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.

Founded in 2014, Swiggy started small, delivering food to a few neighborhoods in Bengaluru, India. As the company grew, the team wanted a mapping technology that could help expand the service throughout India.

Swiggy needed a scalable mapping platform that covered a wide geographic area and offered tools to help the company to build an efficient mobile app and website for customers and delivery staff.

Customers find restaurants and order from them using the Android app, iOS app, or the website. Swiggy worked with Google Maps Partner Media Agility and used a variety of Google Maps Platform APIs to develop web and mobile apps that incorporate relevant local restaurant details.

Google Maps Platform Results

• Built a hyper-local delivery service that is growing throughout India at a rate of 25 percent per month
• Deliveries are made quickly, resulting in higher customer satisfaction and retention—users have been so satisfied that nearly 80 percent of its orders are from repeat customers
• Drivers seamlessly handle tens of thousands of orders per day

In order to guarantee fast food delivery, Swiggy returns only restaurants within four to five kilometers of the customer’s location. The Directions API is used by drivers to easily route to restaurants and customers. The customer can track the progress of the delivery and estimated arrival time using a mobile app or the website.

“Google Maps provides the most accurate and reliable data, which is crucial for us because maps and location are central to our business. We also knew Google’s intuitive interface would provide a great customer experience with little to no learning curve… Google Maps’ ability to provide customer location and the distances of nearby restaurants is the backbone of our success, because it ensures a reliable, consistent customer experience,” said Aman Jain, Senior Product Manager, Swiggy.

If you’re looking to migrate and modernize your Microsoft and Windows workloads, Google Cloud is your premiere destination. No matter what migration strategy you’ve selected or what value you’re looking to achieve, with Google Cloud you’re able to:

• simplify your migration and modernization journey
• reduce your on-prem footprint and increase agility
• optimize license usage to reduce costs
• modernize to reduce single-vendor dependencies
• rely on enterprise-class support backed by Microsoft

Whether you’re looking to migrate applications running on Windows virtual machines, adopt Windows containers in Google Kubernetes Engine (GKE), convert SQL databases to Cloud SQL, or something else, Google Cloud offers you the first-class experience you need. 

But we don’t want you to take our word for it. Try it out yourself with our new online Microsoft and Windows on Google Cloud Demo Center without any commitment or friction.

The demo center uses hands-on guided simulations to walk you through several scenarios for solving business critical challenges with Google Cloud’s Microsoft and Windows solutions. Because these are all simulated, you’ll see how it works without any deployment, configuration, or commitment. It’s a seamless way for you to see exactly how Google Cloud can help you.

Run dedicated hardware and optimize with sole tenant

Sometimes you might want to run your workloads on dedicated hardware (with oversubscription options) for compliance, licensing, and management. Google Cloud provides sole-tenant nodes that allow you to easily deploy your virtual machines onto dedicated machines to avoid “noisy neighbor” issues, address regulatory or licensing constraints, and optimize inter-VM communications.

Plus, the CPU Overcommit option allows oversubscribing sole-tenant node resources by up to 2x, therefore helping save on per-physical core licensing for many licensed workloads like SQL Server.

Learn how to set up a sole tenant group and node.

Optimize license costs with premium images & custom VMs

One of the easiest ways to optimize your cloud experience with virtual machines (VM) is to pick the right VM image. Google Cloud provides premium license-included VM images that are thoroughly tested and optimized, including SQL Server options with pay-as-you-go licensing. These are great for workloads that don’t need to run all the time or when you do not have spare licenses for bring your own license (BYOL). 

Explore some Windows & SQL images and learn how CPU/Memory options can help optimize deployment and save on licensing.

Modernize your databases with Managed SQL Server

Sometimes you need to manage your SQL Server instance to achieve certain business or operational goals. But more often, managing SQL Server deployments can be undifferentiated: backups, high-availability, updates, and patching are just some of the many things you have to take care of when going the do-it-yourself route. One way to modernize your database tier is to migrate to a managed service like Cloud SQL, which is a fully managed Relational Database service for SQL. 

Explore the process of creating an instance in just a few clicks!

Extract apps from VMs and move to containers in GKE with Migrate for Anthos

Many Windows workloads running on virtual machines such as Internet Information Services (IIS) are ideal candidates for migrating to containers without major changes like rewriting or rearchitecting. However, doing this migration manually can be tedious, which is why Migrate for Anthos can help easily re-platform a .NET app running on IIS into a container-based app. 

Simulate intelligently extracting, migrating, and modernizing applications to run natively on containers in GKE and Anthos clusters.

Move .NET applications to GKE on Windows without code changes

When you’re looking to go fully cloud native, you can leverage Windows containers in GKE without rewriting your .NET applications. Simply create clusters with Windows nodes and deploy containerized Windows workloads in a few clicks, even alongside Linux containers. These deployments reduce operational overhead with features such as auto-upgrade, auto-repair, and release channels. 

Learn  how easy it is to build a GKE cluster with a Windows node and deploy an app. 

Now that you’ve gotten a feel for what’s available, go check out the Demo Center. You can also visit us at Windows and Microsoft on Google Cloud to learn more.

We all experienced, first hand, how Covid-19 has impacted the world and our communities. Last year, store closures, reduced hours, and social-distancing requirements drove people to adopt more e-commerce options, which, according to eMarketer, grew 27.6% worldwide in 2020. Meanwhile, consumer desires to shop locally also grew with Google searches for “____ near me” up 100% year over year,¹ and 1 in 3 consumers having tried curbside pickup over the last year.²

As countries begin to lift Covid-19 restrictions, businesses are looking toward a post-pandemic future and asking ‘Which of these shopping trends will stick and how should I adapt?’ At Google, we expect consumers to continue demanding helpful shopping experiences that blur the line between the physical retail and digital experience, as well as help them to continue shopping locally.³ To address these changes in shopper behavior, we refreshed our retail solutions—adding a new solution, Product Locator, and updating Store Locator Plus—to help you offer the best possible online-to-offline experience and drive shoppers to your stores. 

Introducing Product Locator solution: connecting online shopping to nearby stores

Under Covid restrictions, consumers have grown accustomed to having a myriad of shopping options, from curbside pickup to same-day delivery. Increased searches for ‘along my route’ and ‘in stock’ tell us that convenience is king for today’s consumer.⁴ Retailers can increase online conversion rates and drive store visits by including product availability and pick-up options on each of their product description pages (PDP); emphasizing the speed and convenience of these options and saving on significant shipping costs. 

Our newest retail solution, Product Locator, can further help drive customers to visit by showing the exact distance stores are to the shopper and even estimated driving time, to further highlight the convenience of a store visit. A study conducted by Shopify found that showcasing local inventory boosted key store metrics including 45% of local pickup who made an additional purchase upon arrival.⁵ See the Product Locator solution guide to get started today.

Update your store locator page to address more of shoppers’ needs

Store locators allow shoppers to more easily find your stores and can be made even more helpful with our Store Locator Plus solution. Our updated Store Locator Plus solution refreshes the store locator page to be more informative and engaging by integrating offers redeemable in-store, online scheduling for appointments and services, and text me directions services. Implement Store Locator Plus today using the guide.

Build and customize Store Locator Plus for your business in minutes

At Google Maps Platform, we are always thinking of ways to make building a map easier and faster for our customers. In May at I/O we made Cloud-based Maps Styling generally available, enabling map updates to happen without touching a line of code. Today we’re excited to introduce Quick Builder, our free, low code builder which allows you to demo, customize and build a version of the Store Locator Plus solution for your website in minutes. Experience Quick Builder today.
To learn more about how you can start adopting these retail solutions, visit our retail solutions page.

For more information on Google Maps Platform, visit our website.

^{1 Global Insights Briefing: Getting back out there}

^{2 https://www.shopify.com/pos/future-of-retail-2021}

^{3 https://www.thinkwithgoogle.com/future-of-marketing/digital-transformation/covid-trends-1-year/}

^{4 https://www.thinkwithgoogle.com/consumer-insights/consumer-trends/pandemic-shopping-behavior/}

^{5 https://www.shopify.com/pos/future-of-retail-2021}

Vodafone India had a manual, labor-intensive process for determining network capabilities and reach. The company sent field operatives to every customer location to conduct a feasibility study. These feasibility studies help Vodafone determine whether they can provide connectivity and services to customers based on the infrastructure at that location.

Everyone from the IT team and end users to the field operatives doing the work recognized the need to adopt a new solution to automate the measurements. They needed a technology that was easy to use and maintain.

“Vodafone used to manually perform physical surveys for each feasibility, which is a time-consuming and labor-intensive process. Often, feasibility studies were delayed, and we missed out on opportunities to serve additional customers. With SmartFeasibility, we’ve increased our capacity 15 fold, which positively impacts our bottom line and allows us to provide better and smarter customer service.”

—Rajneesh Asthana, IT Planning and Delivery, Vodafone India

Partnering with Lepton Software (a leading global provider of location-based analytics solution) Vodafone introduced SmartFeasibility—a solution that changed the feasibility testing from a manual to an automated process. This involved a full Google geo platform solution – leveraging world class technology like maps, roads and directions.

Google Maps Platform Results

• Employees are able to access information faster with SmartFeasibility—they have data at their fingertips, rather than waiting for an employee to collect it
• Field operatives have increased their conversion rates by providing more accurate readings on feasibilities and closing more customer business
• Addresses are now easy to find with a click of a button. The Vodafone India team can search feasibilities that have been loaded into the database, so if there’s an issue or if they need to reference a past action, they have that information at their fingertips
• 2 day turnaround versus 5 before the solution was implemented
• 400+ new customers added per day

With the new solution, Vodafone India no longer needs field operatives to manually calculate these measurements. With Google Maps, users can search customers’ addresses, calculate the distance between Vodafone’s location and the customer’s location and research building data such as height.

The old system of having field operatives collect data was unreliable. With Google Maps Platform, the Vodafone India team knows that the measurements are accurate and reliable.