- State-of-the-art shelf checking AI solution utilizes
Google's recognition of billions of products
- Google Cloud's Discovery AI solutions launches new AI
features to power ecommerce sites with modern browsing
capabilities, personalized shopping experiences, and better product
recommendations
NEW
YORK, Jan. 13, 2023 /PRNewswire/ -- Ahead of NRF
2023, the retail industry's largest event, Google Cloud today
introduced four new and updated AI technologies to help retailers
transform their in-store shelf checking processes and enhance their
ecommerce sites with more fluid and natural online shopping
experiences for customers.
A new shelf checking AI solution, built on Google
Cloud's Vertex AI Vision, utilizes Google's database of facts
about people, places and things, giving retailers the ability to
recognize billions of products to ensure in-store shelves are
right-sized and well-stocked. Furthermore, in an update to its
Discovery AI solutions, Google Cloud introduced a new
personalization AI capability and new AI-powered browse feature to
help retailers upgrade their digital storefronts with more dynamic
and intuitive shopping experiences. Finally, Google
Cloud's Recommendations AI solution launched new machine
learning capabilities that empower retailers to dynamically
optimize product ordering and recommendations panels on their
ecommerce pages and deliver personalized suggestions for repeat
purchases.
"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."
New shelf checking AI helps
retailers improve product availability
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.
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 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.
Retailers don't 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.
Now in preview, this technology is expected to be generally
available to retailers globally in the coming months. 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.
AI transforms the digital window
shopping experience
People don't 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 modern, faster, intuitive, and fulfilling for
shoppers, Google Cloud today introduced a new AI-powered browse
feature in its Discovery AI solutions for retailers. The capability
uses machine learning to select the optimal ordering of products on
a retailer's ecommerce site once shoppers choose a category, like
"women's jackets" or "kitchenware."
Over time, the AI learns the ideal product ordering for each
page on an ecommerce site using historical data, optimizing how and
what products are shown for accuracy, relevance, and likelihood of
making a sale. The feature can be used on a variety of ecommerce
site pages, from browse, brand, and landing pages, to navigation
and collection pages.
Historically, ecommerce 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,
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 ecommerce pages. The new tool is now
generally available to retailers worldwide supporting 72
languages.
More personalized search and
browsing results with machine learning
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.
AI increases retailers' bottom
line with better recommendations
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.
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.
Technology availability and Google
Cloud at NRF
Google Cloud's shelf checking AI tool is now in preview
globally. The new ecommerce 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 all now
globally available to retailers. NRF event attendees can learn more
about the latest, AI-driven innovations for retailers at Google's
event booth #5607.
About Google Cloud
Google Cloud accelerates every organization's ability to
digitally transform its business. We deliver enterprise-grade
solutions that leverage Google's cutting-edge technology – all on
the cleanest cloud in the industry. Customers in more than 200
countries and territories turn to Google Cloud as their trusted
partner to enable growth and solve their most critical business
problems.
View original
content:https://www.prnewswire.com/news-releases/google-cloud-unveils-new-ai-tools-for-retailers-301721298.html
SOURCE Google Cloud