The Future of Retail: Automated Customer Journeys Powered by Technology
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Editor’s note: To kick off the new year and in preparation for NRF The Big Show, we invited partners from across our retail ecosystem to share stories, best practices, and tips and tricks on how they are helping retailers transform during a time that continues to see tremendous change. Please enjoy this entry from our partner.
If you put a pot of water on the stove, it doesn’t heat up instantly. It simmers slowly at first, eventually picking up steam to reach a rolling boil. The retail landscape is not so different. Capgemini’s research shows the sector has evolved over four generations, from the early days of fragmented outlets to omnichannel and customer-centric focuses, with a fifth generation on the horizon that promises to be centered on consumption.
- Generation 1: Fragmented outlets
- Generation 2: Chain concentration
- Generation 3: Omnichannel
- Generation 4: Consumer-centric
- Generation 5: Consumption-centric
Although incremental change allows companies to experiment and iterate during their digital journeys, the COVID-19 pandemic rapidly accelerated the evolution of online and contactless shopping. Evolution became a revolution, with most Consumer Product and Retail (CPR) companies still mastering the omnichannel generation of their digital transformation to create a seamless shopping experience. Companies that are more digitally mature are already aspiring to the consumer-centric phase, embracing opportunities made possible by technology such as personalization and automation.
What consumers want
Customers have more choices in how they shop and engage with brands. This has made it harder for brands to predict and anticipate needs across customer journeys. And that’s convincing some companies to innovate more quickly as consumer demand drives the need for speed and scale.
Think about your own online behavior. Say you’re shopping for an item and searching online for the closest store in your neighborhood. Google is likely your go-to for finding that information. Looking to troubleshoot an issue with a product or seek out a service? Again, you’ll likely hit up Google first, not even considering going directly to a brand’s website for answers.
Both scenarios point to a disconnect between virtual and physical worlds, a gap technology can bridge in numerous ways such as breaking down silos and integrating fragmented media channels. Interestingly, though, not everything will be centered online all the time. In our recent study on consumer behavior, The great consumer reset, Capgemini discovered 57 percent of shoppers plan to return to brick-and-mortar stores post-pandemic, which is basically unchanged from the 59 percent who often interacted with physical stores before.
But business as usual? Not even close. Consumers have come to expect a frictionless shopping experience (buy online, pick up in store) or an immersive one (products displayed online using augmented reality), and are not content to return to in-store lineups, empty shelves, or a one-size-fits-all approach. Moreover, customers want personalized interactions while ensuring their data and privacy are protected.
So the role of the store is changing. In fact, many online-only brands are opening brick-and-mortar establishments to drive customer experience. In our research on “smart stores,” we found the majority of consumers (66 percent) believe automation can improve their shopping experience by solving the challenges they face at retail stores.
From personalization to serendipity
Retailers must recognize that they have to win consumer trust and confidence. Many consumers believe retailers’ use of tech is focused on reducing costs rather than easing friction. And they’re right. That same Capgemini research found that only one-third (35 percent) of retailers consider “solving customer pain points” as the most important criteria when deciding which automation use cases to implement.
“Retailers are largely in the early stages of adopting automation, and that’s an opportunity to rethink how they’re using technology, not just to smooth out friction and engender consumer trust but to build unexpected consumer benefits,” says Neerav Vyas, Head of Customer First, Co-Chief Innovation Officer, Insights & Data, North America, Capgemini. “We’re trying to move towards this idea of delivering serendipitous experiences to bridge the physical and digital divide.”
The focus is not solely on shoppers seeking out a specific product. “When consumers are in an exploratory mood, retailers can recommend products and services customers didn’t even know they wanted,” says Vyas. For example, business teams that use personalization platforms as part of an integrated media strategy can optimize algorithms against outcomes such as improving conversion and driving engagement.
Vyas says the elevated experience from “personalization to serendipity” fosters trust in the ability of recommendation architectures to persuade and influence consumers’ choices in beneficial ways. A case in point: our research found that half (52 percent) of spending by millennials goes towards experience-related purchases. As always, the key is to meet consumers where they are. Even better, according to Vyas, is to anticipate and understand when signals like customer intent are changing.
How to create value throughout the customer journey
One solution companies can implement right now is an integrated media spend platform that incorporates reporting, planning, and strategy across the entire customer journey. This offers value throughout the customer journey by using technology to reduce friction along the way. Think of it as starting with the customer looking for a product (search and discovery), moving on to the purchase (omnichannel basket, “shoppable” screens) and pick up/delivery (QR code scan in store), and through to post-purchase engagement with the retailer (Google Contact Center AI).
Such a holistic approach also accelerates data acquisition, integration, and reporting using advanced analytics to break down silos and emphasize the importance of privacy and first-party data. This in turn guides end-to-end interventions across customer journeys that enable optimized media spend, empowers businesses to analyze their spend distribution, fine-tunes owned and paid tactics with agencies, and promotes stewardship to support audit efficacy.
A culture of experimentation
With this data-driven focus, CPRs can create a 360-degree perspective of the customer. That intelligence can be used to enhance and humanize automated shopping experiences by putting the customer in control, whether online, in store, or across company brands. Moreover, by using Google Cloud’s emerging technologies such as artificial intelligence (AI), we help companies accelerate value across the spectrum, from supply-chain optimization and customer innovation to consumer experience.
Building a culture of experimentation is a team effort. “It’s not ever just one person who had a big idea. It’s all incremental steps,” says Jennifer Marchand, Google Cloud COE Leader, Capgemini. “Finding the right use case and timing is everything.” Take Google Glass Enterprise, she continues. For greater consumer experience, it can enable in-store associates to better serve with hands-free checkout, customer personalization and recommendations, and special offers. At the same time, Computer Vision and Smart Shelves can help prioritize tasks for employees, notifying them of low stock or a spill in the store.
Marchand points out that companies and consumers alike might not be ready to fully adopt some technology like facial recognition, but since almost everyone has a smartphone these days, they can benefit from automation with ease. “What’s interesting,” she adds, “is the way Google thinks about these types of problems, solving them for the long term.”
The store of tomorrow
Imagine a truly frictionless shopping experience, where state-of-the-art computer vision and AI identifies the products you pick up, put back, and keep, allowing you to head home, completely bypassing the checkout, with a 99 percent accuracy rate and receipts sent directly to your mobile app. That utopian experience is already taking shape at CornerShop, Capgemini’s live experimental store in London, UK.
Jamie MacLoud, Transformation & Strategy Consultant at frog, part of Capgemini, describes the retail space, which runs on Google Cloud, as the store of tomorrow, and not the distant future. It’s an experiential space where retailers and brands can explore, develop, and test technological shopping innovations in real-time. The outcome is a clearer understanding of how digital innovation can enable new ways to progress the customer experience, improve in-store operations, and help consumers to rediscover the joy of in-person retail through new ways to shop and engage with brands.
“We build, test, and learn about store concepts of tomorrow that we believe could be implemented into actual stores in the next one to two years. Getting these experiences in front of real customers in the CornerShop allows us to generate tangible learnings that we can share with our clients and use to shape future store strategy” says MacLeod. CornerShop was opened to the public in two eight-week stints, which allowed real-time testing to see what technologies resonated with customers, which brands can adapt and scale. Frictionless checkout, not surprisingly, was a big win for customers, but the technology underpinning the “virtual try-on” of clothing was deemed more suitable for the store of the future.
So, unlike an innovation lab, CornerShop lets companies experiment risk free, speeding up the process from hypothesis to full-scale implementation. It’s also another step toward solving the challenges customers face, while delivering those serendipitous experiences that build brand loyalty and longevity.
Learn more about how Capgemini is partnering with Google Cloud to help retailers create next-generation shopping experiences today.
We would like to acknowledge Jamie MacLoud, Transformation & Strategy Consultant at frog, part of Capgemini and Neerav Vyas, Head of Customer First, Co-Chief Innovation Officer, Insights & Data, North America at Capgemini who supported with invaluable insights and subject matter expertise in the writing of this blog post.
APAC’s Retail Digital Pulse by Google Cloud and IDC Retail Index
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In the post COVID-19 pandemic world, some retail businesses have aced digital transformation while others are still lagging behind. The Google Cloud commissioned IDC Retail Insights analyzed over 1, 108 retailers across seven nations in the Asia Pacific region and across eight different segments (online, drugstores, speciality shops, convenience stores, department stores, restaurants, supermarkets and hypermarkets/big box stores) to develop Google Cloud Retail Digital Index. The digital maturity was reviewed in five dimensions that define the digital pulse like strategy, people, data, technology and process.
Apart from empowering retail brands to live up to customer expectations, technology is the backbone to back-office operations and still ranks low in the digital pulse. Download the Google-ODC InfoBrief to catch up on the latest, detailed insights on Asia Pacific’s Retail Digital Index along with essential guidance on driving digital transformation, use cases and more!
Insurer Uses Google Cloud AI to Battle Slow Growth: It Improves Sales by 5% in 8 Weeks
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For a business to succeed in the long term, it needs to learn not just to adapt to inevitable change, but to harness it. South Africa-based PPS has been an insurance company since 1941 and today is the biggest mutual insurance provider in the country.
As a mutual company, PPS is owned by more than 200,000 members, making them shareholders. In recent years, PPS and other companies like it have been affected by a number of external factors.
“For one thing, technology platforms have brought in a new gig economy that has all kinds of implications for insurance,” says Avsharn Bachoo, CTO at PPS. “What we’ve been seeing is basically a disruption of the South African insurance industry. We chose to see that as an opportunity.”
“Our servers were at the end of their life cycle and we had to decide whether to refresh them or switch completely. To embrace the world of AI and machine learning effectively, we knew we needed a cloud-based infrastructure. We’ve found the answer in Google Cloud Platform.”
—Avsharn Bachoo, CTO, PPS
In early 2018, faced with an uncertain economic environment that was squeezing growth and profitability, PPS decided to transform itself from a traditional broker-based business into a digital insurance provider. A key pillar of this new strategy was to overhaul the company’s technology infrastructure. To turn the strategy into reality, Avsharn and his team chose Google Cloud Platform (GCP).
“Our servers were at the end of their life cycle and we had to decide whether to refresh them or switch completely,” says Avsharn. “To embrace the world of AI and machine learning (ML) effectively, we knew we needed a cloud-based infrastructure. We’ve found the answer in Google Cloud Platform.”
Power, speed, flexibility with Google Cloud Platform
Previously, PPS maintained an on-premises IT infrastructure, which worked for its traditional business but was unsuited for its new way of working. In early 2018, the company started working on new products for its members but this required large amounts of compute power that proved prohibitively expensive with on-premises servers. Even existing products were starting to require more than the infrastructure could deliver. Aging equipment meant that it’s testing and quality assurance environments bore little resemblance to the actual production environment.
“We had no pre-production environments at all,” says Avsharn, resulting in more work for developers after products had been released. Meanwhile, the capital required to buy and configure more servers for new projects meant fewer resources available for innovation, and left the company less able to react to changes in the market. PPS knew it had to find a cloud-based alternative.
Shortly after devising a new digital strategy, PPS engineers attended a training session on cloud infrastructure given by leading South African Google Cloud Partner Siatik. Impressed with the presentation, PPS engaged Siatik to help run a proof of concept for a cloud-based infrastructure, running on GCP. With on-site engineers and constant communication, Siatik formed a very close working relationship with PPS. “The team at Siatik was exemplary,” recalls Avsharn. “They were well-organized, with cutting-edge technical acumen and very creative solutions to our problems. They were real game-changers.”
“We wanted the platform to retrain its models in response to new data and improve its recommendations with more information. Normally this would be a manual process but Google Cloud ML Engine lets the models do this automatically.”
—Kimoon Kim, Lead Solution Architect and Data Engineer, Siatik
The proof of concept was successful, with GCP outperforming the existing infrastructure in terms of how it handled compute demands, databases, and storage.
“It’s the speed of GCP that really impresses us,” says Avsharn. PPS saw that GCP wasn’t just an opportunity to migrate its existing infrastructure to the cloud. With Siatik’s help, it redesigned its monolithic core architecture to one based around microservices using Google Kubernetes Engine (GKE). For data processing and storage, Cloud Dataflow and Cloud Datastore proved invaluable, while Stackdriver helped the IT team stay on top of logging and monitoring the system.
“Google Cloud makes migrations very easy,” says Brett St. Clair, CEO at Siatik. “It takes care of all the hard work with configurations and replications, so when we switch the machines on, everything is ready and working.”
The ease with which PPS migrated to GCP means that it can now tackle strategic goals much more quickly than before. The most ambitious of these is an AI-powered product recommendation platform. Information is collected from customers who opt in at a defined point in their journey, this database is queried using BigQuery, and the information is fed into the platform. The AI model then calculates the most appropriate products for each member, according to their personal history.
“Most of the product recommendation engines out there are based on clustering, where you’re offered products based on your peer groups,” explains Avsharn. “For the first time, we can make recommendations to members based on their individual preferences and historical behavior. That’s really powerful for us.”
Siatik helped PPS use TensorFlow and Cloud Machine Learning Engine to build the AI platform. For the engineers, these easy-to-use tools helped speed up the process considerably, allowing them to host the models locally without any fuss. Previously, it took one to three months to manually build the model and match an offer to a customer. With the AI platform, a match takes just a few minutes. Cloud ML Engine, in particular, helped the platform adapt to new information on the fly and easily make adjustments to its hyperparameters, that is, preset variables which define the model-training process.
“We wanted the platform to retrain its models in response to new data and improve its recommendations with more information,” says Kimoon Kim, Lead Solution Architect and Data Engineer at Siatik. “Normally this would be a manual process but Google Cloud ML Engine lets the models do this automatically.”
“Google Cloud helped us cancel out a lot of the noise around machine learning and AI. We don’t have to build new complicated algorithms or hire huge teams of data scientists to benefit. We just bring our data and use the right tools to focus on what’s really important.”
—Avsharn Bachoo, CTO, PPS
Harnessing artificial intelligence for real-world results
PPS deployed its new AI recommendation platform in December, 2018. Just a couple of months later, its impact was clear. “In around eight weeks, we saw a 5 percent growth in sales,” says Avsharn. “It’s been a direct result of building our recommendation platform with Google Cloud. We can offer the right products to the right members.”
For developers and engineers at PPS, working with Google Cloud gives them access to high performance technology and automation options with GKE. As a result, the infrastructure runs 70 percent faster than before with fewer cores and less memory. Developers can also work in mature testing environments, and for the first time, are able to build pre-production environments, leading to better quality products. More strategically, moving to a serverless, cloud-based infrastructure has helped PPS take control of its budget, moving away from intermittent, large capital spends to more manageable, project-to-project flows of operational expenditure. The company expects to see savings of around 50 percent, or $695,000.
“We have a lot more flexibility with our resources thanks to Google Cloud,” says Avsharn. “When we have a new idea, we don’t have to outlay new capital such as servers before we can even start working on it. We just spin up instances when we want and spin them back down when we’re done.”
With the AI platform deployed and working well, PPS is already looking at ways to improve it, including real-time updates and further automation. Soon, the company will integrate the platform with more sales campaigns for more effective targeting to boost sales even further. Meanwhile, it’s also experimenting with machine learning to spot patterns in data at scale for fraud analytics and risk assessment.
For PPS, working with Google Cloud has helped it transform quickly and effectively from disrupted to disruptor. The company is now looking to gain the same transformative effects by implementing G Suite for increased productivity and collaboration.
“Google Cloud helped us cancel out a lot of the noise around machine learning and AI,” says Avsharn. “We don’t have to build new complicated algorithms or hire huge teams of data scientists to benefit. We just bring our data and use the right tools to focus on what’s really important.”
Macy’s Uses Google Cloud to Streamline Retail Operations
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As retailers strive to meet the growing expectations of shoppers, they are turning to Google Cloud to transform their businesses and tackle opportunities in an increasingly challenging industry. From optimizing inventory management to increasing collaboration between employees across locations and roles, to helping build omnichannel experiences for their customers, we are working together with retailers to help make the shopping experience as seamless and personalized as possible.
A standout Google Cloud customer is Macy’s, one of the world’s largest retailers. Founded in 1858, Macy’s operates approximately 680 Macy’s and Bloomingdale’s, and 190 specialty stores including Bloomingdale’s The Outlet, Bluemercury and Macy’s Backstage. And through macys.com, bloomingdales.com, and bluemercury.com, it also serves millions of customers across more than 100 countries.
By moving its infrastructure to the cloud, and taking advantage of Google Cloud data warehousing and analytics solutions, Macy’s is streamlining retail operational functions across its network.
With the opening of its new approximately 675,000 square-foot distribution center in Columbus, Ohio, Macy’s is leveraging the scalability of Google Cloud to ensure that merchandise is accurately and efficiently received, sorted, ticketed, picked, packed and shipped from the distribution center to the stores—even during peak retail seasons like back to school and the holidays.
“Powered by software developed at Macy’s technology, this new distribution center is a fantastic first step in our cloud journey. Working with Google Cloud allows us to be more nimble, efficient and flexible in how we utilize our warehouses,”
Naveen Krishna, CTO, Macy’s
Leveraging Google Cloud’s data management and analytics solutions, Macy’s new warehouse management system will initially service 200+ Macy’s Backstage off-price stores at launch. Macy’s will begin rolling out this software solution to additional distribution centers that service its nationwide fleet of Macy’s and Bloomingdale’s department stores, as well as Macys.com and Bloomingdales.com direct-to-customer orders.
Our continued work with Macy’s reflects their investment in technology to improve digital and mobile experiences, site stability, store technology, fulfillment, and logistics, and integrate its front line and back office to reinvent retail. I look forward to deepening our partnership with Naveen and his team to help them achieve these goals.
Italian Utility Company Deploys its SAP Workloads on Google Cloud to Meet Sustainability Goals
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With more than 2.5 million customers, Italian utility company A2A is committed to delivering electricity, gas, clean water, and waste collection every day. More recently, the company made another significant commitment: To incorporate the principles of the “circular economy” into its way of doing business — part of the UN 2030 Agenda’s Sustainable-Development Goals — all while also aiming to double its client base by 2030. Growing rapidly but sustainably requires operating as efficiently as possible at every level of the organization, from operating smart meters to generating accurate demand projections. That’s why A2A chose to deploy its SAP S/4HANA ERP and the SAP BW/4HANA data warehouse on Google Cloud.
Roadblocks to innovation
Instead of a linear consumption model that starts with raw materials and ends with use and disposal, the circular economy is a continuous cycle that emphasizes repair, recycling, and the creation of materials rather than their disposal. To take an example from A2A’s own success story: The company keeps 99.7 percent of collected waste out of landfills.1 Of the UN’s sustainability goals, A2A is committing to the three most relevant to its industries:
- Ensuring availability and sustainable management of water and sanitation for all
- Ensuring sustainable consumption and production patterns
- Protecting, restoring, and promoting sustainable use of terrestrial ecosystems
Achieving A2A’s sustainability and customer-first strategies requires high scalability, rapid data ingestion, and rich, accurate analytics. None of this could be reliably supported with the company’s legacy on-premises SAP and Data Warehouse, especially given A2A’s projected growth and the increasing complexity of the data landscape, including IoT deployments and energy market liberalization.
Provisioning data infrastructure was also slow and complex. Simply adding a new metric could require increasing capacity by an order of magnitude. And analytical and transactional data lived in siloes, which created a fragmented and out-of-date view of each customer across sales and customer support teams. A2A’s fragmented data also made it difficult to take proactive action when changing priorities or processes required shifting focus from one data source to another.
With a data warehouse that refreshed only once every 24 hours, simple processes such as responding to a customer calling because their power has been cut off due to an unpaid bill became cumbersome.
Scalability was also a concern. With the on-premises solution, A2A needed to define the budget for its data warehouse over a two-year timeframe, but the rollout of new electricity meters — each sending data every 10 minutes — across Italy made those data requirements hard to predict.
The move to the cloud: From monolith to microservices
The move has been a giant step forward in A2A’s goal of meeting its data-driven, customer-centric strategy. In deploying its SAP systems to Google Cloud, A2A can take advantage of a highly flexible hybrid environment and powerful data management and analytics. It can replicate data from Salesforce, SAP, and other systems in BigQuery, which operates as a data lake with Google Cloud SQL, connected directly to Google Analytics and Google Ads for data-driven customer service, decision-making, and marketing.
“From BigQuery we can feed relevant information directly to the people who need it. Our customer operators work on Salesforce, so we use an OData protocol to embed real-time data in that platform. Elsewhere, we present the information through a dashboard, or with a BI component delivering one-page reports.” —Vito Martino, Head of CRM, Marketing and Sales B2C & B2B, A2A
By running SAP on Google Cloud, A2A can also count on an infrastructure platform that provides:
- Scalability. The robust data architecture on Google Cloud adapts to shifting and increasing demands without compromising on speed or availability, so A2A doesn’t have to worry about over- or under-provisioning as the rollout of smart meters proceeds.
- Speed. The new A2A data solution refreshes every five minutes instead of 24 hours, so the company can respond to its customers’ needs without delays. Customer operators working in Salesforce now receive real-time data from Google BigQuery so that, when a customer calls, operators can see accurate information in seconds. They can now offer value-added services and sustainable options tailored to the customer’s needs, from energy consumption to their preferred method of communication.
- Availability. With microservices orchestrated by Google Kubernetes Engine, the team can update the solution through continuous integration and delivery (CI/CD), eliminating the need for downtime when changes are required.
- Security and control. The A2A IT team uses Google Kubernetes Engine to orchestrate clusters of instances on Google Compute Engine, with Google Cloud Load Balancing and backups on Google Cloud Persistent Disk. Google Cloud Anthos ensures operational consistency across on-premises and cloud platforms.
Ready to grow the sustainable way
By moving to Google Cloud — the industry’s cleanest cloud, with zero net emissions — A2A is ready to grow quickly while locking down the efficiency it will need to meet its ambitious sustainability goals. “To bring sustainable utilities to market, we need to be both responsive to our customers and responsive to the internal needs of A2A,” explains Davide Rizzo, Head of IT Governance and Strategy at A2A. “Understanding what customers need in detail means we can improve their services and reduce their environmental impact at the same time.”
Learn more about the ways Google Cloud can transform your organization’s SAP solutions with scalability, speed, and advanced analytics capabilities.
1. Circular Economy: one of the four founding pillars of A2A’s 2030 sustainability policy | Drupal
Your DW Need Scaling Up? Try What This Company Did: It Can Run 25,000 Events a Second
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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.”
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The practical benefits of migrating SAP systems to the cloud aren’t lost on most businesses. Running SAP in the cloud lets companies simplify tasks, scale quickly, and reduce costs. But as a growing number of organizations are discovering, the cloud is more than the sum of improved processes and workflows.
Google Cloud’s Professional Service Organization: How it Accelerates Operational Health Review Cloud Migration
Introduction The Google Cloud Professional Services Organization's (PSO) mission is to help our customers get the most out of Google products. PSO is responsible for customer success by sharing our technical expertise in order to unlock business value from the cloud by providing cloud strategy and best practice advice, implementation guidance, and
HSBC Leverages Google Cloud to Deliver Exceptional Cx for 37 Million Customers
HSBC is the world's largest international bank that has been in existence for 150 years. HSBC is present in 70 countries, has 37 million customers and is the number one bank in trade finance or cross-border finance. HSBC is a systemically important financial institution that is heavily regulated. Rapid growth
Google Cloud’s Autism Career Program to Nurture Neurodiverse Talent
My passion for neurodiversity began 10 years ago, when I became involved with Els for Autism, an organization that works with children and adults who have autism, as well as their families. At the time, I had a friend who was struggling to find resources for his son with autism. The
