During uncertain times, ecosystem partnerships that leverage APIs have proven to help companies scale and address gaps in their businesses. Apigee customers, like CHAMP Cargosystems, have pursued API-first ecosystem models to enter adjacent markets, create new customer interaction models, and rapidly grow their brand reach and partner ecosystems. As a result of building their API ecosystem, they are transacting 300 million electronic exchanges and 20 million shipments per year.

CHAMP selected Apigee to provide an API platform and developer self-service portal option to all of its SaaS customers. Google Cloud’s Apigee API management platform and portal allows CHAMP and its customers to quickly connect to a variety of backend systems, including in-house, third party apps, marketplace portals and more– thereby accelerating their digitization strategies and opening up new markets through an API ecosystem.

Join this webcast and hear from this leading Enterprise company on how to:

• Identify new revenue sources and markets using an API management platform
• Improve time to market while still complying with all industry requirements
• How to create a proof of concept to grow API adoption throughout your organization
• Align internal business leaders and partners to see the importance of an API-first platform vision

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.

It has been seven years since Marc Andreesen’s famous article “Why Software is Eating the World” was published in the Wall Street Journal, and given the slow pace of change in many companies, some executives still may not be taking the threat of being “eaten” seriously enough. 

Software, and platform business models based on software, have the potential to deliver powerful economic forces into virtually any company or industry. Every company has valuable assets—such as data, expertise or access to certain services or user bases—and most of these assets can be delivered via software. Once an asset is expressed as software in a modern way—that is, as an application programming interface (API)—it can be combined with other software to create new applications and digital experiences.

Benefits of this approach, just to name a few, include near-zero marginal cost to scale up APIs for new users or use cases; global reach for both partners using APIs and end users consuming the digital experiences those APIs power; and network effects triggered as more partners use a given company’s digital assets and spread its services into new markets and use cases.

Disruption by software-powered business models

In the last two decades we’ve seen individual companies and entire industries upended by these kinds of software-powered business models. Examples abound: Amazon and the retail industry, Netflix and movie rentals, Uber and ride hailing, Airbnb and hotels, etc. We’ve reached the point that these companies’ names have become verbs synonymous with being “eaten” by software (e.g., “Amazoned” or “Netflixed”).

The most famous examples of digital disruption involve digital natives, of course, but legacy businesses are leveraging software to evolve too. Brazilian retailer Magazine Luiza—a company I’ve worked with through my employer, Google Cloud’s Apigee team—has enjoyed enormous revenue growth and seen its stock soar, for example, as it has built out its digital platform capabilities and transitioned from a primarily brick-and-mortar model to an omnichannel one. The point is, whether a company has been in business five decades, five years or five months, software remains ravenous and is always looking for new companies and industries to “eat.”

Change in the face of serious threats

Facing this threat, how should corporate leadership respond? There are some excellent examples of CEOs who have galvanized their companies and led them through the massive, gut-wrenching change required to pivot in the face of a serious threat. A few of the biggest examples include: 

• In 1995, it became apparent to Microsoft co-founder and then-CEO Bill Gates that the internet was “the most important single development to come along since the IBM PC,” and, if not embraced in haste, a threat to many of Microsoft’s businesses. In May of that year he published the “The Internet Tidal Wave” memo and focused all of Microsoft on adopting and building for the internet. Almost 20 years later, current Microsoft CEO Satya Nadella similarly made the bold decision to redirect the company for a cloud-first world. 

• Facebook went public at $38 per share in May of 2012 but within months, stocks could be had for a little over half that. The concern? Facebook was a desktop-optimized website without a polished mobile presence, and by 2012, consumer attention had begun to accelerate towards mobile at a much higher rate than many initially predicted.  Facebook CEO Mark Zuckerberg reacted by not only proclaiming Facebook a mobile-first company, but also backing up that proclamation with action.  

• Turning to another company I’ve worked with via Apigee, T-Mobile launched its highly visible “Uncarrier” campaign—which offers streamlined, customer-friendly plans and services—while also investing in and executing a new IT vision dedicated to ongoing digital transformation. T-Mobile execs have credited the technology effort, spearheaded by CEO John Legere, with helping the company to introduce new services and better service customers. T-Mobile’s market cap has more than doubled since Legere took over in 2012.  

Keeping pace with changing customer needs

In the face of an existential threat, strong executive leadership is required to pivot the company to safety, as these examples attest. Digital transformationisn’t about deploying new technologies just to make an existing approach more efficient or to add a few new apps or features to the status quo; digital transformation is about keeping pace with changing customer needs by leveraging software platforms to continuously evolve how the business operates. This can be akin to turning an enormous ship—and a ship can’t turn very well without her captain, first mate, and other leaders showing the way.  

Research supports this. A recent Deloitte survey, for example, found that over “80 percent of respondents from digitally maturing organizations say their leaders have sufficient knowledge and ability to lead the company’s digital strategy,” compared to only “22 percent of early-stage business respondents [who] have the same belief.” Similarly, Gartner research finds that CEOs are seeking a “deeper understanding of digital business” as they shift their focus from growth in general to how technology helps them attain it.  

More recently, the onslaught of software devouring the world has been further accelerated by machine learning making everything smarter, voice interfaces changing how people interact with devices, and more. To keep pace, corporate leaders need to galvanize their companies to build and deploy software faster, make systems and data easily accessible inside and outside their companies, and improve digital experiences through not only machine learning but also constant data-driven iteration. 

Seven years after Andreesen’s editorial, the pace of digital disruption is still increasing, and so is the need for strong leadership to pivot fully into digital. Over half of the Fortune 500 has been acquired, merged or declared bankruptcy since 2000—and the companies that survive in coming years won’t be those whose leaders treat technology as an IT concern rather than a core part of the business.  

Retail organizations constantly face financial pressure to increase sales while maintaining profit margins. Digital commerce creates new opportunities and a more competitive landscape for retailers by allowing them to reach a global customer base online, but it also exposes them to competition from larger online retailers. To be successful in this environment, retailers must not only have a strong online presence to keep up with their competition and gain market share, but also uplift the transactional customer experience to a more experiential one.

In today’s data-driven artificial intelligence-inspired business environment, organizations require complex IT infrastructure to support various functions such as prospecting, product development, marketing, and data analytics. Managing and scaling this infrastructure can be challenging, especially as needs evolve. As a result, many organizations are turning to Google Cloud as a solution for meeting their business goals, rather than simply expanding their in-house IT resources with more equipment and personnel. Specifically, retailers across the globe are betting on Kubernetes on Google Cloud to take advantage of secure, reliable, and scalable infrastructure.

Here are a few examples of worldwide retailers adapting to changing customer expectations using intelligent infrastructure solutions including Google Kubernetes Engine (GKE), the most scalable and automated fully managed Kubernetes from Google Cloud.

Haravan is a Vietnamese ecommerce platform that aims to improve the process of buying and selling products, allowing businesses to focus on creating and selling their products.

By using Google Cloud, Haravan helped small and medium-sized enterprises in Vietnam achieve double-digit growth, consistently met its 99.97% uptime commitment to clients, efficiently managed 5 times the normal amount of ecommerce activity, and facilitated the implementation of artificial intelligence-powered expansion plans.

“We have a guaranteed commitment to enable any volume of sales for clients over all channels, be it social networks, marketplaces, livestream, or website. Only Google Cloud, with the flexible autoscaling of GKE, gives us certainty to meet our guarantees even in the most massive Black Friday surges.” —Hung Le, VP of Software Engineering, Haravan

Loblaw is Canada’s food and pharmacy leader and the nation’s largest retailer. The company operates over 2,500 locations, including corporate, franchised, and associate-owned stores, and employs nearly 200,000 full- and part-time employees.

By using Google Cloud, boosted the performance of the online grocery platform, resulting in higher conversion rates and increased revenue, recovered up to 50% of Site Reliability Engineers’ time for innovation, introduced new, real-time personalization features and shopping conveniences for customers, and enhanced resiliency to protect customers and revenue.

“Moving our online grocery site to Google Cloud gave us a 4x performance increase and the capacity to handle up to three times the traffic; and we can scale up at any time.” —Hesham Fahmy, VP Technology, Loblaw

L.L.Bean is a North American retail company known for its boots and mail-order catalog, which dates back to 1912. The company has a strong online presence, with ecommerce accounting for $1 billion of its annual revenues of $1.6 billion. Like many other retailers, L.L.Bean is adopting an omnichannel sales strategy by interacting with customers through various channels including print, physical stores, its website, app, and social media.

By using Google Cloud, L.L.Bean enhanced customers’ online experience through faster page load times and access to transaction history, allowed for a focus on providing value to customers rather than managing infrastructure, and enabled the rapid release of cross-channel services by reducing development cycles.

“GKE has significantly streamlined the process of upgrading nodes and masters. By comparison, upgrading even minor releases of another container solution that L.L.Bean tested resulted in the need to rebuild that solution’s clusters four times.” —Randy Dyer, Enterprise Architect, L.L.Bean

LPP is a Polish fashion retailer established in 1991 by Lubianiec and Piechocki, whose initials make up the company’s name. LPP currently manages five clothing brands that are popular in 38 countries across Europe, Africa, and Asia, and has over 24,000 employees based in its main offices in Central and Eastern Europe. Growing demand for its ecommerce services led LPP to migrate from an on-premises setup to Google Cloud, harnessing automation to ensure great shopping experiences globally.

By using Google Cloud, LPP promoted a DevOps culture among developers through streamlined deployment of new features, provided 90% more time for engineers to work on innovative solutions instead of managing infrastructure, instantly created and updated new VMs, allowing developers to quickly launch new features, ensured a seamless online shopping experience by automatically adjusting capacity to meet demand.

“GKE enables us to deploy new features for our ecommerce sites very quickly. Previously, it took weeks to launch new instances for each brand. Today, it takes seconds: we simply launch a new machine, deploy the code, and changes are reflected automatically across our environment.” —Marek Maciejewski, Head of IT Service Operations, LPP

Noon.com, based in Riyadh, Saudi Arabia, is a local ecommerce marketplace focused on serving the Middle East. The company aims to become the top online retailer in the region, supporting the growth of a digital economy for both consumers and local businesses.

By using Google Cloud, Noon.com built its ecommerce platform to access self-managed services, allowed developers to establish a fully operational staging environment within two weeks, provided uninterrupted service to nearly four times as many daily users during busy seasons using autoscaling on GKE, used real-time data streaming on BigQuery to inform business decisions and personalize the customer experience, and achieved 99.999% availability with no downtime for planned maintenance or schema changes using a fully managed relational database.

“Google Cloud-managed services are playing a major role in enabling Noon.com customers to get their shopping done whenever they need it, without experiencing any delays or glitches, and without us having to lose sleep at night to ensure our platform is functioning as it should.” —Alex Nadalin, SVP of Engineering, Noon.com

In conclusion, the retail industry is constantly evolving and retailers must stay up-to-date with the latest technology and customer preferences to remain competitive. Digital commerce has changed the landscape of retail, allowing businesses to reach a global customer base but also increasing competition. The use of Kubernetes on Google Cloud can help retailers improve the customer experience, streamline internal processes, and make data-driven and AI-inspired decisions. By embracing these changes, retailers can stay ahead in a constantly evolving industry. Get started today with an exclusive workshop, Unlocking efficiency and innovation with Kubernetes on Google Cloud.

Editor’s note: February is Black History Month—a time for us to come together to celebrate the diverse set of experiences, perspectives and identities that make up the Black experience. Over the next few weeks, we will highlight Black-led startups and how they use Google Cloud to grow their businesses. Today’s feature highlights Acclinate and its founders, Del and Tiffany. 

As patients, as caregivers, and as parents taking our own children to the doctor, we want recommended medications to be safe and effective. It’s a right everyone deserves.

It’s known that certain medications don’t work in the same way in all populations. For example, Albuterol, a medication often prescribed for asthma, is less effective in 67% of all Puerto Ricans and 47% of Black Americans. These problems—which can have deadly consequences—result from historically limited diversity in pharmaceutical clinical trials. 

We founded our startup Acclinate to integrate culture and technology to achieve more inclusive clinical research. Help pharmaceutical companies and healthcare organizations access and engage communities of color so research is more inclusive.

Bridging the health equity gap by building trust

It’s important that health research organizations access and engage communities of color so their efforts reflect all the people they serve. Take a disease like diabetes, which affects a significantly higher proportion of Black Americans. When you look at even recent clinical trials for diabetic drugs, the representation of Black Americans among participants is only in the low single digits, despite comprising 13 percent of the U.S. population and more than 40 percent of diabetes patients in this country. Industry leaders have been aware of the lack of diversity issue, but some have chosen to ignore it or brush it aside. The biggest problem, in our opinion, is that there have been no penalties for not achieving higher diversity figures in clinical trials, and only minor financial repercussions to pharma/biotech companies when their treatments either do not work across all groups once approved, or there is a lack of uptake by all groups due to the lack of testing in those groups. The lack of clinical trial diversity has adversely impacted the reputation of the industry and the ability to recruit diverse populations in the future. 

Acclinate integrates culture and technology to promote diverse patient representation in medical research. Our approach is not transactional. We build trust through our  #NOWINCLUDED community, which is an ongoing, ever-expanding digital platform that educates and engages with communities of color on health issues.

#NOWINCLUDED includes a website app, and social media presence where members can learn information about diseases, particularly those with greater negative impacts on people of color, such as cancer, diabetes, and cardiovascular diseases. Members can share stories and ask questions. By providing access to trusted resources about these health issues and the latest clinical research, we empower Black people to take control of their health and consider  participating in research that is shaping the future of healthcare.  

For healthcare-related organizations, we offer the opportunity to better understand the attitudes, aspirations, and unmet needs of underrepresented minority communities. Data from #NOWINCLUDED feeds our HIPAA-compliant SaaS platform, e-DICT™ (Enhanced Diversity in Clinical Trials), which uses predictive analytics and machine learning to identify individuals matching the requirements and most likely to be receptive to participation in a particular clinical trial.

Acclinate scales its platform with Google Cloud

We rely on Google Cloud services, including Vertex AI, to know whom to ask, when to ask, and how to ask for clinical trial participation. With Vertex AI, we enjoy a unified platform for developing our artificial intelligence models, including tools for preparing and storing our datasets. We can easily train and compare models using AutoML, which requires minimal ML expertise or effort with its intuitive graphical interface. This allows us to leverage more than ten ordinal and categorical data points to determine in real time a community member’s likelihood to enroll, which we call our Participation Probability Index (PPI). Our models evolve in an iterative process the more we interact with, and learn about, our community members.

We follow the pay-per-use Google Cloud Platform architecture model using serverless technology, which helps reduce infrastructure management costs and lets us focus on product development and engaging with communities across the U.S.

CloudSQL, a fully managed relational database service, integrates easily with BigQuery so we can glean insights for our clients in real time, all with Google Cloud’s robust security, governance, and reliability controls. Virtual Private Cloud (VPC) gives us scalable and flexible networking for our cloud-based resources and services. We also use Identity and Access Management (IAM) to simplify oversight of Google Cloud resource permissions for different user groups and roles, with appropriate security protections. 

API Gateway manages our APIs using Cloud Functions, which both use consumption-based pricing, plus give our developers consistent and highly secure access to our services through a well-defined REST API. We use Memorystore for Redis to reduce platform latency. This is done with a fully managed service powered by the Redis in-memory data store, which builds application caches for fast data access. All of this comes together to provide an outstanding experience for our platform’s users and contributors.

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Expanding influence with the Google for Startups Accelerator for Black Founders

Three months of one-on-one Google support and mentorship as part of the most recent Google for Startups Accelerator : Black Founders cohort not only helped us build our product, but also helped us earn external credibility. People use Google every day, so whether we’re trying to engage in conversations with industry experts or with somebody in a rural community, it is helpful to have the buy-in of a globally-recognized brand as we take on a historically difficult, systemic issue with challenges around trust. Getting access to the products, best practices, and people we need to build and grow through the Accelerator program has been priceless. For example, working with the Google AdWords team helped us generate important traffic from people interested in learning more about #NOWINCLUDED or sharing their story with us. Jason Scott, who leads the Google for Startups Accelerator: Black Founders program, is still connecting us to people in his network and identifying key opportunities for us months after the program wrapped. He continues to demonstrate that he is invested in seeing us succeed. 

Our company has made great progress against our goals, in part thanks to receiving capital from the Google for Startups Black Founders Fund. We received $100K in non-dilutive funding along with Google Cloud credits, Google.org Ads grants, and hands-on support. We used the funds to pay for the transition and development costs associated with moving to Google Cloud. The Google support and accountability has been incredible. After receiving the Google for Startups Black Founders Fund award, we’ve gone on to raise another $1M and moved our cloud from Salesforce to Google Cloud. 

We also had the amazing opportunity to be selected as one of six companies to take part in a face-to-face web conference with Sundar Pichai, Google’s CEO. We were thrilled to hear him explain his vision around health equity and the role Google plays. Ultimately, for us, it’s not just about the funding we get, but we are also gratified to receive support from an entity that truly believes in addressing this issue. We know Google is aligned with our mission of health and racial equity. 

Championing diversity in clinical trials

Our 2022 looks bright. We expanded our presence to Washington, D.C. as part of the Johnson & Johnson Innovation JLABS ecosystem. We were also selected to take part in the BLUE KNIGHT initiative created between Johnson & Johnson Innovation and the Biomedical Advanced Research and Development Authority (BARDA) under the U.S. Department of Health and Human Services. 

Acclinate is also on track to have contracts with five of the top 25 largest biopharmaceutical companies in the U.S this year. They’ve taken note, as has the Food and Drug Administration (FDA), that the lack of diversity in clinical trials represents a significant health concern—to the extent that the FDA has provided strong guidance for pharmaceutical companies to  diversify their clinical trials. At the same time, the industry is also responding to pressure from communities of people of color to make equitable representation a priority.

Today, we are in the fortunate but challenging position to have significant inbound opportunities coming our way. In response, we continue to recruit and hire talented people to join our team. On the technology side, we are happy to be aligned with Google Cloud to have powerful cloud infrastructure that will scale with us, as well as high-caliber champions united in partnership. With people’s lives at stake, we are passionate in our commitment to helping ensure medications do what they are supposed to do: heal and improve the quality of life for everyone who takes them. 

Hear Acclinate cofounders Del Smith and Tiffany Whitlow chat with Google’s Head of Startup Developer Ecosystem Jason Scott and fellow Black Founders Fund recipient Bobby Bryant about building on Google Cloud in a recent Google for Startups Instagram Live. 

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more