They say black never goes out of style. It’s something the team at Nuuly, URBN’s digital rental and resale business, know well. And it’s not just true of the company’s garments but their gadgets, too.

“I was having an offhand conversation with a UX designer recently,” Rebecca Sandercock, Nuuly’s strategy and insights manager, recalled in a recent interview from the company’s sunny, South Philly headquarters. “The designer was talking about how we had chosen dark mode for a number of interfaces internally because it actually saves so much on electrical output. They had the data to back that decision up, but more importantly, it’s just the kind of thing everyone is thinking about all the time here.”

Of course most every business is thinking about sustainability in some way these days. What makes Nuuly stand out is how quickly it can act, thanks in large part to the technology platform that’s made the entire enterprise possible.

“We’re kind of sustainable by our very nature,” Kim Gallagher, Nuuly’s director of marketing and customer success, said.

While she meant the rental and resale business, which helps customers buy fewer clothes and sees Nuuly items worn many times by many people—Gallagher could just as well have been referring to the sustainability inherent in, and enabled by, cloud computing.

There are the obvious, and oft-cited, advantages, such as how centralized data centers can operate more efficiently (some have been carbon neutral since the beginning). Yet there are even more subtle yet substantial benefits. In a marketplace and climate that are both changing faster and faster, sustainability requires a certain amount of agility. Such adaptability and scalability are intrinsic to the cloud technology that threads its way throughout Nuuly.

It turns out that being cloud native also means being sustainability native—as well as growth native. Since its 2019 launch, Nuuly’s net sales have risen roughly 6x over the first three fiscal years.

Cloud fits any situation

When URBN was developing Nuuly—launching in just 10 months—it chose to create everything from scratch on Google Cloud. Despite being part of a larger organization with decades of history and expertise, the company recognized the limitations presented by legacy systems and, more importantly, the necessity of building a wholly new platform that could be fully responsive.

The company has to react not only to new fashion trends but, crucially, the changing behaviors of customers. And not just their evolving tastes but also shopping habits, delivery preferences, unexpected customer service requests—is this a pattern, or a stain?—and social media chatter.

The pressure for a successful launch was high. The URBN portfolio, which also includes Urban Outfitters, Anthropologie, and Free People, had to keep evolving to satisfy a new generation of shopper who exists in an increasingly crowded and demanding digital marketplace.

The cloud’s responsiveness has thus proven its worth in creating a financially sustainable business as well as an environmentally sustainable one. Those even go hand in hand, as Gallagher points out: “Every customer who keeps renting is one who isn’t buying more occasion-specific clothes that go unworn most of the time.”

Dr. Alan Rosenwinkel, director of data science at URBN, had heard from his team about one garment that has become an emblem for the power of the platform. “It had been rented 25 different times before someone loved it enough to buy it and keep it forever,” he explained. (It’s also an emblem of the power of the cloud, that they would have the data awareness to track a single item so closely.)

It’s a new way of shopping made possible by a new way of computing. As one writer for Business Insider cheered, Nuuly “completely cured my addiction to fast-fashion.”

It makes for a healthy business, too. Sales for fiscal year 2019 exceeded $8 million and surpassed $24 million in 2020—one of the few URBN segments to grow during a tough year for fashion—and reached $47 million in 2021. The subscriber base had grown to 51,000 at the end of January.

Agility drives sustainability drives agility

For digital retailers to achieve such customer enthusiasm often relies as much on how the clothes get there as how they look. And those deliveries turn out to be a prime example of where Nuuly’s sustainability and technology meet.

For now, all shipping is handled through a state-of-the-art distribution center in Bucks County on the Philadelphia outskirts. Garments are shipped six-at-a-time, in fully reusable packaging, using ground transportation to keep the carbon footprint to a minimum. In certain limited geographic areas, shipments were sometimes taking longer than the two to five days most members would find acceptable.

“If we were an older company or weren’t set up from a technology perspective to be agile, we might have just said, ‘All right, we’re going to just go to three-day shipping for everyone,’” Rosenwinkel said. “That would drive up the cost, and the environmental impact. But we were able to be more strategic and more targeted about it.”

By regularly analyzing customer sentiment and retention data through BigQuery and Cloud Composer, and tying those to historical shipping times, Nuuly has been able to understand how shipping speed impacts its customers. Using a custom-built order management system, deliveries can be automatically adjusted to arrive more quickly, particularly when certain regions or days of the week are proving difficult to reach customers in time. These accelerated deliveries, like all Nuuly shipments, are made via UPS’s Carbon Offset program.

“Because we have the data, and the platforms to analyze it all,” Rosenwinkel said, “we’re delivering faster with the bare minimum impact on cost and emissions.”

It’s just one example of how modern retailers must juggle so many demands from consumers, workers, suppliers, and even regulators. Adding sustainability to that mix could be seen as a burden, but Nuuly shows how the right technology can lead to a holistic approach that makes all those interests work together even better.

And it allows for more opportunities and more kinds of sustainability, reaching from the designer’s atelier to the customer’s doorstep.

Sustainable details at every level

Back at the distribution center, workers can experience sustainability in a different way, as data is leveraged to enhance their well-being.

All workers are equipped with customized Android devices that help guide order tracking and fulfillment, plus cleaning, repairs, and reselling of garments as needs change throughout their lifecycle. Yet the insights go even deeper. The data science team has closely analyzed routes and repetitive motions for workers to keep their strenuous jobs as low-impact as the company’s broader environmental footprint.

“Through machine learning, we estimate we’ll be able to save our workers over 300,000 miles of steps over a five year period,” Rosenwinkel said. That’s enough walking to circumnavigate the globe 12.5 times.

The company has also applied ingenuity to one of the most notorious aspects of digital retail: packaging. Made from 100% post-consumer recycled materials like plastic bottles, Nuuly’s reusable carriers require no disposable bags or hangers to send goods back and forth. And once the packages have reached their end of life, the team is working with designers on ways to repurpose them into items that can then be offered for rental or sale on Nuuly.

“We’re really looking at the circular economy from every angle,” Gallagher said. “It’s built into the business.”

That includes not just the research the team did on-site at the Dry Cleaning & Laundry Institute—”We went to laundry school,” Gallagher jokes—but the digital tools that take those lessons even further. The team is always looking for ways to optimize fabric care, both to cut down on chemicals and water usage and extend the life of a garment. By analyzing the lifespan of every item, Nuuly not only makes them last longer but can identify problems faster. Employees even use custom apps to mark stains and damage so repair teams can more easily identify and fix the issues.

With its meticulous inventory tracking, Nuuly can even take marginal items, like a white gown or jeans with a small stain, and turn them into a custom dye job or an upcycling opportunity with a partner. These reworked items are then inserted back into the Nuuly Rent inventory as part of a growing collection of one-of-a-kind pieces, called Re_Nuuly.

Other services have launched quickly and easily thanks to the company’s cloud-enabled backend. Wanting to encourage more community and more reuse, the company created Nuuly Thrift. Debuting last fall after just a year in development, the team built everything from new interfaces to an evolved point of sale system.

It’s enabled Nuuly to offer many garments for rent or sale simultaneously, with “truly real-time inventories,” Rosenwinkel said. “So when it’s gone on one site, it shows up as gone on every site—no more surprises.”

Except for the good kind.

“I like to think we’re helping our customers think about ownership in a completely new way,” Sandercock said. “Once they run out of a use for a garment, they can offer it back, and sell it, and someone else will get to enjoy it and give it a new life. And Nuuly, we get to keep it in the community and keep it in the ecosystem, which is really cool—we’re taking extended responsibility over what happens to the clothing we create.”

Communication services providers (CSPs) are experiencing a period of disruption. Overall revenue growth is decelerating and is projected to remain below 1 percent per year, following a trend that started even before the pandemic.¹ At the same time, driven by the pandemic, data consumption in 2020 increased by 30 percent relative to 2019, with some operators even reporting increases of 60 percent.² 

The combination of pressure on revenues with rising data traffic costs is forcing operators to innovate in three fundamental ways. First, operators are looking to establish new sources of revenue. Second, increased network utilization must be met with a reduction in network cost. And third, there is an opportunity to gain new customers by improving the customer experience.

Fortunately, 5G offers a path forward across each of these three areas. Concepts such as network slicing and private networks allow CSPs to offer differentiated network services to public sector and enterprise customers. The disaggregation of hardware and software allows new vendors with unique strengths to enter the market and to enable CSPs to build, deploy, and operate networks in fundamentally new ways. And the ability to place workloads at the edge permits CSPs to offer compelling experiences to consumers and businesses alike. In this blog, we will discuss how CSPs can create a solid foundation for their cloud networks. 

Understanding telecommunications networks

First, it is useful to consider the way telecommunications networks were traditionally built. Initially, networks were built using physical network functions (PNFs) — appliances that used a tight combination of hardware and software to perform a specific function. PNFs offered the benefit of being purpose-built for a specific application, but they were inflexible and difficult to upgrade. As an example, deploying new features frequently required replacing the entire PNF, i.e., deploying a new hardware appliance.

The first step in improving deployment agility came with the concept of virtualized network functions (VNFs), software workloads designed to operate on commercial off-the-shelf (COTS) hardware. Rather than utilizing an integrated hardware and software appliance, VNFs disaggregated the hardware from the software. As such, it became possible to procure the hardware from one vendor and the software from another. It also became possible to separate the hardware and software upgrade cycles. 

However, while VNFs offered advantages over PNFs, VNFs were still an intermediate step. First, they typically needed to be run within a virtual machine (VM), and as such required a hypervisor to interface between the host operating system (OS) and the guest OS inside the VM. The hypervisor consumed CPU cycles and added inefficiency. Second, the VNF itself was frequently designed as a monolithic function. This meant that while it was possible to upgrade the VNF separately from the hardware, such an upgrade, even for a feature that affected only a portion of the VNF, required deployment of the entire large VNF. This created risk and operational complexity, which in turn meant that upgrades were delayed just as they were with PNFs.

Creating the foundation for cloud networks

The trick to establishing your cloud based network resides in the challenge of moving from VNFs to containerized network functions (CNFs) — network functions organized as containers as a collection of small programs, each of which can be independently operated. 

The concept of containers is not new. In fact, Google has been using containerized workloads for over 15 years. Kubernetes, which Google developed and open-sourced, is the world’s most popular container orchestration system, and is based on Borg, Google’s internal container management system.³ There are lots of benefits to using containers, but fundamentally, it frees developers from worrying about resource scheduling, interprocess communication, security, self-healing, load balancing, and many other tedious (but important!) tasks. 

Consider just a couple examples of benefits that containerization brings to network functions. First, when upgrading the network function to implement new features, you no longer need to re-deploy the entire network function. Instead, you only need to re-deploy the containers that are affected by the upgrade. This improves developer velocity and reduces the risk of the upgrade because, rather than infrequent upgrades that each introduce substantial changes, you can now have frequent upgrades that each deploy small changes. Small changes are less risky because they are easier to understand and to roll back in case of anomaly. Incidentally, this also improves your security posture because it reduces the time between when a security vulnerability is discovered and when a patch is deployed.

Speaking of security, another example of the benefits that containerization brings to network functions is an automatic zero-trust security posture. In Kubernetes, the communication among microservices can be handled by a service mesh, which manages mundane aspects of inter-services communication such as retries in case of failure and providing observability into communication. It can also manage other essential aspects such as security. For example, Anthos Service Mesh, which is a fully-managed implementation of the open-source Istio service mesh (also co-developed by Google), includes the ability to authenticate and encrypt all communications using mutual TLS (mTLS) and to deploy fine-grained access control for each individual microservice.

Automation and orchestration for cloud networks

CNFs bring tremendous benefits, but they also bring challenges. In place of a relatively small number of network appliances, we now have a large number of containers, each of which requires configuration, management, and maintenance. In the past, many of these processes were accomplished using manual techniques, but this is impossible to accomplish economically and reliably at the scale required by CNFs.

Fortunately, there are cloud-native approaches to solving these challenges. First, consider the problem of autonomously deploying and maintaining CNFs. The ideal way is to use the concept of Configuration as Data. Unlike imperative techniques such as Infrastructure as Code, which provide a detailed description of a sequence of steps that need to be executed to achieve an objective, Configuration as Data is a declarative method whereby the user specifies the desired end state (i.e., the actual desired configuration) and relies on automated controllers to continuously drive the infrastructure to achieve that state. Kubernetes includes such automated controllers, and the great news is that this method can be used not just for infrastructure but also for the applications residing on top of it, including CNFs. This cloud-native technique frees you from the toil and associated risk of writing detailed configuration procedures, so you can focus on the business logic of your applications.

As another example, consider the problem of understanding your network performance, including anomaly detection, root cause analysis, and resolution. The cloud-native approach starts with creating a data platform where both infrastructure and CNF monitoring data can be ingested, regularized, processed, and stored. You can then correlate data sets against each other to detect anomalies, and with AI/ML techniques, you can even anticipate anomalies before they happen. AI/ML is likewise indispensable in gaining an understanding of why the anomaly is happening, i.e. performing root cause analysis, and automated closed-loop controllers can be developed to correct the problem, ideally before it even happens.

Architecting for the edge

The transition from VNFs to CNFs is a critical piece in addressing the challenge that CSPs face today, but it alone is not enough. CNFs need infrastructure to run on, and not all infrastructure is created equal. 

Consider a typical 5G network. There are some functions, such as those associated with an access network, that need to be deployed at the edge. These functions require low latency, high throughput, or even a combination of the two. In 5G networks, examples of such functions include the radio unit (RU), distributed unit (DU), centralized unit (CU), and the user plane function (UPF). The first three are components of the radio access network (RAN), while the last is a component of the 5G core. At the same time, there are some other control plane functions such as the session management function (SMF) or the authentication and mobility management function (AMF) that do not have such tight latency and high throughput requirements and can thus be placed in a more centralized data center. Furthermore, consider an AI/ML use case where a particular model (perhaps for radio traffic steering) needs to run at the network edge because of its latency requirements. While the model itself needs to run at the edge, model training (i.e., generating the model coefficients) is frequently a compute-intensive exercise that is latency-insensitive and is thus more optimal to run in a public cloud region.

All of these use cases have one thing in common: they call for a hybrid deployment environment. Some applications must be deployed at the edge as close to the user as possible. Others can be deployed in a more centralized environment. Still others can be deployed in a public cloud region to take advantage of the large amount of compute and economies of scale available therein. Wouldn’t it be convenient — if not transformational — if you could use a single environment for deploying at the edge, in a private datacenter, and in public cloud, with a consistent set of security, lifecycle management, policy, and orchestration resources across all such locations? This is indeed what Google Distributed Cloud, enabled by Anthos, brings to the table.

With Google Distributed Cloud, you can architect a 5G network deployment such as the one shown below.

Business benefits of cloud networks

Beyond the technical benefits, consider the business benefits of such an architecture. First, by following the best practices of hardware and software disaggregation, it permits the CSP to procure the infrastructure and the network functions from different vendors, spurring competition among vendors. Second, each workload is placed in precisely the right location, enabling efficient utilization of hardware resources and offering compelling low-latency, high-throughput services to users. Third, because the architecture utilizes a common hybrid platform (Anthos), it makes it easy to move workloads across infrastructure locations. Fourth, the separation of workloads into microservices accelerates time-to-market when developing new features or applications, such as those enabling enterprise use cases. And finally, the container management platform supports the simultaneous deployment of both network functions and edge applications on the same infrastructure, allowing the operator to deploy new experiences such as AR/VR directly on bare metal as close to the user as possible.

The next generation cloud network is now

There is a lot more we could say, but perhaps the most important takeaway is that this architecture is not a future dream. It exists today, and Google is working with leading CSPs and network vendor partners to deploy it, helping them realize the promise of 5G to deliver new revenues, reduce operating costs, and enable new customer experiences.

To learn more, watch the video series on the cloudification of CSP networks.

Discover what’s happening at the edge: How CSPs Can Innovate at the Edge.

^{1.Statista, Forecast growth worldwide telecom services spending from 2019 to 2024
2 PricewaterhouseCoopers, Global entertainment and media outlook 2021-2025
3. Borg: The Predecessor to Kubernetes}

The digital media and entertainment industry is experiencing dramatic growth, as audiences migrate to online experiences and content providers seek to deliver new and innovative content. According to The Global Internet Phenomena Report, streaming video accounted for 53.7% of internet bandwidth traffic, up by 4.8% from a year ago. This rapid growth of over-the-top content is straining existing infrastructure, fueling media companies’ shift to the public clouds with their global presence and greater distribution capacities. In addition, other use cases such as gaming, social networks, AR/VR experiences, and education continue to fuel the need for intelligent media services and operations.

Today, at the 2022 NAB Show Streaming Summit, we’re excited to announce the general availability of Media CDN — a modern, extensible platform for delivering immersive experiences with unparalleled scale and intelligence. Media CDN will enable media and entertainment customers to efficiently and intelligently deliver streaming experiences to viewers anywhere in the world. The same infrastructure that Google has built over the last decade to serve YouTube content to over 2 billion users is now being leveraged to deliver media at scale to Google Cloud customers with Media CDN.

Unparalleled planet-scale reach and scale

Media CDN’s foundational advantage is the Google network. We have invested decades of resources to build tremendous capacity and reach in over 200 countries and more than 1,300 cities around the globe. Modern video applications are sensitive to fluctuations in latency, so getting content closer to users enables higher bitrates and reduces rebuffers, resulting in a superior experience for the end user. Media CDN builds on the success of the existing Cloud CDN portfolio for web and API acceleration and complements it by enabling delivery of immersive media experiences.

In addition to running on planet-scale infrastructure, Media CDN tailors delivery protocols to individual users and network conditions. Media CDN includes out-of-the-box support for QUIC (HTTP/3), TLS 1.3, and BBR, optimizing for last-mile delivery . When the Chrome team rolled out widespread support for QUIC, video rebuffer time decreased by more than 9% and mobile throughput increased by over 7%.

Media CDN also achieves industry-leading offload rates. With multiple tiers of caching, we minimize calls to origin — even for infrequently accessed content. This alleviates performance or capacity stress in the content origin and saves costs. These features are built into the product and seamlessly support customer content hosted on Google Cloud, on-premises, or on a third-party cloud.

“We are excited to leverage Media CDN to continue to deliver an exceptional streaming experience for Stan users across Australia. With Google’s massive network, and a deep reach into the ISPs, we are able to deliver the highest quality video for our users, no matter where they are”—John Hogan, Chief Technology Officer, Stan

“Our mission at U-NEXT is to deliver the highest quality and most entertaining content to our users. Google Cloud’s Media CDN helps us efficiently scale our infrastructure, which is challenging with a vast library of content. Media CDN offloaded 98.3% of requests from our origin server while delivering consistent great quality.”—Rutong Li, Chief Technology Officer, U-NEXT

Broader platform for monetization and immersive experiences

While global distribution is critical for a high-quality end-user experience, it’s only one piece of delivering a world-class platform for immersive experiences. Media CDN offers additional capabilities to enable this transformation — ad insertion, ecosystem integrations and platform extensibility, and powerful AI/ML analytics for interactive experiences.

Streaming providers can improve monetization through integrated ad serving via the Video Stitcher API, which allows manipulation of video content to dynamically insert ads.

Through extensible ecosystem integrations, Media CDN connects customers to key capabilities to simplify their operations. For example, the Transcoder API supports custom streaming formats, while the Live Stream API transcodes mezzanine live signals into direct-to-consumer streaming formats, for multiple device platforms.

Media CDN is built with AI/ML that will give viewers more control over how they see, experience, and even interact with content. For example, sports fans watching a game can obtain real-time stats and analytics, viewers can purchase items from virtual billboards, etc.

Cloud-native and developer-friendly operations

Media companies are under pressure to develop and deploy innovative experiences at a furious pace. Media CDN was built by developers, for developers, with automation and observability built in, giving media providers the speed and flexibility they need to integrate delivery provisioning and management into their content release processes.

Media CDN offers comprehensive APIs and automation tools such as Terraform. Detailed, pre-aggregated metrics and playback tracing make it easy to diagnose performance across the entire infrastructure stack. Real-time visibility is provided via Google Cloud’s operations suite, and integrates with tools that developers already use such as Grafana and ElasticSearch.

“Leveraging the same infrastructure as YouTube, Google Cloud’s Media CDN combines geographic reach, API-first architecture and integration with the Cloud operations suite. This is a transformative move that is aligned with the future of the CDN industry.”— Ghassan Abdo, Research Vice President, WW Telecom, Virtualization and CDN, IDC

“Viewers around the world are demanding best-in-class video quality and performance across modes of consumption. A video-first delivery network can be a game changer in this space. We’re excited to partner with Google Cloud and to leverage Media CDN to enable premium video experiences and customer engagements.”—Juan Martin, Founder and CTO, Firstlight Media

Planet-scale advanced security

Media CDN lets streaming media providers take advantage of Google’s decades-long experience delivering video safely, securely, and reliably. The platform includes deep integration with Google Cloud Armor for planet-scale DDoS protection and a rich set of capabilities to detect and mitigate attacks, prevent abuse, manage risk, and comply with regulatory or licensing requirements.

If you want to deliver rich, immersive experiences to global audiences with an extensible, modern delivery platform, we’d love to hear from you. For more information, including technical specifications and platform architecture, please visit cloud.google.com/media-cdn. To get started with Media CDN, contact your sales team.

With access to more data than ever before, companies have never been better positioned to adopt precision marketing methods and target the right customers at the right time. Emarsys, a digital marketing platform, enables its clients to collect, analyze, and act on a wide variety of data. From websites to mobile apps to emails, Emarsys’ customers can handle data from all its digital channels on a single, easy-to-use platform. Emarsys also makes sure that customers receive the highest quality data possible, making for smarter decisions and better business practices.

“We were close to the limits of our internal data warehouse, scalability-wise. We didn’t want to get to the point where we’d have to delete data or cancel projects. In Google Cloud we saw a platform that could scale with our ambitions and be optimized for AI and real-time solutions.”

—Levente Otti, Head of Data, Emarsys

Since launching as an email solutions provider in 2000, Emarsys has grown into the world’s largest independent digital marketing platform, with more than 2,500 clients worldwide and reaching more than 1.4 billion people. By 2016, the company felt that its existing data warehouse platform was close to its limits, affecting not just day-to-day operations but also important strategic goals.

“We were close to the limits of our internal data warehouse, scalability-wise. We didn’t want to get to the point where we’d have to delete data or cancel projects,” says Levente Otti, Head of Data at Emarsys. “In Google Cloud, we saw a platform that could scale with our ambitions and be optimized for AI and real-time solutions.”

Minimal maintenance, unlimited scale with Google Cloud

Digital marketing is a highly competitive environment. Emarsys works alongside big players with a huge market share on the one hand and smaller, specialist companies on the other. It has thrived by successfully combining the all-inclusive offerings of the former with the agility of the latter, constantly looking for ways to innovate and improve. In recent years, the company had started to feel that the ability to handle large quantities of data was no longer enough. The next challenge was speed. “We truly believe that in the future, everything will be done in real time, including data processing, analytics, and AI predictive models,” Levente says.

At the start of 2016, Emarsys’ existing data warehouse was a software-as-a-service solution running on-premises, which required hardware and software maintenance in order to keep up with the company’s growing appetite for data-heavy use cases such as prediction and analytics. The existing platform had proven its worth processing large amounts of data in batches, but its real-time capabilities were limited. Moreover, Emarsys had begun to experiment with AI technology, but found that its data warehouse couldn’t scale to accommodate some of the more resource-intensive processes, such as training the predictive models. The company decided that it needed a new, cloud-based data platform.

After evaluating some of the leading cloud providers, Emarsys chose Google Cloud for its mature AI capabilities and its ease of use. “With the other solutions, we still had to rent virtual machines and hardware and be responsible for maintenance. At the time, Google Cloud was the only provider that could take that management overhead away from us, while keeping customers accounted for on every query level,” Levente says.

To implement its new data platform, Emarsys teamed up with Google Cloud Partner Aliz. Over a series of meetings, workshops, and architecture reviews, Aliz helped Emarsys navigate the Google Cloud ecosystem to find the right products for the solution it was looking for. “Aliz really helped us set off in the right direction,” explains Levente.

With Google BigQuery, we can run queries which process terabytes of data, in seconds. We can also develop our own user-defined functions, incorporating Bayesian statistics into our predictive algorithms. That means we can take into account historical data, resulting in much more accurate predictions in a scalable way within seconds.”

—Levente Otti, Head of Data, Emarsys

Emarsys’ new data platform would actually be two: one platform for batch processing data and one for real-time analysis and interactions. Firstly, a proprietary publishing component gathered all the data points from Emarsys’ various channels including the website, mobile, emails, and custom events. With Cloud Pub/Sub and Cloud Dataflow, Emarsys transported and processed the data into BigQuery, which allows for further work and reviews that take into account errors or delayed events. After this, the data was exported to the main batch processing platform, which ran on BigQuery. For the real-time analytics, Emarsys used Cloud Bigtable to access data and Cloud Dataflow to pipeline it into the real-time platform, which could communicate with AI components or interaction components via an API to deliver real-time interactions with customers.

On top of the overall data infrastructure, Emarsys built a new AI platform with Google Cloud components. Training the predictive models had been an issue in the past due to the large number of resources required, so Emarsys chose to use Google Kubernetes Engine clusters, which can scale up and down on demand, without the need for hardware configuration or management. The trained models were held securely in Cloud Storage. From here, they were integrated with BigQuery for power and flexibility, allowing Emarsys to improve not just the speed of its AI predictions but also the quality.

“With Google BigQuery, we can run queries which process terabytes of data, in seconds,” shares Levente. “We can also develop our own user-defined functions incorporating Bayesian statistics into our predictive algorithms. That means we can take into account historical data, resulting in much more accurate predictions in a scalable way within seconds.”

Real-time insight, long-term satisfaction

Google Cloud enabled Emarsys to build a scalable data and AI platform that delivers powerful, actionable insights in real time. According to Levente, the company wanted to spend less time managing overload and more time considering how it should handle data. An immediate result of the new platform has been that data is now available in a scalable way, without hardware additions and management.

“With Google Cloud, we’ve been able to build a truly real-time data platform. The norm used to be daily batch processing of data. Now, if an event happens, marketing actions can be executed within seconds, and customers can react immediately. That makes us very competitive in our market.”

—Levente Otti, Head of Data, Emarsys

The clear and innovative pricing schemes of Google Cloud have also brought a new level of accountability to Emarsys’ costs in a way that wasn’t possible with its on-premises infrastructure. “Now that we only pay for what we use, we can assign costs to specific customers or queries, which has a huge impact on our pricing and product development strategies,” says Levente.

Thanks to the power of BigQuery and the scale at which it can handle data, Emarsys can now apply its analytics and AI tools to their full potential. “It’s very important to enable our clients to create the best possible experience for customers,” says Levente. At the same time, the company has cut its AI platform costs by 70% with Kubernetes while increasing scalability compared to the previous solution. The whole data platform was built to be scalable, and its first big test came during the retail peak of Black Friday, when it comfortably handled 250,000 events per second. “Perhaps the biggest impact on the business came with the real-time nature of the new platform,” says Levente.

“With Google Cloud, we’ve been able to build a truly real-time data platform,” he explains. “The norm used to be daily batch processing of data. Now, if an event happens, marketing actions can be executed within seconds, and customers can react immediately. That makes us very competitive in our market.”

Since implementing the new platform, Emarsys has continued to innovate with it and is about to release a new Real-Time Decision Framework, which will provide customers with even more real-time products and tools. The company continues to work with Aliz and Google Cloud, exploring other products such as Google BigQuery ML and TensorFlow to improve its AI processes. “We had a problem that we wanted to tackle now, and for us, Google Cloud was the best way of doing that,” says Levente. “But it was also about looking ahead. We felt that Google Cloud offered us the best way of future-proofing our platform.”