State-of-the-art shelf checking AI utilizes Google’s recognition of billions of products
- New Google Cloud Discovery AI features to power e-commerce sites with modern browsing capabilities, personalized shopping experiences, and better product recommendations
- Google Cloud and Accenture will bring the best of Google Cloud technology to ai.RETAIL and collaborate on a new initiative to help retailers modernize stores
Google Cloud has introduced four new artificial intelligence (AI) innovations to help retailers transform their in-store shelf checking processes and enhance their e-commerce sites with fluid and natural online shopping experiences for consumers. Google Cloud has also announced new initiatives with Accenture to help retailers modernize their businesses and benefit from cloud technology, including deep integrations with Accenture’s widely-adopted ai.RETAIL platform.
“Upheavals in the past few years have reshaped the retail landscape and retailers are now seeking new ways to be more efficient, more compelling to shoppers, and less exposed to future shocks,” said Sameer Dhingra, Director, Retail and Consumer, Asia Pacific, Google Cloud. “The leaders of tomorrow will be those who address today’s most pressing in-store and online challenges with the newest AI tools. Our work with Accenture will also help retailers quickly adopt integrated solutions that amplify the true benefits of AI, so that they can holistically understand their business across functional boundaries and continuously optimize their offerings and operations to thrive in a complex retail environment.”
1. New shelf checking AI helps retailers improve product availability
With shoppers visiting competitor stores instead when they can’t find the product that they are looking for—leading to a loss in sales and long-term loyalty—addressing the problem of low or no inventory on in-store shelves remains a priority. While retailers have tried different shelf checking technologies for years, their effectiveness has 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 different types of toothbrushes.
Now available in preview globally, Google Cloud’s new 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. Built on Google Cloud’s Vertex AI Vision and powered by two machine learning (ML) models—a product recognizer and tag recognizer—the shelf checking AI enables retailers 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 will no longer have to expend time, effort, and investment into data collection and training their own AI models. Leveraging Google’s database of billions of unique entities, Google Cloud’s 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.
This technology is expected to be generally available to retailers globally in the coming months. More importantly, a retailer’s imagery and data remains their own and the AI can only be used for the identification of products and price tags.
2. AI transforms the digital window shopping experience
People do not always know what they want. That’s why they window shop or browse through websites, looking for inspiration. To help retailers make the online browsing and product discovery experience more intuitive and fulfilling for shoppers, Google Cloud has introduced a new AI-powered browse feature in its Discovery AI solutions for retailers. This capability uses ML to optimize the order of products (i.e., which products the shopper sees first) on a retailer’s e-commerce site once shoppers choose a category, such as “women’s jackets” or “kitchenware.”
Over time, the AI learns the ideal product ordering for each page on an e-commerce site using historical data, optimizing how and what products are shown for accuracy, relevance, and likelihood of being bought. The feature can be used on a variety of e-commerce site pages, from browse, brand, and landing pages, to navigation and collection pages.
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. This browse technology takes a whole new approach by self-curating, learning from experience, and requiring no manual intervention. In addition to driving significant improvements in revenue per visit, it can also save retailers the time and expense of manually curating multiple e-commerce pages. Supporting 72 languages including Bahasa Indonesia, Bahasa Melayu, Thai, Simplified Chinese, Traditional Chinese, and Vietnamese, the new tool is now generally available to retailers worldwide.
3. More personalized search and browsing results with ML
Research commissioned by Google Cloud found that 75% of shoppers prefer brands that deliver personalized interactions and outreach. To help retailers create more fluid and intuitive online shopping experiences, Google Cloud has 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 supercharges the capabilities of Google Cloud’s new browse feature 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 e-commerce site, such as their clicks, cart, purchases, and other information, to determine shopper taste and preferences. The AI then moves up products that match those preferences 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 e-commerce 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.
4. AI increases retailers’ bottom line with better recommendations
Product recommendation systems are now a critical component of any retailer’s e-commerce strategy for good reason: online retail sales are expected to reach more than US$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 ML to help retailers bring product recommendations to their shoppers.
New upgrades to Recommendations AI can make a retailer’s e-commerce properties even more personalized, dynamic, and helpful for individual customers. For example, a new page-level optimization feature now enables an e-commerce site to dynamically decide what product recommendation panels to 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 newly added revenue optimization feature uses ML to offer better product recommendations that can lift revenue per user session on any e-commerce site. A ML model, built in collaboration with DeepMind, combines an e-commerce 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.
Compared to baseline recommendation systems used by Google Cloud customers, Recommendations AI has shown double digit uplift in conversion and clickthrough rates in experiments controlled by retailers using the technology. The new page-level optimization, revenue optimization, and buy-it-again models are now globally available to retailers.
ai.RETAIL for Google Cloud
Accenture’s ai.RETAIL is an integrated solution that helps retailers better utilize data and AI to optimize common systems and programs, such as customer acquisition, pricing and promotions, assortment, and supply chains. Retailers can now deploy the ai.RETAIL platform on Google Cloud, meaning it is extended to Google Cloud’s trusted infrastructure and is integrated with multiple Google Cloud products and capabilities. The new features and benefits of the solution include:
- Centralized supply chain analysis: ai.RETAIL includes a supply chain control tower powered by Accenture’s Intelligent Supply Chain Platform. With deeper integration across Google Cloud products like Looker and BigQuery, customers can now better organize data and provide a real-time view of their most critical supply chain metrics, including procurement, logistics, inventory, and sales. Retailers can then run “what if” simulations, calibrate demand forecasting, improve inventory planning, formulate strategies for supply chain segmentation, and more.
- Personalized customer experiences: ai.RETAIL now leverages Google Cloud’s Discovery AI solutions for retail, which can reduce search abandonment through Google-quality search capabilities, deliver personalized recommendations at scale, and help shoppers find products using images. Additional integrations with Accenture’s Customer Data Architecture and Google Cloud’s Customer Data Platform will let retailers eliminate data silos and drive predictive marketing engagements with AI and ML.
- Assortment optimization: Using BigQuery, Looker, and Vertex AI, ai.RETAIL now features new store clustering capabilities that will help retailers identify, group, and optimize stores with similar characteristics, enhancing strategies for assortment, space management, and inventory. This includes recommendations for whether to maintain, reduce, or drop specific products, which can be filtered by individual stores or store clusters, and ultimately improve overall sales performance.
“With shifting consumer buying habits, now more than ever, retailers need to invest in building a digital core – which includes a solid data foundation, ML, and AI. Powered by the cloud, these technologies can help our clients spot trends, make decisions faster, and repeatedly reset the business as the market changes,” said Sridhar Subramanian, Managing Director of Accenture’s Google Business Group in Asia Pacific. “With the best of Accenture’s integrated ai.RETAIL platform and Google Cloud technology, companies can now access products and capabilities to help improve consumer engagement and conversions, and make their supply chains more sustainable.”
Google Cloud and Accenture are also collaborating on a broad, new initiative to address complex challenges facing retailers today, including applying intelligence from ai.RETAIL to help businesses optimize their customer, workforce, and storefront experiences, and utilizing other technologies and offerings from both companies. With improved knowledge of end-to-end operations, retailers will be able to apply Accenture and Google Cloud technology to modernize fundamental components of their business. For example, using Google Distributed Cloud Edge technology, organizations will be able to seamlessly integrate and scale cloud infrastructure to their stores, factory floors, and more.