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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.

By offering customers a choice of 30,000 shoe styles at reasonable prices with a straightforward returns policy, Footway has become one of the largest online shoe retailers in Europe. Founded in 2011 and based in Kista, Sweden, the company has grown to become a market leader and four years ago acquired the Heppo and Brandos brands.

In 2017, Footway expanded from the Nordic market to seven other European countries: Poland, Holland, the UK, France, Germany, Austria, and Switzerland.

To build the business, Footway has had to prove to its customers that buying shoes online is quick and easy. That means, at the backend, processing returns and queries efficiently to provide a seamless consumer experience. However, as the company’s IT infrastructure grew organically, its data storage procedures were not clearly systematized, which made it difficult to implement the efficient, time-saving processes that enable Footway to provide its excellent customer service.

The company needed to move its team onto a system that would resolve this issue, and make compliance to ever-changing data regulations easier. Implementing G Suite was the answer.

“One of our strategic objectives is to automate processes and increase efficiency in our organization,” says Louise Liljedahl, HR Manager and Co-founder at Footway. “We want to work more efficiently every day. One of our main goals with the G Suite project was to get everyone working in the same platform with good structure.”

Systematizing data storage

In order for a relatively small team to run a big retail operation, good organization and process automation are key. Before working with Google Cloud, Footway’s communications and data storage processes were not systematized, making it difficult to work effectively. “Our internal IT had many different places to save files, and, for example, no structure for where to save pictures or graphic elements or where to archive agreements,” explains Louise. “Another trigger for change was the GDPR. To be compliant, we needed to see who has access to what information, and looked for good audit systems to control those permissions”. To do that, Footway implemented G Suite.

With support from implementation partner Avalon Solutions, the migration of the company’s calendar, contacts, and email data using CloudMigrator was swift and seamless. “The transformation process was very lean and quick,” says Louise. “We had a small test group of six people working with G Suite for two weeks to see how to move over to Google Sheets from Excel, for example. Everyone was very enthusiastic.”

To systematize its storage and make day-to-day working easier, Footway moved its document and images to Google Drive. This means Footway can control access both internally and externally. “Google Drive helps us keep everything in the right place. And it is definitely an easier way to share things when it comes to working with external partners, as we know exactly who we’re sharing documents and information with.”

Increasing automation

Following the move to G Suite, Footway automated communications with influencers and bloggers using Gmail and Google Forms. This saves time and means more bloggers can be contacted.

“We send automatic emails through G Suite to different influencers to set up cooperative agreements and partnerships,” says Louise. “Using Gmail and Forms has helped us to go from having contact with 4 bloggers a day, to 200 a day. The person responsible saves two hours a day from his or her workload with this new process.”

Achieving stable growth

Thanks to G Suite, Footway’s data is now organized in a way that is easily accessible and highly secure. “It’s much easier to find company and client information because it’s structured,” says Louise. “That saves a lot of time when searching for something.” The ways people can collaborate have also been transformed, as Footway made the switch to using Google Chromebook, encouraging more flexible working and spontaneous collaboration. “Before, people would take notes on paper and come back to their computer to write everything down. Now they use their Chromebooks instead and have more small, spontaneous meetings.”

Now, Footway plans to extend the use of Chromebooks and to use the functions G Suite offers to further automate the company’s daily processes and grow the business. “People tend to be fearful of change, but this one has been very positive for the whole organization,” says Louise. “We’re excited about how G Suite can help us as a company to grow in a stable way. We can automate processes and use Google products to work more efficiently.”

Security and the way we’ve been able to advance user protections over the years is hands down one of my favorite topics when it comes to Chrome. I’ve been on the Chrome team for over 14 years and have seen the browser evolve to become a critical tool that helps people get work done at home or in the office. At the same time, Chrome is often the best line of defense to protect users against malicious software on the internet.

Now more than ever, security has to be top of mind for IT and security teams. Cybercrime is up 600% due to the COVID-19 pandemic, and remote work has increased the average cost of a data breach by $137,000. Those numbers are staggering. And IT teams need to make sure every piece of their tech stack helps support their security needs.

Chrome has worked for years to quickly mitigate risks, block malicious sites and content, and proactively improve web security to keep your users and corporate data safe, pioneering new layered defenses like sandboxing and site isolation. When we think of security and Chrome, we believe it takes multiple elements of security coming together to give organizations the best protection possible.

Security you don’t have to think about: Chrome has many built in protections in place by default that benefit all users. This includes our team’s work to minimize zero days and roll out fixes quickly. With auto updates enabled, Chrome offers enterprises fast and automatic fixes for zero day vulnerabilities. The Chrome security team recently published a post explaining the state of in-wild-bugs that’s worth a read. More examples of the defense-in-depth Chrome offers include capabilities like site isolation and sandboxing that keep malicious code from affecting other visited sites or impacting user machines. Our scanning infrastructure helps us detect and remove harmful extensions from the Chrome Web Store (in fact, last year we saw an almost 90% drop in malware) to prevent your end users from getting infected with malicious extensions. These are protections built directly into Chrome that automatically keep your users and organization safe.

Protections for Chrome users: We provide additional layers of protection that keep your end users safe while they’re on the web and give them helpful information whenever there are potential security risks. For example, Safe Browsing, offered natively in Chrome, helps protect devices by showing warnings to users when they attempt to navigate to dangerous sites or download malicious files. We also give users insights into the safety of their passwords, letting them know if their passwords have been compromised, or even prompting them to change their corporate password if they try to re-use it against company policies. You users are working hard to get things done, and Chrome’s layered security supports their workflows while helping them stay safe on the web.

Enterprise Controls: Every organization has unique security needs. That’s why Chrome provides enterprises with advanced safeguards and granular policy management to help meet those security goals. With hundreds of policy options, admins can use a variety of management tools, including Chrome Browser Cloud Management, to customize the browser. Chrome Browser Cloud Management goes even further, providing powerful enterprise extension management, a security priority for many organizations. Our extension request workflow allows end users to request extensions and admins to allow or deny extension requests. We also recently released extension pinning that allows admins to pin to specific versions of extensions to support any internal security review processes.

Visibility and reporting is another focus area to support IT teams. Through Chrome Browser Cloud Management, administrators can get more insight into their browser environment. This data can help IT teams make security decisions, better understand the devices running Chrome and investigate if issues arise.

Zero trust security: Finally, Chrome can help modernize an enterprise’s security strategy by migrating to Zero Trust access models. BeyondCorp Enterprise helps secure endpoints by directly integrating Threat and Data Protection in Chrome. This protects against intentional or accidental data loss and prevents malware and phishing in real-time. Additionally, Chrome and BeyondCorp Enterprise provide device trust and endpoint security signals directly from the browser, significantly easing deployment of zero-trust in your environment. In 2022, we plan to partner with leading players in the cybersecurity and zero trust space to add additional integrations.

For security recommendations, check out the new Chrome 2.1 CIS Benchmark that covers independent recommendations on which Chrome policies to configure to help support organizations’ security and compliance needs.