Time to go outside again, I guess. I’ll need a sun hat. Sunscreen. Maybe some new sandals? What else?

With the Recommendations AI service, I might be reminded to grab a reusable water bottle and a swimsuit. Or some after-sun aloe lotion. Good thing, cause I’ll need it.

Recommendations AI is a solution that uses machine learning to bring product recommendations to their shoppers across any catalog or client list. This service is part of our full suite of Retail solutions. When you integrate with the Retail API, you get the benefit of Google’s Product Discovery. Integrating once to reap the benefits over and over. Recommendations is the starting point, and you can easily extend into Retail Search and Vision Product Search in the upcoming future. 

The Recommendations solution is fully managed, global-scale and powered by deep learning, so you can focus on a great shopping experience and let someone else worry about the infrastructure.

Compared to baseline recommendation systems used by customers, Recommendations AI showed double digit uplift in conversion and clickthrough rates in A/B experiments controlled by the customers. You can optimize for click-through, conversion or session revenue, and fine tune the models to make sure you omit out-of-stock items or duplicates, for example.

So how does it work, and how do you get started? Read on, and we’ll walk you through the pipeline, starting with the data you already have to placement in your online store. 

Formula: Data -> Model -> Placement

You start with your catalog, the list of all the things (postcards, movies, pie recipes) that you want to show your customers. Then you ingest your PII-redacted user events -this is the historic event data like home page views, add to cart events and more along with real time user events. This user event is joined with the product catalog and items that allows us to construct the sequence of shoppers’ activity, thus being able to predict what the shopper has a high propensity to purchase next. The user events can come from both online activity across devices or offline store purchases

The recommendation model will return a list of products, which are the recommendations. The brains of the operation, if you will. This model is trained using all the data that you ingest, using the latest neural network models and techniques that Google has built expertise over the years in flagship products like Youtube and News, that allows us to uncover shopper intent,  so it can best predict the right recommendations to show to the right people.

Every model outputs a list of product identifiers, but where do they go? They go into placements, the spots, panels, carousels on your customer’s journey interacting with your brand that you’ve set aside to highlight recommendations. A model can send recommendations to one or more placements, but each placement only receives information from one recommendation model. Your pages will then need to render the products with the right images, text or other metadata, using the product ID that is returned by the model.

What do recommendations look like?

Let’s start by browsing our postcard-selling website, where I’ve been buying some vintage California postcards already. The recommendations algorithm has caught on to my interest, showing me other potential cards to purchase based on my history:

Put your data to work

To get started we need to bring your data into the recommendation model, so it can understand your customers, your inventory, and your sales patterns. 

The model takes in the product catalog you use, and metadata about those products to better understand nuances in assortment, pricing and variables like size and style. You might already have this data stored in BigQuery or Merchant Center, and hence we provide easy integrations that you can leverage to get started even faster.

As for the user events, don’t worry if you already have systems in place to capture web and mobile activity. We make it easy to bring in your real time event logs by providing seamless integrations with Google Tag Manager, Javascript pixel, or even historic events from Cloud Storage, BigQuery or using inline API or JSON, so you can immediately train the models on this imported  data. All this allows you to kickstart integrating with Recommendations AI in a matter of days.

The models then construct a sequence of activities that the user went through and joins with the products that the user engaged with. Once your data is ready to go, it takes a few days to train the model. Next onto making the data work for you.

Quickly customize your model

Setting up your own recommendations project in the console gives you the ability to choose what sort of model to train (based on what recommendations you want to generate) and your objective. Are you optimizing for click-through rate–more people click on the recommendation links or products–or for conversion rate–more people choose or buy what was suggested or revenue ?

Different models can be optimized for different optimization goals.; the GCP console explains what each one can do and how you can choose to optimize it.

Let’s unpack some of this terminology real quick.

We’ve got three model types:

• Recommended for you – Means we think these are items you’ll want to buy, based on your history; this is usually used on a home page to showcase items.
• Others you may like – Means if you’re browsing the page of a water bottle, we will recommend  alternative brands of water bottles that you may like as well as a backpack, based on your engagement  history.
• Frequently bought together – Means that when anyone buys sunscreen, we notice that they often also buy aloe lotion, so we will surface those items when someone adds any one of them to their cart.

And then we have three business objectives that the models optimize for:

• Click-through rate – How frequently did somebody click on a recommended item?
• Conversion rate– How frequently did somebody add a recommended item to their cart?
• Revenue per session – How much money did the recommendations generate for you?

Deliver anywhere along the journey

Now that you’re all set up in the Retail AI console, you can test out the recommendations in the console, even before you deploy to production.

You can integrate Recommendations into your frontend by calling the Predict APIt. The placements of recommendations will report data back into the dashboard and you can analyze and measure success for future iterations. 

On top of that you can use the recommendations for other parts of your customer’s journey. Email promotions, storefront kiosks, display ads or follow-up notifications can include recommendations based on past activity and cart contents. The model gives you useful product recommendations for a wide variety of touchpoints and steps in the purchasing process.

More best practices, and guides, are available inside our documentation.

How to get started

Training your own models can be tedious, time-consuming, and expensive. On top of that it requires deeper data science expertise to set up. Let us do it instead!

You can see how IKEA Retail uses Recommendations AI in this recent talk and blog from the Google Cloud Retail Summit..

To get started today you’ll need to make a Cloud project and enable the Retail API, which then allows you to access all the recommendation tools in one menu. Bring in your catalog and purchasing data, define a placement or two, and you can start putting recommendations on your site in a matter of days.

Editor’s note: Today’s post is by Eileen Murphy, Senior Director of Special Projects, and Andrew Nault, Director of Business Development, both from Iroquois Healthcare Association Workforce Investment Organization (IHA WIO). The health association represents 44 hospitals in 32 counties in New York State, providing advocacy, cost-saving initiatives, and business solutions. IHA WIO has provided its participating long-term care organizations with more than 5,000 Chromebooks to support 80 organizations in several ways, including training staff, connecting nursing home residents to their families, and helping administrative staff work safely from home.

Since early 2020, clinicians and staff at IHA WIO participant organizations have had to adapt to constant change—everything from implementing pandemic safety protocols to helping nursing-home residents stay in touch with families. We’re proud of the wonderful work they’ve done and continue to do.

During the pandemic, our use of Chrome OS and Chrome devices was expanded and changed to meet the needs of organizations dealing with an unprecedented event. Chrome devices were used to not only train staff but to enable remote work, as well as to connect residents to their friends and families during long periods of isolation. In the morning, a Chromebook could be in a managed guest session mode, helping a grandmother virtually attend her grandkids’ birthday party; at lunch, a nursing assistant  could log in to her latest continuing education classes on that same Chromebook. 

That evening, the Chrome OS devices could be found at check-in desks, helping visitors fill out a COVID-19 survey. All it took to change a device’s purpose were a few clicks in the Google Admin console, all of which could be done remotely.

Chrome OS for training

Most of our 5,000 Chrome OS devices came from a New York State grant to provide continued education training for long-term care organizations and its employees. We wanted to offer online classes, which meant we’d have to give people the technology to access that training. Our partner, CDW, recommended Chrome OS for us because it’s an operating system built for the cloud. In addition, devices running Chrome OS are easy to use, easily shared, and cost-effective.

Best of all, Chromebook Enterprise came with all the enterprise capabilities of Chrome OS unlocked. This allowed us to use the Google Admin console to manage our entire Chromebook Enterprise fleet remotely. We were able to set up one high-level organizational unit across IHA, with almost 80 organizational units for facilities and groups.

To support training, Lenovo helped us create a five-minute video that we added to our HealthStream learning platform so staff could learn the basics of using Chromebooks. This video was created because we didn’t realize there were people who have never touched a laptop before. The video and Chrome OS ease of use made it possible to get the folks who needed the most help up to speed quickly.

Chrome OS for connecting residents to loved ones 

Once the pandemic began and people could no longer visit nursing home residents, the staff for one of our facilities asked about using a training Chromebook for video calls between residents and families. We loved this idea as it was still in line with our mission: to enable the workforce to provide higher-quality care.

With the Admin console, the process was easy. We changed the managed guest session to auto load, and changed what is populated on startup. With help from healthcare staff, residents were able to choose from several video apps including Google Meet, Zoom, and Facebook.

Chrome OS for remote workers

When office and administrative staff had to work remotely, Chromebooks again came to the rescue. Since staff could access all their work applications via Citrix—like Outlook, office phone systems, and other public documents—we placed the remote-work laptops in managed guest session mode and loaded the Citrix app at startup. All the employees had to do was connect to Wi-Fi and log into Citrix, and they were ready to work.

With our training program still running and virtual remote ability on the rise, we realized we would need more Chromebooks. CDW informed us that Chrome OS device supply was running low, but they located 1,000 Chromebooks for us and sent them to our home office in Clifton Park, New York.

Normally, the job of configuring and shipping 1,000 laptops would strike fear into the hearts of the IT team. Not this time: With zero-touch enrollment, all of the Chromebooks were enrolled in our high-level organizational unit before the boxes arrived at our Clifton Park office. We created a list of Chromebook serial numbers for each IHA location getting a shipment, then packed up the boxes. We never had to open a Chromebook box, or even turn the devices on before preparing them for use by employees.

Chrome OS for telehealth and clinic kiosks

There were more Chromebook transformations to come. In the interest of keeping residents and medical professionals safe, we enabled Chromebooks for non-urgent telehealth sessions. Doctors and nurses could see and talk to nursing home residents, using the same video conferencing apps that residents used to talk to loved ones. In these cases, we had PointClickCare, our electronic medical records app, loaded at startup on doctors’ Chromebooks.

We even turned a few Chromebooks into guest kiosks at IHA member hospitals that have walk-in clinics. If the clinics required visitors to fill out a COVID-19 screening form, the Chromebook, in kiosk mode, was stationed at the front desk. Once visitors submitted the form, a new blank form appeared, ready for the next visitor without any data stored on the device. 

Chrome OS for whatever’s needed next

What’s amazing is that we could help people in all these different ways in just a few minutes—without visiting a nursing home, getting someone to ship laptops back to us, or loading software. Chromebooks are so versatile that sometimes they’re used in various modes several times a day. One user can log in, while another user can use managed guest sessions to login as a guest user. No matter what changes come to our healthcare association in the future, we’ll be ready.

It’s simply amazing to witness how some of our systems integrators employ Google Cloud solutions to drive innovation in ways we at Google may never have considered—especially in healthcare.  According to analyst firm MarketsandMarkets, the market for the Cloud in healthcare is projected to grow 43% between 2020 and 2025 to nearly $65 billion, fueled by the need for better technology infrastructures and faster digital transformation.  

Healthcare and life sciences communities are looking to Google Cloud and its Service engagement model partners to improve collaboration and activate the power of medical data.  These transformations deliver robust data analytics and bring a much deeper perspective into health epidemics like COVID-19 to help save lives.

In the healthcare and life sciences industry, our Google Cloud partners and customers provide constant energy and inspiration–and are the magic in some key healthcare innovations globally.  

Let me show you how they are solving real-world business challenges.

Improving collaboration in Healthcare

Cloudbakers and Comanche County Memorial Hospital transitioned 2,000 employees to Google Workspace to improve collaboration, reduce costs, and increase security. By implementing a system that requires less maintenance while enabling mobile access to data and true collaboration, Comanche County Memorial Hospital saves $175,000 annually on licenses and helps medical professionals spend more time with patients.

“We chose Google Workspace because it cost a quarter of what we were paying previously, offers the kind of modern features that healthcare facilities need, and gave us data security and peace of mind.” —James Wellman, CIO, Comanche County Memorial Hospital

Accessing previously locked down medical data

Google Cloud and Quantiphi supported advances in cloud-based machine learning services to reduce infrastructure costs, unlock new paths of treatment, and dramatically reduce the amount of time it takes to evaluate scanned imagery following a stroke.  John Hopkins University BIOS Division has been working on medical imaging to accelerate insights from scans on approximately 500 patients from 2,500 hours to 90 minutes, and lead to more accurate decision-making for brain injury patients that will ultimately improve medical outcomes.  

“We’ve aligned closely with the goal of showing that a cloud-based, AI-driven approach is robust and that it can be performed while protecting personal PHI. In terms of costs, the cloud has definitely reduced some of the traditional financial demands of our research.” —Daniel F. Hanley, Jr., M.D., Director, Johns Hopkins University BIOS Division

Improving data access with the flexibility of Google Cloud

With the help of MediaAgility, TRIARQ migrated to Google Cloud to modernize their platform, build new applications, and expand their global footprint.  With BigQuery, TRIARQ can now consolidate all transactional data, past and present, into a single location to report on specific insights and predict future data from e-prescriptions to complex revenue-cycle data and value-base analysis.

“The future of this industry will need to be a global one, and we can no longer be stuck in legacy systems if we want to survive. Having a team that is passionate about supporting us in this journey is definitely a plus point.”—Yaw Kwakye, Co-founder and Chief Architect, TRIARQ

Increasing visibility to COVID-19 outbreaks for actionable insights

As part of its response to the COVID-19 pandemic, HCA Healthcare chose to work with Google Cloud and SADA to create a national portal that increases visibility into outbreaks in 3,100 counties across the country in just 8 weeks.  Now, the portal generates 30,000 new analytical views each day that can help inform private and public sector decision making for reopenings, closures, hot spots, and many other population health management activities. 

“This project required deep knowledge of AI, and consumer-facing platforms, as well as healthcare. Google brought together the ideal combination of product, people, and partners. Google Cloud’s healthcare-specific products along with SADA’s expertise in the healthcare IT space made this partnership the perfect choice to move quickly and intelligently.”—Dr. Edmund Jackson, Chief Data Officer, HCA Healthcare

We’re committed to building the technology, resources, and services through Partner Advantage to help our partners address this opportunity.  Looking for a solution focused partner in your region who has achieved Expertise and/or Specialization in your industry?  Search our Global Partner Directory.  Not yet a Google Cloud partner? Visit Partner Advantage and learn how to become one today!

In the past six months, Mynd Property Management has doubled in size—in part by hiring, but also through acquisitions that help build our business. It’s an exciting time to be at Mynd—every week brings new people who need to be set up with email and network access, which can also be a bit hectic. To speed onboarding, we use G Suite Enterprise to get our new people working as soon as they walk through the door.

Streamlined onboarding
We started using G Suite when we launched the business because it fit our overall approach to technology—that is, deploying cloud tools that are flexible for our employees and easy for IT to manage. Good thing we picked G Suite, because given the pace of our acquisitions—13 at last count—we have our hands full with combining business systems and keeping people productive. Onboarding is literally 80 percent of our daily work! 

Some people on our team don’t have technical backgrounds, so we are careful to only introduce new software tools once people are prepared to use them. With G Suite, people need very little ramp-up time because it is an intuitive, integrated solution. In one of our in-person training sessions, we showed an accountant how to open up a Microsoft Excel file in Google Sheets, and he started working on it right away, without much help.

We’re a remote-first company, so working in the cloud is second nature to us. About 75 percent of our total workforce are remote full-timers. G Suite tools are optimized for mobile, so they’re especially useful for the 50 full-time property managers who work solely on mobile devices in the field. 

Better meetings, easier file storage
In Mynd’s early days, we used G Suite Basic, but upgraded to G Suite Enterprise after Google Cloud Premier Partner Suitebriar showed us the benefits of using tools like Hangouts Meet and shared drives in Google Drive. They’ve really helped us scale the business, so we can stay productive even in the midst of acquisitions—and of course, onboard people more quickly. 

Many G Suite tools have become things we can’t live without. Our executives got first crack at using Hangouts Meet Hardware, and loved it so much—especially the recording feature—that now all of our conference rooms have it. 

Google Drive got the same love from everyone. It’s all we need for file sharing and storage, which means we don’t have to pay for another storage solution. Drive also helps with onboarding; as soon we assign an employee a Google Account, she or he automatically gets access to the shared drives (in Drive) that they need to do their jobs. 

Customizing the tech stack
As much as we love G Suite, we still like to use some non-G Suite tools. It helps that we can tailor G Suite to fit in our technology stack any way we want. We’d never get that control and customization from other vendors in the productivity space.

For example, we love using Slack for internal chat and messaging, so we turned off Hangouts Chat. We like that we can control these channels, and that G Suite is not an all-or-nothing product. That means we’re not afraid to try out new G Suite features, since we can roll them back if they’re not right for us. 

The result of our day-to-day G Suite use is that we don’t suffer much down time even during acquisitions. We can pull relevant data from users’ old systems and easily automate user creation from start to finish. When we merge with companies that already use G Suite, we can move their data over to the business in less than a week.

When we recently moved over 80 new employees and 50 different web domains to G Suite, it was much less challenging than we expected. Since we don’t plan to slow down on acquisitions, we breathe easier knowing that G Suite keeps everyone working in the middle of so much change.