a store-roaming robot on shelf-checking duty.
Research commissioned by Google Cloud 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.
To help retailers create more fluid and intuitive online shopping experiences, Google Cloud today introduced a new AI-driven personalization capability that customizes the results a customer gets when they search and browse a retailer’s website. The technology turbo-charges the capabilities of Google Cloud’s new browse offering and existing Retail Search solution.
The AI underpinning the new personalization capability is a product-pattern recognizer that uses a customer’s behavior on an ecommerce site, such as their clicks, cart, purchases, and other information, to determine shopper taste and preferences. The AI then moves products that match those preferences up in search and browse rankings for a personalized result. A shopper’s personalized search and browse results are based solely on their interactions on that specific retailer’s ecommerce site, and are not linked to their Google account activity. The shopper is identified either through an account they have created with the retailer’s site, or by a first-party cookie on the website.
As with all Google Cloud solutions, customers own and control their data—information on customer preferences stays with the retailer. This technology is now generally available to retailers worldwide.
In July 2024, NielsenIQ (NIQ) announced the availability of NIQ Activate in the Microsoft Azure Marketplace. NIQ retailers and Consumer Packaged Goods (CPG) companies can leverage NIQ’s industry-leading data insights and personalization to enable deeper collaboration, drive performance, and engage customers in targeted ways. Microsoft Azure’s world-class AI and data analytics capabilities help businesses make better decisions.
Ecommerce Impact
Product recommendation systems are now a critical component of any retailer’s ecommerce strategy for good reason: online retail sales are expected to reach more than $8 trillion by 2026. However, retailers have long had difficulty determining which panels to display on their websites, how to effectively arrange them, and how to coordinate content that is both relevant and personalized. Google Cloud’s Recommendations AI solution uses machine learning to help retailers bring product recommendations to their shoppers.
New upgrades to Recommendations AI, announced today, can make a retailer’s ecommerce properties even more personalized, dynamic and helpful for individual customers. For example, a new page-level optimization feature now enables an ecommerce site to dynamically decide what product recommendation panels to uniquely show to a shopper. Page-level optimization also minimizes the need for resource intensive user experience testing, and can improve user engagement and conversion rates.
In addition, a recently added revenue optimization feature uses machine learning to offer better product recommendations that can lift revenue per user session on any ecommerce site. 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. Finally, a new buy-it-again model leverages a customer’s shopping history to provide personalized recommendations for potential repeat purchases.
A full suite of Google Vertex AI and Cloud ML products can be found at https://cloud.google.com/vertex-ai. The new inventory technologies, including the personalization AI capability, browse feature, and updates to Recommendations AI (page level optimization machine learning model, revenue optimization model, and buy-it-again model) are revolutionizing retail and ecommerce. To learn more visit Google Cloud’s website here.
Companies who are leveraging the power of product AI technology can dive into consumer and shopper insights at NielsenIQ.com
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