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.

We launched the Chrome Insider blog in 2019 to share tips and best practices with IT admins so they could better deploy, manage, and secure Chrome browser. Since then, the series has covered everything from supporting legacy browsers to protecting enterprise credentials to simplifying deployments. To make it even easier to find and browse the series, we’ve pulled together all the posts to date here so you can read them all in one place or bookmark them for later.

Managing Chrome tips and tricks

• Understanding Policy Precedence for Chrome browser
• Protecting enterprise credentials with Password Alert
• Managing Chrome browser in a controlled environment
• Simplifying Chrome deployments on macOS
• Configuring Chrome browser in your VDI environment
• Know your options for Modern Enterprise Browser Management
• Managing policies and extensions across operating systems in Chrome Browser

Improving extension management and reporting 

• Improving Chrome browser extension management through permissions
• Publishing custom extensions for the enterprise
• Enhanced extension reporting with Chrome Browser’s Takeout API

Ensuring your organization is secure 

• Managing BeyondCorp Enterprise’s threat and data protection capabilities in Chrome
• A primer on enterprise private browsing modes

Increasing your organization’s productivity

• Ready, set, Chrome—Improvements and polices to make Chrome more performant
• Troubleshooting with Chrome Browser
• Preparing your enterprise for Flash deprecation
• Apps from the past? Fear not with legacy browser support in Chrome Enterprise

Although the series is 16 posts strong, there are plenty more Chrome Insider posts to come. You can read these posts and more on our Chrome Enterprise channel.

Established companies in any industry, including telecommunications, often take a cautious approach when it comes to adopting new technology. This is true of any technology and especially of tools used across entire organizations and at all levels—like collaboration and communication tools. I joined Optiva Inc. as its Chief Marketing Officer in August 2018, and right off the bat I faced the need for cloud-based productivity and collaboration tools like Google Workspace (formerly G Suite), which was new to me. 

Optiva was in the midst of making an organization-wide change to implement Google Workspace productivity and collaboration tools. Employees at that time—at Optiva and across other organizations I used to work with—primarily collaborated by attaching documents to company emails and meeting their colleagues in office corridors. Meetings were almost always in person or via phone. These processes were inefficient and contributed to version-control confusion and decreased overall productivity. They were also based on our pre-COVID world.

Today, Optiva is a fully remote-first organization, and our employees use all the tools in Google Workspace. Collaboration has increased dramatically, surprising even initial skeptics. Occasionally we still encounter resistance from new employees who are used to a different way of working. This is logical, but more often than not, they thank us later, and many have told me that they see the value for their productivity and the entire organization’s collaboration. 

Google Workspace has become a real advantage, especially for a global company like Optiva with employees across the world and in so many different time zones. Instead of waiting for team members’ availability to review or collaborate on a document, wasting time coordinating who works on the latest version, or who is the “owner” of a deck, employees can open a document, view new changes, and add updates, even when others are working on the same document in parallel.  

Google Workspace promotes business as usual during COVID-19

The combination of Optiva’s remote-first policy and Google Workspace collaboration tools proved a welcome addition during the challenges posed by COVID-19. Optiva was ahead of the curve of remote working policies in the telecommunications industry and in the software industry as a whole, so when the work-at-home orders went into effect in March last year, Optiva’s business charged ahead without missing a beat.

Google Meet has helped us maintain a strong corporate culture during this challenging time. For example, we constantly use video conferencing, which has been a great way to see the entire team and get a real sense for how each employee is doing—professionally as well as personally. Using Google Chat allowed us to keep in touch with everyone across our global teams. It helps us ensure that everyone feels secure and can work during this challenging time. Also, we have continued to create new teams with employees all around the world. If we attempted to onboard or train them using conference calls or emails, the experience wouldn’t be the same. Meet has helped us develop the strongest teams possible, faster, and much more effectively. 

We also use Google Workspace tools to cross-train employees on tasks and processes in departments outside their own. That way, if one of our employees has to be out of the office unexpectedly, the company benefits from full business continuity. To make this possible, various teams and departments documented their specific processes, held training sessions on Meet, and recorded them so any employee could access them on demand.

Many struggled in the early days of the self-quarantine orders, especially when it came to the sudden need to select and deploy new technologies for remote collaboration and, more importantly, to keep supporting their customers. We were fortunate that our business was unphased—and we owe a lot of this success to Google Workspace.