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.

Vinayak Bhavnani, Co-Founder and CTO of India-based bus transport technology company Chalo, shares how Google Maps Platform is used to improve visibility for commuters and bus operators across India by visualizing geospatial data.

Effective public transport networks contribute to the local economy and help make cities safe, pleasant, and sustainable. In India, buses make up around 90% of the public transport offering, but when you talk to the people who ride them every day, you find that there’s a lot of room for improvement. Heavy traffic means there are rarely any fixed schedules and it’s impossible to know exactly when your bus is coming. We’ve found that people tend to wait at a bus stop for up to 30 minutes a day, which creates a lot of frustration and wasted time.

When we founded Chalo, our aim was to make the daily city commute a more positive experience. Reliability is synonymous with visibility: when you know exactly when the bus is coming, you can plan your day better. If you’re in your office, for example, and see the next bus is in 10 minutes, you can be at the stop at the exact time it arrives, instead of waiting around. To enable this, we base our solutions on richly-detailed geospatial data provided by Google Maps Platform.

Eliminating wait times and increasing revenue with geospatial data

In India, bus passengers tend to have fewer resources. The Chalo App, which can be downloaded for free, allows them to see exactly where their bus is on its route and when it will arrive at their nearest stop. They also tend to be late adopters of mobile technology, meaning we had to create an interface that was user-friendly, reassuring and adapted to all age groups and backgrounds. One of the main reasons we opted for Google Maps Platform is that it’s very present in India and other emerging markets and is familiar to our users, which inspires trust. At the same time, we like the fact that Google Maps Platform provides rich geospatial data while being simple to implement and work with. We use the Geocoding API, Reverse Geocoding, and the Directions API to enable location search and provide directions.

We also worked with MediaAgility to identify the Google Maps Platform products most suited to our needs and the best practices to be followed. This helped to ensure that our business objectives could be met efficiently.

The Chalo App also enables digital ticketing, alongside the Chalo Card, a payment card for those who don’t own a smartphone. India, like the rest of the world, is gradually moving away from cash payments, but the public transport system is proving slow to catch up, meaning people still must have cash in hand when they board the bus. Digital ticketing not only makes commuting more convenient, it also helps protect passengers, drivers and conductors during the COVID-19 health crisis by limiting physical contact. 

Chalo also offers solutions aimed at bus operators that help them improve their services and their bottom line. In major Indian cities, buses are run by a combination of public and private agencies and small private individual bus operators, with the majority of the latter only operating one or two buses. The market is very fragmented and there’s not much incentive for bus operators to invest in infrastructure or customer experience – especially when they have little to no visibility on where their fleet is at a given time, how many kilometers it travels in a day, or how much money it takes. 

The Chalo dashboard provides operators with a map-based real-time overview of bus locations, alongside scheduling features and route, ticketing, and passenger statistics. Geospatial intelligence and insight into passenger demand enable operators to explore new avenues of revenue and adapt routes and services to passenger needs. Operators who’ve partnered with Chalo report an average improvement of 10% to 30% of their bus fleet operations.

Chalo is currently powering about 100 million rides a month on 15,000 buses in 37 cities. We’d like to see the number of rides increase tenfold over the next few years. To do that, we’re looking to broaden our offer and expand to other parts of the country and across international borders. A pilot is underway in Bangkok, and we’re considering expansion into South-East Asia, Africa, and the Middle East. Having access to detailed geospatial data anywhere in the world via Google Maps Platform, without having to make any major investments or changes to our technology stack, will make this considerably easier. 

At the same time, we’re exploring artificial intelligence and machine learning to improve the accuracy of our scheduling features and we’re introducing video-based solutions for people-counting on buses. Our aim is to continually improve our offering and optimize our services. We’re looking forward to working closely with Google to make that happen.  

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

In a perfect cloud world, you would host all your applications in containers running on Kubernetes and Istio, benefitting from the portability and improved resource utilization of containers, plus a robust orchestration platform with advanced application management, networking, and security functionality. This is easy to do if you’re developing a new application, but it can be hard for existing applications to take advantage of those capabilities.

Many of the applications that you may want to move to the cloud have been around a long time, and you may not have the application-specific knowledge that would be required to rewrite them to be more cloud-native—or it would be incredibly time-consuming to do so.

Another option is to lift-and-shift to a virtual machine (VM) hosting platform like Compute Engine, but that means you still need to maintain the VMs. Even if you’re not able to fully modernize an existing app, it would still be great to get some of the benefits of containers and Kubernetes.

What is Migrate for Anthos?

Enter Migrate for Anthos, a fast and easy way to modernize your existing applications with a service that encapsulates them in a container. Moving your physical servers or existing VMs into Kubernetes containers gives you crucial portability and resource utilization benefits without having to rewrite the underlying application.

Since Migrate for Anthos is built for Google Kubernetes Engine (GKE), you also automatically capture the scaling and flexibility benefits of a managed Kubernetes environment in the cloud. Migrate for Anthos recently became generally available.

Converting an application with Migrate for Anthos happens in two phases. First, it creates a generic container wrapper around your application that makes it seem like it is still running in a full VM environment. Then, you launch Migrate for Anthos software on your Kubernetes cluster that runs the containerized application.

You can find more details about this in the documentation and in our blog post: Migrating from Compute Engine to Kubernetes Engine with Migrate for Anthos.

As the name suggests, Migrate for Anthos works with Anthos GKE. However, you can also use Migrate for Anthos with only GKE—all it requires is your application and a GKE cluster running the Migrate for Anthos software. 

Getting started with Migrate for Anthos

Migrate for Anthos works with a variety of workloads, but not all. It’s particularly adept at migrating legacy applications, stand-alone applications, and monolithic applications. As you start the modernization process, here are some questions to ask to determine whether to use Migrate for Anthos with your applications:

1. Should this app be in the cloud?
By its nature, the cloud may not be able to support some characteristics of your on-prem environment, such as geography and legal compliance. The best way to find out whether the cloud will work for each of your applications is to plan out a full migration. That will allow you to identify groups of applications that can benefit from cloud offerings such as a global network and ease of resizing resources. After that, try out a proof of concept by testing the apps in the cloud to see if it fits your business needs.

2. Should this app be in Kubernetes?
Containerizing an application simplifies workload administration, improves scalability (both up and down), and increases host utilization. Kubernetes orchestrates the containers and GKE handles node upgrades, while add-ons like Istio let you manage network and security policies independently of your application.

With those advantages, it’s easy to think that containers are always the right way to go, but there are some cases where it may make sense to stick with VMs. Strict hardware requirements, specialized kernel modules, and license constraints may be harder to run with containers, negating their advantages.

3. Should this app migration use Migrate for Anthos?
Migrating your apps or workloads to the cloud isn’t just about shifting where the compute resources run; it’s also an opportunity to modernize them with containers. Using Migrate for Anthos (or Migrate for Compute Engine) gives you the ability to get your workloads in the cloud quickly, with minimal upfront downtime that’s easy to plan for. 

However, even if you use the Migrate for Anthos wrapper, your application is still the same application. The benefits of the modern platform may not outweigh a legacy application and a rewrite may be the only way to meet your business needs. There are also some specific services from your VM that may not work with Migrate for Anthos, for example licensing requirements.

Migrate for Anthos can also be the first step in a larger migration effort. Once you’ve migrated the application to GKE, you can gradually break up a monolithic app into microservices by manually rewriting parts. Spreading out the migration effort gets you in the cloud sooner, giving you more time to modernize.

Next steps

A successful modernization starts with creating a full migration plan, testing the workloads, and monitoring them. You can experience the benefits of modernization with Migrate for Anthos by picking a small workload and trying it out for yourself!

Financing digital transformation poses a significant problem for many enterprises we work with. It’s not because the C-suite doesn’t support it: the need to become more agile, digital, and data-driven is nearly ubiquitous in executive public pronouncements. But most large organizations look at IT investment in a way that’s ineffective for a successful digital transformation effort.  

A varied combination of  factors affect an organization’s success in the digital ecosystem, and a lack of alignment between IT and business leaders can hamper even the most digitally sophisticated organizations. Empirical research by the Apigee Institute shows clear evidence that a solid digital transformation effort, aligned across business and marketing, whose progress is carefully tied to core enterprise metrics, must be supported by an investment strategy that uses a “real options” portfolio approach.

Let’s explore ways to think about funding digital transformation.

Planning for the Unimaginable

Common IT investment strategies are focused on straight-line net present value (NPV) business case calculations, which emphasize linear solutions and impacts. These methods ignore the value of enabling a range of options and potential future use cases, which an API program, with its reusable, highly consumable digital assets, makes available for the enterprise. In addition, these methods often ignore the value of the new categories of business data generated by such programs, as well as business capabilities that were impossible to imagine before the capabilities came online.

One media company we’re working with was astounded by a project that now makes key sales provisioning available to large distribution partners within two months, when previous estimates predicted two years and many millions of dollars, each.

More importantly, that functionality will now be reused across all of their numerous current partners and sales channels, and is enabling exciting new partnerships that were too costly before. This functionality was an unexpected by-product of their original API effort—it wasn’t even the main focus of the initial project.  

There was no way that the impact of this capability or its implications on new business could have been predicted at the outset, and no way to formally model the outsized impact this functionality will have on the enterprise. Any attempts to do so would have been deemed ludicrous.

Breaking from the Status Quo

So how do you fund an API-driven digital transformation, which by nature emphasizes speed and optionality to unlock new ways of doing business, instead of the business-as-usual single-use, point-to-point integrations that perpetuate status quo thinking? For a world where the latter is the dominant paradigm and you don’t have a clear C-level mandate, here are three suggestions.

1.  Enable your web/e-commerce teams. Many such teams find their responsibilities expanded to include mobile, kiosk, and many other digital initiatives, while being trapped in the traditional web server, do-it-yourself paradigm. These teams are actually quite open to technology, and often harbour weekend hackers who love to explore the new capabilities that are expanding the digital ecosystem at light speed. Tapping this passion by funding new approaches and quickly realizing immediate projects can create first the technical, and then the business proof points that resonate with a strategy team. These capabilities must be nurtured to move out of the lab into production, where real results are created.  

2. Focus on partnerships. Opening and accelerating  your relationships with partners via APIs (read more about partner API initiatives here) is high-impact and relatively easy to model. This business area can prove out an API platform, and in our experience creates clear business value that can support future justifications for other business units. What’s best about these wins is that sales operations groups are typically very tactical and demanding, so when they see the value, these groups will send a message to the rest of the organization that APIs have business value. Further, your enterprise’s core partnerships have the focus of executive attention. Adding value here will elevate the stature of the API program in the C-suite.

3. Work on your CFO/financial organization. Using your successes in e-commerce and partnerships, you can propose a more robust investment using a real options approach that leads to treatment of APIs as products. This path is predicated on what research has proven to work: the adoption of a digital KPI target, coupled with a portfolio of programs whose net result is to achieve the target KPI.  

Building Toward the Big Vision

But what about the visions of ground-breaking business models, ubiquitous connectivity, and enablement of an ever-shifting set of opportunities on big platforms and with unlimited partnership possibilities? Yes, these are still the endpoints to work toward, and fundamentally comprise the strategic reasons to embark on a digital transformation. If your enterprise has the C-Level leadership (in the form of a CDO or CDO-like figure), supported by a well-funded, sophisticated investment approach, by all means, build that new world now.

However, should that vision be shared only within your business unit, a forward-thinking IT group, or a dedicated innovation team, the most important thing to do is to build your team’s capability—and your program’s credibility—while delivering value along the way. The approach we suggest here has been proven in many of the organizations we work with; once your organization begins working faster and at lower costs by orders of magnitude, you’ll have created enough leverage to change the way your organization invests in its API program.