Digital technology offers increasing flexibility and choice to consumers. As a result, the retail industry is dramatically shifting toward more tailored and personalized experiences for shoppers, and businesses are rethinking how they deliver value to customers.

This couldn’t be more true for the beauty retailing industry where leading companies are turning to digital technology to create customized shopping experiences.

At Google Cloud, we’re particularly excited about our work with Ulta Beauty, the largest beauty retailer in the United States with more than 1196 stores in all 50 states, and how the company is using Google Cloud technology solutions to power personalization and redefine beauty retailing.

Established in 1990, Ulta Beauty has had incredible success as a company, and as customers become more discerning and curious about their purchases, the company is finding new ways to meet their changing needs.

Recently, leaders at Ulta Beauty recognized a huge opportunity to complement and enhance the shopping experience by helping beauty enthusiasts navigate through more than 500 brands and 25,000 products carried in their stores and online channel.

They decided to leverage the data from Ulta Beauty’s successful Ultamate Rewards loyalty program to create and offer more unique and personalized user experiences.

With more than 30 million members generating data through sales, transactions, product reviews, and social media engagement, Ulta Beauty’s Loyalty Program creates a comprehensive data set, and the company sought the right technology partner to help organize, analyze and transform that data into valuable insights for its customers.

Ulta Beauty’s leaders knew they had an opportunity to leverage data analytics and machine learning to reach customers in new ways, enhance the guest experience, and continue to grow their active loyalty member base. After considering a number of cloud providers, they chose to expand their existing partnership with Google Cloud.

“Google Cloud listened to our needs and worked in tandem with our engineering team to address our challenges,” said Michelle Pacynski, vice president of digital innovation at Ulta Beauty. “The ease of working with the Google Cloud team and their breadth of experience made the decision a no-brainer, laying the foundation for a great partnership.”  

In 2019, Ulta Beauty announced it was working with Google Cloud Platform to unify and organize its data, using:

• BigQuery to perform data analysis and generate dynamic content, personalized product recommendations, and event-based messages for customers.
• Cloud Storage to provide highly available, secure, resilient and cost-effective access to data across the entire enterprise.
• Compute Engine for the high-performance scalability needed to grow with customer demand while painlessly migrating existing applications to the cloud.
• Anthos to build a hybrid cloud foundation that allows their applications to take advantage of all this data, combining the power and flexibility of GKE with the ability to leverage their existing investment in secure infrastructure on-premises.

Our partnership with Ulta Beauty has enabled increased engagement with customers in store and online, and the creation of new tools and capabilities, including a new Virtual Beauty Advisor tool to deliver tailored recommendations and help shoppers choose the right products, and a Customer Conversation Platform that’s enabling deeper connections with guests, ultimately driving customer loyalty.

“It’s been a really efficient process so far due in part to the ease of working with the Google team,” said Michelle Pacynski, vice president of digital innovation at Ulta Beauty. “They’re experienced, approachable, and their can-do style makes for a great partnership. They listened to our needs and worked in tandem with our engineering team, figuring things out, and getting it done.”

Editor’s note: Today’s guest post comes from Indian social media platform ShareChat. Here’s the story of how they improved performance, app development, and analytics for serving regional content to millions of users using Google Cloud. 

How do you create a social network when your country has 22 major official languages and countless active regional dialects? At ShareChat, we serve more than 160 million monthly active users who share and view videos, images, GIFs, songs, and more in 15 different Indian languages. We also launched a short video platform in 2020, Moj, which already supports over 80 million monthly active users. 

Connecting with people in the language they understand

As mobile data and smartphones have become more affordable in India, we noticed a large new segment of people, many in rural areas, being welcomed onto the internet. However, many of them didn’t speak English, and when it comes to accessing content and information—language plays a significant role. Instead of joining other social media sites where English reigned supreme, new internet users chose to join language or dialect-specific Whatsapp groups where they felt more comfortable instead.

So, we set out to build a platform where people can share their opinions, document their lives, and make new friends, all in their native language. ShareChat simplifies content and people discovery by using a personalized content newsfeed to deliver language-specific content to the right audience.

Given the high-intensity data and high volume of content and traffic, we rely heavily on IT infrastructure. On top of that, a large number of our users rely on 2G networks to post, like, view, or follow each other. Our platform needs to deliver great experiences to people who are spread out across the country and different networks without any reduction in performance.

The right cloud partner to support future growth

ShareChat was born in the cloud—we already knew how to scale systems to serve a large customer base with our existing cloud provider. But like many companies, we struggled with over-provisioning compute and storage to accommodate unpredictable traffic and avoid running out of storage. With demand rising for local language content and an increase in online interactions in response to the COVID-19 crisis, we realized that we would need a more efficient way to scale dynamically and allocate resources as needed.

Google Cloud was a natural choice for us. We wanted to partner with a technology-first company that would make it easy (and cost-effective) to manage a strong technology portfolio that would allow us to build whatever we wanted. Google is at the forefront of technology innovation and provided everything we needed to build, run, and manage our applications (including creating an efficient DevOps pipeline to fix and release new features quickly). 

We had a few issues in mind at the start of discussions with the Google Cloud team, but over time, as we got information and support from them, we realized that these were the partners we wanted in our corner when it came time to tackle our most challenging problems. In the end, we decided to take our entire infrastructure to Google Cloud.

To support millions of users, we deploy and scale using Google Kubernetes Engine. While we analyze our data using a combination of managed data cloud services, such as Pub/Sub for data pipelines, BigQuery for analytics, Cloud Spanner for real-time app serving workloads, and Cloud Bigtable for less-indexed databases. We also rely on Cloud CDN to help us distribute high-quality and reliable content delivery at low latency to our users. 

We now use just half the total core consumption of our legacy environment to run ShareChat’s existing workloads.

Google Cloud delivers better outcomes at every level 

By moving to Google Cloud, we saw major benefits in several key areas: 

Zero-downtime migration for users

At the time of migration, we had over 70 terabytes of data, consisting of 220 tables—some of which were up to 14 terabytes with nearly 50 billion rows. Due to our data’s interdependencies, moving services over one at a time wasn’t an option for us. 

Even though we were migrating such large volumes of data, we didn’t want to impact any of our customers. Latency spikes for out-of-sync data might affect message delivery. For instance, if a message or notification was delayed, we didn’t want to risk a bad user experience causing someone to abandon ShareChat. 

To prepare for the move, we ran a proof-of-concept cluster for over four months to test database performance in a real-world scenario for handling more than a million queries per second. Using an open-source API gateway, we replicated our legacy data environment into Google Cloud for performance testing and capacity analysis. As soon as we were confident Google Cloud could handle the same traffic as our previous cloud environment, we were ready to execute.

Using wrappers, we were able to migrate without having to change anything in our existing application code. The entire migration of 60 million users to Google Cloud took five hours—without any data loss or downtime. Today, ShareChat has grown to 160 million users, and Google Cloud continues to give us the support we need.

Scaling globally to meet unexpected demand

We rely on real-time data to drive everything on ShareChat by tracking everything that goes on in our app—from messages and new groups to content people like or who they follow. Our users create more than a million posts per day, so it’s critical that our systems can process massive amounts of data efficiently. 

We chose to migrate to Spanner for its global consistency and secondary index. Unlike our legacy NoSQL database, we could scale without having to rethink existing tables or schema definitions and keep our data systems in sync across multiple locations. It’s also cost-effective for us—moving over 120 tables with 17 indexes into Cloud Spanner reduced our costs by 30%.

Spanner also replicates data seamlessly in multiple locations in real time, enabling us to retrieve documents if one region fails. For instance, when our traffic unexpectedly grew by 500% over just a few days, we were able to scale horizontally with zero lines of code change. We were also launching our Moj video app simultaneously, and we were able to move it to another region without a single issue. 

Simplifying development and deployment

On average, we experience about 80,000 requests per second (RPS) –nearly 7 billion RPS per day. That means daily push notifications sent out to the entire user base about daily trending topics can often result in a spike of 130,000 RPS in just a few seconds. 

Instead of over-provisioning, Google Kubernetes Engine (GKE) enables us to pre-scale for traffic spikes around scheduled events, such as holidays like Diwali, when millions of Indians send each other greetings. 

Migrating to GKE has also enabled us to adopt more agile ways of work, such as automating deployment and saving time with writing scripts. Even though we were already using container-based solutions, they lacked transparency and coverage across the entire deployment funnel. 

Kubernetes features, such as sidecar proxy, allows us to attach peripheral tasks like logging into the application without requiring us to make code changes. Kubernetes upgrades are managed by default, so we don’t have to worry about maintenance and stay focused on more valuable work. Clusters and nodes automatically upgrade to run the latest version, minimizing security risks and ensuring we always have access to the latest features.

Low latency and real-time ML predictions

Even though many of our users may be accessing ShareChat outside of metropolitan areas, it doesn’t mean they’re more patient if the app loads slowly or their messages are delayed. We strive to deliver a high-performance experience, regardless of where our users are. 

We use Cloud CDN to cache data in five Google Cloud Point of Presence (PoP) locations at the edge in India, allowing us to bring content as close as possible to people and speeding up load time. Since moving to Cloud CDN, our cache hit ratio has improved from 90% to 98.5%—meaning our cache can handle 98.5% of content requests. 

As we expand globally, we’d like to use machine learning to reach new people with content in different languages. We want to build new algorithms to process real-time datasets in regional languages and accurately predict what people want to see. Google Cloud gives us an infrastructure optimized to handle compute-intensive workloads that will be useful to us both now—and in the future.  

The confidence to build the best platform

Our current system now performs better than before we migrated, but we are continuously building new features on top of it. Google’s data cloud has provided us with an elegant ecosystem of services that allows us to build whatever we want, more easily and faster than ever before. 

Perhaps the biggest advantage of partnering with Google Cloud has been the connection we have with the engineers at Google. If we’re working to solve a specific problem statement and find a specific solution in a library or a piece of code, we have the ability to immediately connect with the team responsible for it. 

As a result, we have experienced a massive boost in our confidence. We know that we can build a really good system because we not only have a good process in place to solve problems—we have the right support behind us.

Organizations across industries are looking to AI to turn troves of data into intelligence, powered by the latest advances in generative AI. Yet for many organizations, there is a barrier to adopting the latest models because they can be costly to train or serve. A new class of cloud GPUs is needed to lower the cost of entry for businesses that want to tap the power of AI. 

Today, we’re introducing G2, the newest addition to the Compute Engine GPU family in Google Cloud. G2 is the industry’s first cloud VM powered by the newly announced NVIDIA L4 Tensor Core GPU, and is purpose-built for large inference AI workloads like generative AI. G2 delivers cutting-edge performance-per-dollar for AI inference workloads that run on GPUs in the cloud. By switching from NVIDIA A10G GPUs to G2 instances with L4 GPUs, organizations can lower their production infrastructure costs up to 40%. We also found that customers switching from NVIDIA T4 GPUs to L4 GPUs can achieve 2x-4x better performance. As a universal GPU offering, G2 instances also help accelerate other workloads, offering significant performance improvements on HPC, graphics, and video transcoding. Currently in private preview, G2 VMs are both powerful and flexible, and scale easily from one up to eight GPUs. 

Currently organizations require end-to-end enterprise ready infrastructure that will future proof their AI and HPC initiatives for a new era. G2s will be ready to be deployed on Vertex AI, GKE, and GCE, giving customers the freedom to architect their own custom software stack to meet their performance requirements and budget. With optimized Vertex AI support for G2 VMs, AI users can tap the latest generative AI models and technologies. With an easy to use UI and automated workflows, customers can access, tune and serve modern models for video, text, images, and audio without the toil of manual optimizations. The combination of these services with the power of G2 will help customers harness the power of complex machine models for their business.

NVIDIA L4 GPUs with Ada Lovelace Architecture

G2 machine families enable machine learning customers to run their production infrastructure in the cloud for a variety of applications such as language models, image classification, object detection, automated speech recognition, and language translation. Built on the Ada Lovelace architecture with fourth-generation Tensor Cores, the NVIDIA L4 GPU provides up to 30 TFLOPS of performance for FP32, and 242 TFLOPs for FP16. Newly added FP8 support, on top of existing INT8, BFLOAT16 and TF32 capabilities, makes the L4 ideal for ML inference. 

With the latest third-generation RT Cores and DLSS 3.0 technology, G2 instances are also great for graphics-intensive workloads such as rendering and remote workstations when paired with NVIDIA RTX Virtual Workstation. NVIDIA L4 provides 3x video encoding and decoding performance, and adds new AV1 hardware-encoding capabilities. For example, G2 can enable gaming customers running game engines such as Unreal and Unity with modern graphics cards to run real-time applications. Likewise, media and entertainment customers that need GPU-enabled virtual workstations can use the L4 to create photo-realistic, high-resolution 3D content for movies, games, and AR/VR experiences using applications such as Autodesk Maya or 3D Studio Max.

What customers are saying

A handful of early customers have been testing G2 and have seen great results in real-world applications. Here are what some of them have to say about the benefits that G2 with NVIDIA L4 GPUs bring:

AppLovin

AppLovin enables developers and marketers to grow with market leading technologies. Businesses rely on AppLovin to solve their mission-critical functions with a powerful, full stack solution including user acquisition, retention, monetization and measurement.

“AppLovin serves billions of AI powered recommendations per day, so scalability and value are essential to our business,” said Omer Hasan, Vice President, Operations at AppLovin. “With Google Cloud’s G2 we’re seeing that NVIDIA L4 GPUs offer a significant increase in the scalability of our business, giving us the power to grow faster than ever before.”

WOMBO

WOMBO aims to unleash everyone’s creativity through the magic of AI, transforming the way content is created, consumed, and distributed. 

“WOMBO relies upon the latest AI technology for people to create immersive digital artwork from users’ prompts, letting them create high-quality, realistic art in any style with just an idea,” said Ben-Zion Benkhin, Co-Founder and CEO of WOMBO. “Google Cloud’s G2 instances powered by NVIDIA’s L4 GPUs will enable us to offer a better, more efficient image-generation experience for users seeking to create and share unique artwork.”

Descript

Descript’s AI-powered features and intuitive interface fuel YouTube and TikTok channels, top podcasts, and businesses using video for marketing, sales, and internal training and collaboration. Descript aims to make video a staple of every communicator’s toolkit, alongside docs and slides. 

“G2 with L4’s AI Video capabilities allow us to deploy new features augmented by natural-language processing and generative AI to create studio-quality media with excellent performance and energy efficiency” said Kundan Kumar, Head of Artificial Intelligence at Descript.

Workspot

Workspot believes that the software-as-a-service (SaaS) model is the most secure, accessible and cost-effective way to deliver an enterprise desktop and should be central to accelerating the digital transformation of the modern enterprise.

“The Workspot team looks forward to continuing to evolve our partnership with Google Cloud and NVIDIA. Our customers have been seeing incredible performance leveraging NVIDIA’s T4 GPUs. The new G2 instances with L4 GPUS through Workspot’s remote Cloud PC workstations provide 2x and higher frame rates at 1280×711 and higher resolutions” said Jimmy Chang, Chief Product Officer at Workspot.

Pricing and availability

G2 instances are currently in private preview in the following regions: us-central1, asia-southeast1 and europe-west4. Submit your request here to join the private preview, or to receive a notification as when the public preview begins. Support will be coming to Google Kubernetes Engine (GKE), Vertex AI, and other Google Cloud services as well. We’ll share G2 public availability and pricing information later in the year.

While the shift to digital business and the cloud has been well under way for some years now, organizations today have a new sense of urgency due to COVID-19. Delivering digital transformation is no longer a ‘nice to have’ option, rather, it is an operational imperative. Taking advantage of the infrastructure, platform and solution gains that cloud and microservices architecture provide is a must for brands today. 

At Google Cloud, we understand the pressures and challenges organizations of all sizes, across all industries are facing. The pandemic has dramatically impacted global commerce at-large, exposing (for many organizations across multiple sectors) gaps in omnichannel capabilities, business continuity and forecasting plans, not to mention spots in supply chain agility, resilience and responsiveness. 

A rapidly evolving consumer-driven commerce landscape has put innovation squarely in the spotlight for supply chain teams all over the world, with the effects of the global pandemic making it increasingly difficult for manufacturers, wholesalers, third party logistics providers and retailers (in particular) to weather the perfect storm of fast-moving consumer trends and a need for ‘always on’ digital innovation. 

These same effects have driven increasing interest and uptake of technology like the Manhattan Active® suite of solutions, as well as our own cloud platform; both of which afford organizations the levels of agility, flexibility and scalability needed to insulate their people, processes and long-term business strategies against unforeseen future obstacles such as global pandemics or international trade disputes.

An excellent example of this agility, flexibility and scalability in action is PVH’s response to the global pandemic. One of the most admired fashion and lifestyle companies with such iconic brands as Calvin Klein, TOMMY HILFIGER, Van Heusen, and IZOD, PVH was forced to temporarily close its physical stores and, as a result, experienced a sudden massive increase in online sales. The retailer was able to quickly pivot by adjusting its business rules in Manhattan Distributed Order Management (part of Manhattan Active Omni) to expose store inventory to online consumers and reroute its fulfillment processes. Thanks to Manhattan’s solution delivered through Google Cloud, in a matter of days, PVH was able to leverage both its distribution centers and vast store network to fulfill its online orders.

“The events of 2020 have accelerated retail and ecommerce operations forward,” said David Herridge, executive vice president of Global Value Chain Technologies for PVH. “With quick, creative thinking and the right partner, we were able to pivot operations, satisfy our customers and prepare for the future.”

Manhattan’s products have been recognized for their ability to solve real-world challenges through innovation, and used by many of the world’s top brands to solve some of their most complex commerce and supply chain challenges: the latest recognition is Manhattan’s position as sole leader in the 2021 Forrester Wave™ for Order Management Solutions. 

Since December 2018, Google Cloud has been collaborating closely with the team at Manhattan and its ‘always on’, versionless approach to innovation. And, during the last two and a half years, Manhattan has significantly accelerated its cloud-first solutions and market adoption, resulting in tremendous growth in its overall cloud business efforts. 

By building cloud native solutions on Google Cloud, the teams at Manhattan continue to deliver the high-performance, elastic, high-redundancy, secure solutions their customers rely on. Moreover, it means both Google Cloud and Manhattan continue to innovate and push the boundaries of what is possible in terms of the supply chain and omnichannel innovations that underpin global commerce – innovation that is needed more now than maybe ever before.

Our commitment to distributed cloud solutions and ongoing innovation, not to mention the fact Google Cloud operates a net carbon-neutral cloud, means that the working partnership between both industry leading teams continues to be a perfect match of brand values; not just from a technology perspective, but also a long-term sustainability and environmental one too.

More information on the partnership can be found here.