Google’s Intelligent Products Essentials Assist Manufacturers in Product Development Journey
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Expectations for both consumer and commercial products have changed. Consumers want products that evolve with their needs, adapt to their preferences, and stay up-to-date over time. Manufacturers, in turn, need to create products that provide engaging customer experiences not only to better compete in the marketplace, but also to provide new monetization opportunities.
However, embedding intelligence into new and existing products is challenging. Updating hardware is costly, and existing connected products do not have the capability to add new features. Furthermore, manufacturers do not have sufficient customer insights due to product telemetry and customer data silos, and may lack the AI expertise to quickly develop and deploy these features.
That’s why today we’re launching Intelligent Products Essentials, a solution that allows manufacturers to rapidly deliver products that adapt to their owners, update features over-the-air using AI at the edge, and provide customer insights using analytics in the cloud. The solution is designed to assist manufacturers in their product development journeys—whether developing a new product or enhancing existing ones.
With Intelligent Products Essentials, manufacturers can:
- Personalize customer experiences: Provide a compelling ownership experience that evolves over the lifetime of the product. For example, a chatbot that contextualizes responses based on product status and customer profile.
- Manage and update products over-the-air: Deploy updates to products in the field, gather performance insights and evolve capabilities over time with monetization opportunities.
- Predict parts and service issues: Detect operating thresholds, anomalies and predict failures to proactively recommend service using AI, reducing warranty claims, decreasing parts shortages and increasing customer satisfaction.
In order to help manufacturers quickly deploy these use cases and many more, Intelligent Products Essentials provides the following:
- Edge connections: Connect and ingest raw or time-series product telemetry from various device platforms utilizing IoT Core or Pub/Sub and enable deployment and management of firmware over-the-air and machine learning models with Vertex AI at the edge.
- Ownership App Template: Easily build connected product companion apps that work on smartphones, tablets, and computers. Use a pre-built API and accompanying sample app that can incorporate product or device registration, identity management, and provide application behavior analytics using Firebase.
- Product fleet management: Manage, update and analyze fleets of connected products via APIs, Google Kubernetes Engine, and Looker.
- AI services: Create new features or capabilities for your products using AI and machine learning products such as DialogFlow, Vision AI, AutoML, all from Vertex AI.
Enterprise data integration: Integrate data sources such as Enterprise Asset Management (EAM), Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) systems and others using Dataflow and BigQuery.
Intelligent Products Essentials helps manufacturers build new features across consumer, industrial, enterprise, and transportation products. Manufacturers can implement the solution in-house, or work with one of our certified solution integration partners like Quantifi and Softserve.
“The focus on intelligent products that Google Cloud is deploying provides a digital option for manufacturers and users. At its heart, systems like Intelligent Product Essentials are all about decision making. IDC sees faster and more effective decision-making as the fundamental reason for the drive to digitize products and processes. It’s how you can make faster and more effective decisions to meet heightened customer expectations, generate faster cash flow, and better revenue realization,” said Kevin Prouty, Group Vice President at IDC. “Digital offerings like Google’s Intelligent Product Essentials potentially go the last mile with the ability to connect the digital thread all the way through to the final user.”
Customers adopting Intelligent Products Essentials
GE Appliances, a Haier company, are enhancing their appliances using new AI-powered intelligent features to enable:
- Intelligent cooking: Help cook the perfect meal to personal preferences, regardless of your expertise and abilities in the kitchen.
- Frictionless service: Build smart appliances that know when they need maintenance and make it simple to take action or schedule services.
- Integrated digital lifestyle: Make appliances useful at every step of the way by integrating them with digital lifestyle services – for example, automating appliance behaviors according to customer calendars, such as oven preheating or scheduling the dishwasher to run in the late evening.
“Intelligent Products Essentials enhances our smart appliances ecosystem, offering richer consumer habit insights. This enables us to develop and offer new features and experiences to integrate with their digital lifestyle.“ —Shawn Stover, Vice-president Smart Home Solutions at GE Appliances.https://www.youtube.com/embed/zaWAJN8aKOw?enablejsapi=1&
Serial 1, Powered by Harley-Davidson, is using Intelligent Product Essentials to manage and update its next generation eBicycles, and personalize its customers’ digital ownership experiences.
“At Serial 1, we are dedicated to creating the easiest and most intuitive way to experience the fun, freedom, and adventure of riding a pedal-assist electric bicycle. Connectivity is a key component of delivering that mission, and working together to integrate Intelligent Product Essentials into our eBicycles will ensure that our customers enjoy the best possible user experience.”— Jason Huntsman, President, Serial 1.
Magic Leap, an augmented reality pioneer with industry-leading hardware and software, is building field service solutions with Intelligent Products Essentials with the goal of connecting manufacturers, dealers, and customers to more proactive and intelligent service.
“We look forward to using Intelligent Products Essentials to enable us to rapidly integrate manufacturers’ product data with dealer service partners into our field service solution. We’re excited to partner with Google Cloud as we continue to push the boundaries of physical interaction with the digital world.” — Walter Delph, Chief Business Officer, Magic Leap
Intelligent Product Essentials is available today. To learn more, visit our website.
Google is a Leader in the 2019 Gartner Magic Quadrant for Data Management Solutions for Analytics
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As organizations continue to produce vast quantities of data, they increasingly need platforms that allow them to analyze, store, and extract meaningful insights from that data. And research from analyst firms like Gartner offer an important way for organizations to evaluate and compare cloud data warehouse providers.
Earlier this year, Gartner named Google a Leader in the 2019 Gartner Magic Quadrant for Data Management Solutions for Analytics (DMSA) (report available here). This evaluation covers Google Cloud’s core data analytics offerings, including BigQuery, a serverless, managed data warehouse, Cloud Dataproc, a managed Spark and Hadoop service, and Cloud Dataflow, which enables you to stream and batch-process your data. Here are a few takeaways:
Simplicity and speed
BigQuery’s performance permits complex queries on large-scale data sets to return in seconds, and a substantial number of BigQuery customers maintain data warehouses that store more than 50 terabytes (and a few customers now use more than 100 petabytes). More than half of these customers are loading data either continuously or many times per day. These customers value the ability to extract, transform, load, and analyze their data on a serverless platform, all without maintaining any underlying infrastructure.
A versatile serverless data warehouse
One of BigQuery’s major advantages is its ability to allow customers to address a wide variety of use cases—from a traditional data warehouse to data science. Over the past year, we’ve worked hard to introduce new features in BigQuery like data types for financial and monetary uses, BigQuery GIS for geospatial data, and machine learning capabilities through BigQuery ML. BigQuery’s continuous ingest capabilities make it suitable for an operational—or a real-time—serverless data warehouse.
An expanding ecosystem
With overall increased market adoption, our analytics offerings continue to benefit from a fast-growing partner ecosystem of service providers, and business intelligence (BI) and data integration vendors. In particular, in 2018 we expanded our partnerships with established industry providers, including Confluent, Dell Boomi, Informatica, Looker, Reltio, Tableau, and ThoughtSpot.
More and more organizations are finding value in Google Cloud’s serverless data warehouse and analytics offerings. If you’d like to learn more, you can download a complimentary copy of the Gartner Magic Quadrant for Data Management Solutions for Analytics on the Google site (requires an email address).
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
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Baking Gets Sweeter: Build ML Models that Help Predict the Best Recipe!
Baking recipes and ML models have one thing in common—they follow a pattern. Machine Learning is all about finding pattern in data sets, you can predict what you are baking based on the core ingredients and their respective amounts! Bread, cake or cookies, watch the video to make you make your baking experiences and learning with ML sweeter.
AutoML Tables, a no-code Google Cloud tool for ML models analyzes data from the databases and spreadsheets to help creates an automatic stats and dashboard with lists of ingredients and their values to predict a new recipe. Watch more episodes from Making with Machine Learning.
How One Company Uses AI and Data Analysis to Boost Revenue
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AI, deep learning, and image recognition is transforming the shopping experience. These technologies enable consumers to use product images or screenshots rather than text to search for similar products. This improves the customer experience and enables retailers with online and offline outlets to provide a genuine omnichannel experience.
The lack of complexity and the ease of use of BigQuery has enabled ViSenze to reduce its data infrastructure and management costs by 30%–50%, and scale up without incurring downtime.
—Renjie Yao, Data Platform Lead, ViSenze
Visual commerce provider ViSenze is helping some of the world’s leading retailers improve conversion rates through image-based search.
The business’s products also enable media companies to use the platform to turn images and videos into engagement opportunities—driving new and incremental revenues.
Created through NExT—a research center established by the National University of Singapore and Tsinghua University of China—ViSenze now operates in the United States, United Kingdom, India, China, and Singapore. The business is backed by Japan-based internet and ecommerce company Rakuten and cross-border investment specialist WI Harper Group.
Growth in SMB and Mobiles
Renjie Yao, Data Platform Lead at ViSenze, sees opportunities for growth in the small-to-medium business sector, where companies do not have the resources to build similar technologies, and with mobile device OEMs to integrate ViSenze natively on smartphones.
Phone owners can activate a “shopping lens” on camera and gallery apps to capture an image of a product. They then receive matching results from more than 800 partner merchants and retailers and can then click through to product pages on partner apps or mobile websites. Alternatively, they may use a photo to compare products sold on different sites or shop matching styles.
“Our research found Google Cloud provided a complete, integrated ecosystem rather than a disparate collection of tools and components, and so was ideal for our needs.”
—Renjie Yao, Data Platform Lead, ViSenze
The ViSenze API analyzes the contents of a selected or clicked image and sends the information back to the organization’s visual commerce platform. The platform feeds back similar results based on that information.
The ViSenze offering also extends to image analysis for the tagging of product attributes—such as a white turtleneck cardigan with full sleeves—to provide an improved search experience.
Data Vital to ViSenze
Capturing and analyzing large volumes of data is integral to ViSenze. “We have to understand how consumers interact with our customers’ ecommerce websites and apps,” says Yao. “For example, we need to know who has looked at a particular pair of jeans on a website and whether that visit led to a conversion. We can then tell that customer whether they need to make more stock available.”
ViSenze also relies on data to provide high-quality training for its image recognition models and its domain-specific models for online retail.
Protect Customer Data
Data is vital to ViSenze—but customer privacy is most important. “All the data we collect is transparent to our customers, meaning they can decide what they do not want us to collect. In addition, all personal data processing complies with privacy protection regulations in each region, such as the General Data Protection Regulation in Europe.”
A Quick Move to the Cloud
ViSenze started operations using servers, storage, networking, and associated systems in an on-premises data center operated by NExT.
However, to support rapid growth, the business decided to move its workloads to the cloud. ViSenze opted for a multi-cloud architecture, using in part a Google Cloud data infrastructure.
“Our research found Google Cloud provided a complete, integrated ecosystem rather than a disparate collection of tools and components, and so was ideal for our needs,” says Yao. “We could connect different components with the click of a mouse.” Further, the business found it could easily configure rules and pipelines to route data logs to relevant Google Cloud services.
The review found Google Cloud’s extensive managed services would also remove administration and maintenance tasks from ViSenze’s in-house technology team—freeing team members to focus on more valuable tasks.
In addition, Google Cloud provided the security features—including custom hardware running hardened operating systems and file systems and encryption of data at rest and in transit—needed to protect sensitive information. Finally, the location of Google Cloud regions in several countries would enable the business to meet regulatory and data sovereignty requirements.
A Three-Month Implementation
ViSenze opted to move to Google Cloud in mid-2017 and completed a three-month implementation using internal resources. “The process was very smooth and intuitive, and we had no problems building our entire data platform within Google Cloud,” says Yao.
The business now uses an architecture comprising Google Kubernetes Engine to manage and orchestrate Docker containers running in Google Cloud Platform; BigQuery to provide an analytics data warehouse, with Google Data Studio providing customizable visualization and reports; Stackdriver to monitor and manage virtual machine instances and services inside Google Cloud; Cloud SQL to manage its relational databases for real-time analytics; Compute Engine to provide compute resources; Cloud Storage to store files and objects; Cloud Pub/Sub to provide real-time messaging between applications; and Cloud Functions to build event-driven applications.
After collecting the request logs of users in virtual machine instances and Docker containers, ViSenze distributes them in three directions. “We export raw logs into Cloud Pub/Sub for indexing inside an Elasticsearch search engine, and to a BigQuery data warehouse for further analytics,” explains Yao. “We also use Cloud Functions-created applications to obtain the logs from Cloud Pub/Sub to perform some real-time calculations.”
“We are currently using Airflow workflow management on Compute Engine as our hosted ETL platform, but are likely to move to Cloud Composer in future.”
500 Million Records Per Day
With Google Cloud providing its data infrastructure, ViSenze is well positioned to meet internal and customer demands for more granular insights. The business is now processing 500 million records per day through BigQuery and saves up to one year’s aggregated data—excluding any personal data—in the data warehouse for analysis.
The nature of ViSenze’s business means most reports are generated for data processed on an hourly, daily, or monthly basis. “BigQuery is extremely stable and performance optimized, regardless of the volume of data it processes,” says Yao. “Across BigQuery and other Google Cloud Platform services, we’ve recorded 99.99% availability over the past year.”
The lack of complexity and the ease of use of BigQuery has enabled ViSenze to reduce its data infrastructure and management costs by 30%–50%, and scale up without incurring downtime.
“With BigQuery, we have saved the equivalent of two full-time engineers and now need only half of one person’s time to maintain our whole data platform,” says Yao.
“In addition, BigQuery integrates closely with Data Studio, enabling non-technical people in our product and business teams to create dynamic, detailed analysis dashboards. We now use Data Studio to create nearly 50 separate reports.”
The business has now grown to offer access to more than 1 billion users and a listing of more than 400 million purchasable products.
Next Steps
ViSenze is now researching the potential of the Cloud AutoML suite of machine learning products to improve the training of its models and run a fully managed NoSQL database through Cloud Datastore.
“A NoSQL database service is the only missing piece of our architecture for now, and using Cloud Datastore would enable us to focus almost exclusively on our business,” says Yao. “With Google Cloud Platform, we are ideally positioned to continue providing support to our business team and help them continue expanding into new markets.
“In addition, we can help retailers and consumers to unlock the potential of the web and apps to transform the purchasing experience.”
AI-powered Business Messages for Timely, Engaging and Helpful Conversations with Customers
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Over the last two years, we’ve seen a significant uptick in the number of people using messaging to connect with businesses. Whether it was checking hours of operation, verifying what was in stock, or scheduling a pick-up, the pandemic caused a significant shift in consumer behavior.
- 45% of users are spending more time on messaging services because of the pandemic
- 85% of consumers would like to message brands directly
With the rise in demand for messaging, consumers expect communication with businesses to be speedy, simple, and convenient. For businesses, keeping up with customer inquiries can be a labor-intensive process, and offering 24/7 support outside of store hours can be costly.
To help businesses seamlessly deliver helpful, timely, and engaging conversations with customers when and where they need help, we introduced AI-powered Business Messages.
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What is AI-powered Business Messages?
With AI-powered Business Messages, you can connect with your customers in their moment of need, in the places they’re looking for answers—such as Google Search, Google Maps, or any brand-owned channel. For instance, check out how Walmart customers in the US are able to receive real-time information on product availability, straight from a search results page.
People turn to Google when they are searching for answers to their questions, looking to buy something, or trying to accomplish a particular task with one of our many tools. In fact, 68% of all online experiences begin with a search engine.
At Google, we know how important it is for interactions with a brand to be personalized, helpful, and simple. With AI-powered Business Messages, customers are able to chat with virtual agents that understand, interact, and respond in natural ways.
We are also combining smart automation with the ability for customers to chat with live agents when it’s really needed. This approach saves your customers precious time, while also saving you money. And with Business Messages automatically handling many customer inquiries in the background, businesses have the option to distribute their human customer service agents to address other needs.
Getting started with conversational AI is easy with Bot-in-a-Box
We know it can be difficult to get started with AI. That’s why we are utilizing existing Google AI tools like Dialogflow—part of Google Cloud Contact Center AI—to create the capability within Google’s Business Messages called Bot-in-a-Box, which makes getting started with Conversational AI easy. Bot-in-a-Box allows for fast and effective adoption of automation for businesses of all sizes.
Enabling Business Messages with Bot-in-a-Box can be as simple as leveraging an existing customer FAQ document you already have, whether it’s from a web page or an internal document. And since the conversational AI is powered by Business Messages and Dialogflow working together, your chat bot is able to understand and respond to customer questions automatically without the need to write code.
Bot-in-a-Box also supports other critical journeys like “Custom Intents.” That means that your bot is able to understand the different ways customers express a similar question and respond accurately by using machine learning capabilities.
Finding success
In April 2021, Wake County courthouses in North Carolina partnered with Tango Technology to implement Business Messages when it became apparent that being able to provide the public and attorneys with around-the-clock access to information would significantly reduce the pressure on courthouse staff. Using Bot-in-a-Box, Tango Technology was able to customize a solution for Wake County Courthouse, Justice Center, and Clerk of Superior Court in just four days.
“With the combination of Google’s Business Messages, GCP, and Dialogflow, we were able to spin up an AI-driven bot for the courts in days. And the technology stack allows us to continually improve by adding functionality in an agile process.”— Mike Lotz, Co-Founder, Tango Technology
With Business Messages, North Carolina courthouses saw a 37% decrease in the call volume handled by courthouse staff. With 398,298 fewer phone calls during the first year of operation, the AI-based messages helped Wake County Courthouse work more efficiently and productively.
We’ve seen many brands benefiting from AI-powered Business Messages. For instance, Levi’s saw a 30% increase in off-hours shoppers and surpassed 85% customer satisfaction scores after implementing Business Messages. They also drove 30x more store-related questions than Levi’s website chat.
Bring Google’s conversational AI to your storefront with Business Messages
Google’s Business Messages makes it easier for businesses of all sizes to engage their existing or potential customers in a virtual conversation, when and where they need it.
To learn more, watch our Cloud Next session here or visit us at g.co/businessmessages. We have specialized services to help you get started and can share the wisdom of our channel partners and dedicated experts who specialize in unleashing the potential of conversational AI.
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Sure, machine learning is becoming a business imperative, but how does it work in practice—and what are the benefits for IT managers?
That’s the subject of a new step-by-step guide to solving business and IT problems with artificial intelligence and ML, based on insights gathered by IDG Research Services.
Its publication comes at a time when technology departments face growing pressure to embrace these emerging technologies, yet many have questions about how to get started.
It has real-life examples such as the health services company that used ML to reduce support ticket-resolution time from 48 minutes to six.
In another section, a financial services VP explains that cloud-based ML services enable his company to avoid spending money on computing resources that sit idle.
The guide also includes concrete tips for new ML adopters. For example, a real-estate CIO recommends the use of third-party tools that rely on AI and ML technologies, while a financial services VP highlights the challenge and potential of incorporating unstructured data into ML initiatives.
Download the guide now!
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