When Google announced our plan to go beyond purchasing renewable power for 100% of our energy usage and operate on 24/7 carbon-free energy by 2030, we noted that achieving this goal will require new transaction structures, advancements in clean energy policy, and innovative new technologies. Today, we’re pleased to announce that one of these new technologies—a first-of-its-kind, next-generation geothermal project—will soon begin adding carbon-free energy to the electric grid that serves our data centers and infrastructure throughout Nevada, including our Cloud region in Las Vegas.  

Google and clean-energy startup Fervo have just signed the world’s first corporate agreement to develop a next-generation geothermal power project, which will provide an “always-on” carbon-free resource that can reduce our hourly reliance on fossil fuels. In 2022, Fervo will begin adding “firm” geothermal energy to the state’s electric grid system, where Google’s commitments already include one of the world’s largest corporate solar-plus-storage power purchase agreements. 

Importantly, this collaboration also sets the stage for next-generation geothermal to play a role as a firm and flexible carbon-free energy source that can increasingly replace carbon-emitting fossil fuels—especially when aided by policies that expand and improve electricity markets; incentivize deployment of innovative technologies; and increase investments in clean energy research, development, and demonstration (RD&D). 

Next-generation geothermal technology

Traditional geothermal already provides carbon-free baseload energy to a number of power grids. But because of cost and location constraints, it accounts for a very small percentage of global clean energy production. 

That’s one reason this new approach is so exciting; by using advanced drilling, fiber-optic sensing, and analytics techniques, next-generation geothermal can unlock an entirely new class of resource. And the US Department of Energy has found that with advancements in policy, technology, and procurement, geothermal energy could provide up to 120 GW of firm, flexible generation capacity in the US by 2050. 

As part of our agreement, Google is partnering with Fervo to develop AI and machine learning that could boost the productivity of next-generation geothermal and make it more effective at responding to demand, while also filling in the gaps left by variable renewable energy sources. Although this project is still in the early stages, it shows promise.

Using fiber-optic cables inside wells, Fervo can gather real-time data on flow, temperature, and performance of the geothermal resource. This data allows Fervo to identify precisely where the best resources exist, making it possible to control flow at various depths. Coupled with the AI and machine learning development outlined above, these capabilities can increase productivity and unlock flexible geothermal power in a range of new places. 

This won’t be the first time that Google is applying software solutions to clean energy applications: we’ve just announced an update to our carbon-intelligent computing program that helps us reduce emissions associated with running applications at Google data centers. And other forms of AI and machine learning are currently being used to increase the value of wind energy. 

Already this year, Google has taken significant strides toward sourcing 24/7 carbon-free energy for all our data centers, office campuses, and Cloud regions. On Earth Day, our CEO Sundar Pichai announced that for the first time, five of our global data center sites operated near or at 90% carbon-free energy in 2020. 

Not only does this Fervo project bring our data centers in Nevada closer to round-the-clock clean energy, but it also acts as a proof-of-concept to show how firm clean energy sources such as next-generation geothermal could eventually help replace carbon-emitting power sources around the world.

When peer-to-peer marketplace Mercari came to the US in 2014, it had its work cut out for it. Surrounded by market giants like eBay, Craigslist, and Wish, Mercari needed to carve out an approach to compete for new users. Furthermore, Mercari wanted to build a network for buyers and sellers to return to, rather than a site for individual specialty purchases. 

As an online marketplace that connects millions of people across the U.S. to shop and sell items of value no longer being used, Mercari is built for the everyday shopper and casual seller. Two teams, Machine Learning (ML) team and Marketing Technology specialists, both led by Masumi Nakamura, Mercari VP of Engineering, saw an opportunity to supercharge Mercari’s growth in the US by leveraging their first-party data in BigQuery and connecting predictive models built in Google Cloud directly to marketing channels, such as churn predictions and item recommendations for email campaigns, and LTV predictions to optimize paid media. Churn predictions could be used to target marketing communications, and item recommendations could be used to personalize the content of those communications at the user level. By fully utilizing cloud computing services, they could grow sustainably and flexibly, focusing their team’s efforts where they belonged — user understanding and personalized marketing.

In 2018, the Mercari US team engaged GrowthLoop, formerly Flywheel Software, experts in leveraging first-party customer data for business growth. Working exclusively in Google Cloud and BigQuery, GrowthLoop helped Masumi transform Marketing Technology at Mercari in the US.

Use cases: challenges

Masumi and the ML team’s primary goal aimed to reduce churn across buyers and sellers. Customers would make an initial purchase, but repurchase and resale rates were lower than the team hoped for. The ML team, led by Masumi, was confident that if they could get customers to make a second and third purchase, they could drive strong lifetime value (LTV). 

Despite the team’s robust data science capabilities and investments in a data warehouse (BigQuery), they were missing the ability to streamline efforts for efficient audience segmentation and targeting. Like most companies looking to utilize data for marketing, the team at Mercari had to engage with engineering in order to build out customer segments for campaign launches and testing. From start to finish, launching a single campaign could take three months. 

In short, Mercari needed a way to speed up the process across the teams at Mercari. How could they turn the team’s predictions into active marketing experiments with greater velocity and agility?

Solution: BigQuery and GrowthLoop supercharge growth across the customer lifecycle

With their strong data engineering foundation and BigQuery already in place, the Mercari team began addressing their needs step-by-step. First, they used predictions to identify retention features, then built out initial segment definitions based on those features. From there, the team designed and launched experiments and measured their performance, refining as they went. By providing Mercari’s marketing team with the ability to build their own customer lists that leveraged predictive models without requiring continuous support from other teams’ data engineers and business intelligence analysts, GrowthLoop enabled them to address churn and acquisition with a single, self-serve solution.

The dynamic duo: GrowthLoop and BigQuery

• Customer 360: GrowthLoop enabled Mercari to combine their data sources into a single view of their customers in BigQuery, then connected them to marketing and sales channels via GrowthLoop’s platform. Notably, Mercari is able to leverage its own complex data model, which was ideal for a two-sided marketplace. This is shown in the “Collect & Transform” stage in the architecture diagram above.
• Predictive models: GrowthLoop activated predictions that had been snapshotted by Mercari’s team in BigQuery. The Mercari ML team used Jupyter notebooks offering part of Google Vertex AI Workbench to build user churn and customer lifetime value (CLTV) prediction models, then productionized them using Cloud Composer to deploy Airflow DAGs, which wrote the predictions back to BigQuery for targeting, and triggered exports to destination channels using Pub/Sub. This is shown in the “Intelligence” stage of the architecture diagram above.
• Extensible measurement and data visualization: Since GrowthLoop writes all audience data back to BigQuery, the Mercari analytics team can conduct performance analysis on metrics from revenue to retention. They are able to use GrowthLoop’s performance visualization in-app, but they are also able to create custom data visualizations with Looker Studio. This is also shown in the “Intelligence” stage of the architecture diagram.
• Seamless routing and activation: With GrowthLoop’s audience platform connected directly to Customer 360 and the predictive model’s results in BigQuery, the marketing team is able to launch and sync audiences and their personalization attributes across all of Mercari’s major marketing, sales and product channels, such as Braze, Google Ads and other destinations. This is shown in the “Routing” and “Activate” stage of the architecture diagram.

“Being able to measure what you’re doing – that results-based orientation – is key. The thing that I like most about GrowthLoop is that you brought a really fundamental way of thinking which was very feedback-based and open to experimenting but within reason. With other products, that feedback loop isn’t so built in that it’s very easy to get lost.”– Masumi Nakamura, VP of Engineering at Mercari

Predictive modeling puts the burn on churn

In collaboration with GrowthLoop, Mercari began analyzing user data in BigQuery via Vertex AI Workbench to identify patterns across churned customers. The teams evaluated a range of attributes like the customer acquisition channel, categories browsed or purchased from, and whether or not they had any saved searches while shopping. Comparing various models and performance metrics, the teams selected the best model for accurately predicting when a buyer or seller was at risk to churn. For sellers, they evaluated audience members by the time elapsed since their last sale – for buyers, the time since their last purchase. 

These churn prediction scores could then be applied to data pipelines that would feed into GrowthLoop’s audience builder. Audience members with a high likelihood to churn would be segmented into their own group and from there, Mercari could target those users with relevant paid media and email campaigns. 

By partnering with GrowthLoop, Mercari was able to simultaneously bridge the gap between the data and marketing teams – and reduce the time between segmentation and campaign launch from months to just a few days.

“One of the big areas of benefit of working with GrowthLoop was the increased integration of marketing channels such as the CRM, User Acquisition, as well as more traditional marketing channels.” – Masumi Nakamura, VP of Engineering at Mercari

Creating the audience within the audience

Once the team had successfully created a model to predict churn across buyers and sellers, Mercari needed to launch retargeting campaigns to measure their ability to reduce churn. Each of their ongoing experiments features tailored segments along with automatic A/B testing. With analytics and activation all under one roof, the marketing team at Mercari could craft audiences and begin measuring the impact of their targeted campaigns. Since starting their work with GrowthLoop, the Mercari team has created over 120 audiences.

“Our marketing teams are more sophisticated with in-house knowledge, but GrowthLoop provides a more user-friendly way to build audiences for campaigns.” – Masumi Nakamura, VP of Engineering, Mercari

“GrowthLoop brings a very fundamental way of thinking about problems, including experimentation…. The ability to organize experiments and results was key. The number of variables is too high for most people without good organization.”– Masumi Nakamura, VP of Engineering at Mercari 

Making segmentation smarter

Mercari’s first audiences leveraging GrowthLoop were sent to Braze to supercharge email campaigns and coupons with churn predictions and automated campaign performance evaluations. Then, Mercari shifted its focus to Facebook for paid media retargeting, using GrowthLoop’s lifecycle segmentation framework to target customers at the right step in their user journey. Lastly, Mercari moved its focus to Google Ads, where they used GrowthLoop to implement new segmentation models based on product category propensity. Mercari had long used Google Ads for product listing ads, and with GrowthLoop, Mercari was able to define more powerful product propensity segments and measure custom incremental lift metrics.

Finding new users in the haystack

Finally, in addition to preventing churn and driving retention, the Mercari team also wanted to boost user acquisition. They were having trouble measuring performance of UA campaigns due to new iOS and Facebook data privacy restrictions that made measuring campaign attribution impossible for many users. Using the familiar stack of Vertex AI Workbench for analysis, performance analysis on campaign data in BigQuery, and Airflow DAGs deployed via Cloud Composer to productionize the data pipelines, GrowthLoop enabled the team to activate targeted campaigns based on a user’s geographical location. In this way, Mercari could make decisions about their UA campaigns using incrementality analysis between geographic regions rather than attribution data, thus preserving user privacy. 

The Mercari approach to customer data activation and acquisition

Other marketplace retailers can learn from Mercari’s successes activating data from BigQuery with GrowthLoop. Here are a few best practices to apply:

Identify your team’s needs and existing strengths

Mercari knew that their team had built out a strong foundation for data analysis within BigQuery. They also knew that their process was missing a key component that would allow them to activate that data. In order to achieve similar results, work to evaluate the strength of your team and your data – and define exactly what you aim to achieve with customer segmentation.

Partner with the right providers

With BigQuery, the Mercari team had all of their data centralized in one single location, simplifying the process for predictive modeling, segmentation, and activation. By partnering with GrowthLoop, this centralized data could be activated with ease across Mercari’s marketing teams. When evaluating providers for data warehousing, segmentation, and activation, be sure to partner with a provider that ensures you can get the most out of your data.

Know your audience

With a deeper understanding of their customers, Mercari was able to see nearly immediate value. By investing in the proper tools to accurately predict customer behavior, Mercari delivered impact in exactly the right areas. Using the data you’ve already compiled on your customers, consider partnering with a customer segmentation platform provider like GrowthLoop. In fact, Masumi went so far as to organize his Machine Learning team around these concepts: “We split the ML team into two areas – one to augment and work with GrowthLoop, the other team was to augment and orient around item data.”

How to boost growth like Mercari in three steps

Today, many leading brands leverage GrowthLoop and BigQuery to drive marketing and sales wins. Whether your company is in retail, financial services, travel, software, or another industry entirely, you can join the growing number of companies driving sustainable growth through real-time analytics by connecting BigQuery from Google Cloud to GrowthLoop. Here’s how:

If you have customer data in BigQuery…

1. Book a GrowthLoop + BigQuery demo customized to your use cases.
2. Link your BigQuery tables and marketing and sales destinations to the GrowthLoop platform.
3. Launch your first GrowthLoop audience in less than one week.

If you are getting started with BigQuery…

1. Get a Data Strategy Session with a GrowthLoop Solutions Architect at no cost.
2. Use our Quick Start Program to get started with BigQuery in 4 to 8 weeks.
3. Launch your first GrowthLoop audience in less than one week thereafter.

GrowthLoop and Google: Better together

The key question for many marketers today is, “How do you best leverage all you know about your customers to drive more intelligent and effective marketing engagement?” When Mercari set out to answer this question in 2019, they applied an innovative BigQuery data strategy that leveraged machine learning models. However, they achieved remarkable marketing results because they were among the first companies to discover and apply GrowthLoop to enable the marketing team to launch audiences with a first party data platform directly connected to their datasets and predictions in BigQuery. This greatly accelerated the design-launch-measure feedback loop to generate repeatable growth in customer lifetime value.

The Built with BigQuery advantage for ISVs and Data Providers

Google is helping companies like GrowthLoop build innovative applications on Google’s data cloud with simplified access to technology, helpful and dedicated engineering support, and joint go-to-market programs through the Built with BigQuery initiative. Participating companies can: 

• Accelerate product design and architecture through access to designated experts who can provide insight into key use cases, architectural patterns, and best practices. 
• Amplify success with joint marketing programs to drive awareness, generate demand, and increase adoption.

BigQuery gives ISVs the advantage of a powerful, highly scalable data warehouse that’s integrated with Google Cloud’s open, secure, sustainable platform. And with a huge partner ecosystem and support for multi-cloud, open source tools and APIs, Google provides technology companies the portability and extensibility they need to avoid data lock-in.

Click here to learn more about Built with BigQuery.

^{We thank the Mercari, GrowthLoop and Google Cloud team members who collaborated on the blog:
Mercari: Masumi Nakamura, VP of Engineering
GrowthLoop: Julia Parker, Product Marketing Manager; Alex Cuevas, Head of Analytics
Google: Sujit Khasnis, Solutions Architect}

Whether with the cloud or within their own data centers, enterprises have undergone a period of remarkable consolidation and centralization of their compute resources. But with the rise of ever more powerful mobile devices, and increasingly capable cellular networks, application architects are starting to think beyond the confines of the data center, and looking out to the edge. 

What exactly do we mean by edge? Think of the edge as distributed compute happening on a wide variety of non-traditional devices — mobile phones of course, but also equipment sensors in factories, industrial equipment, or even temperature and reaction monitoring in a remote lab. Edge devices are also connected devices, and can communicate back to the mothership over wireless or cellular networks. 

Equipped with increasingly powerful processors, these edge devices are being called upon to perform tasks that have thus far been outside the scope of traditional IT. For enterprises, this could mean pre-processing incoming telemetry in a vehicle, collecting video in kiosks at a mall, gathering quality control data with cameras in a warehouse, or delivering interactive media to retail stores. Enterprises are also relying on edge to ingest data from outposts or devices that have even more intermittent connectivity, e.g., oil rigs or farm equipment, filtering that data to improve quality, reducing it to right-size information load, and processing it in the cloud. New data and models are then pushed back to the edge; in addition, we can also push configuration, software, and media updates and decentralize processing workload.

Edge isn’t all about enabling new use cases – it’s also about right-sizing environments and improving resource utilization. For example, adopting an edge model can also relieve load on existing data centers. 

But while edge computing is full of promise for enterprises, there are many pieces that are still works in progress. Further, developing edge workloads is very different from developing traditional applications, which enjoy the benefits of persistent data connections and run on well-resourced hardware platforms. As such, cloud architects are still in the early days of figuring out how to use and implement edge for their organizations. 

Fortunately, there are tools you can use to help ease the transition to edge computing — and that likely fit into your organization’s existing computing systems. Kubernetes, of course, but also higher level management tools like Anthos, which provides a consistent control plane across cloud, private data center and edge locations. Other parts of the Anthos family – Anthos Config Management and Anthos Service Mesh — go one step further and provide consistent, centralized management to your edge and cloud deployments. And there’s more to come.

For the remainder of this blog post, we’ll dive deeper into the past and current state of edge computing, and the benefits that architects and developers can expect to see from edge computing. In a next post, we’ll take a deeper look at some of the challenges that designing for edge introduces, and some of the advantages the average enterprise has in adopting the edge model. Finally, we’ll look at the Google Cloud tools that are available today to help you build out your edge environment, and look at some early customer examples that highlight what’s possible today — and that will spark your imagination for what to do tomorrow. 

The evolution of edge computing 

The edge is not a new concept. In fact, it’s been around for the last two decades, spanning many use cases that are prevalent today. One of the first applications for edge was to use content delivery networks (CDN) to cache and serve daily static website pages near clients, for example, web servers in California data centers serving financial data to European customers. 

As connectivity has improved and software evolved, the edge has evolved too, and the focus has shifted towards using edge to distribute services. First, simple services expanded from static HTML to javascript libraries or image repositories. Common functions like image transformation, credit and address validation support services followed. Soon, organizations were deploying more complex cloudlet and clustered microservices installations, as well as distributed and replicated datasets. The term “endpoint” became ubiquitous, and APIs profilerated. 

In parallel, there’s been an explosion of creativity in hardware, microcontrollers and dedicated edge devices. Fit-for-purpose products were deployed globally. Services like Google Cloud IoT Core extended our ability to manage and securely connect these dispersed devices, allowing platform managers to register tools and leverage managed services like Pub/Sub and Dataflow for data ingestion. And with Kubernetes, large remote clusters — mini private clouds in and of themselves — operate as self-healing, autoscaling services across the broader internet, opening the door to new models for applications and architectural patterns. In short, both distributed asynchronous systems and economies have blossomed.

What does this mean for enterprises? For the purposes of this series, edge means you can now go beyond the corporate network, beyond cloud VPCs, and beyond hybrid. The modern edge is not sitting at a major remote data center, nor is it a CDN, cloud provider, or in a corporate data center rack — it’s just as likely to look like 100 of these attached to a thousand sensors.

Edge, in short, is about having hardware and devices installed at remote locations that can process and communicate back the information they collect and generate. The edge management challenge, meanwhile, is being able to push configuration and software/model/media updates to these remote locations when they are connected.

Enable new use cases

Today, we have reached a new threshold for edge computing — one where micro-data-processing centers are deployed as the edge of a fractal arm, as it were. Together, they form a broad, geographically distributed, always-on framework for streaming, collecting, processing and serving asynchronous data. This big, loosely coupled application system lives, breathes and grows. Always changing, always learning from the data it collects — and always pushing out updated models when the tendrils are connected. 

Right now, the rise of 5G is pushing the limits of edge even further. Devices enabled with 5G can transmit using a mobile network — no ISP required — enabling connectivity anywhere within reach of a cell tower. Granted, these networks have lower bandwidth, but they are often more than adequate for certain types of data, for example fire sensors in forests bordering remote towns that emit temperature or carbon monoxide data periodically. Recently, Google Cloud partnered with AT&T to enhance business use of 5G edge technology but there is so much more that can be done. 

Reduce data center investments

In addition to enabling the digitization of a broad range of new use cases, adopting edge can also benefit your existing data center.

Let’s face it: data centers are expensive to maintain. Moving some data center load to edge locations can reduce your data center infrastructure investment, as well as compute time spent there. Edge services tend to have much lower service level objectives (SLOs) than data center services, driving lower levels of hardware investment. Edge installations also tend to tolerate disconnectedness, and thus function perfectly well with lower SLOs — and lower costs. 

Let’s look at an example of where edge can really reduce costs: big data. Back in the day, we used to build monolithic serial processors — state machines — that had to keep track of where they were in processing in case of failure. But time and again, we’ve seen that smaller, more distributed processing can break down big, expensive problems into smaller, more cost-effective chunks. 

Starting with the explosion of MapReduce almost 20 years ago, big-data workloads were parallelized across clusters on a network, and state management was simplified with intermediate output to share, wait for, or restart processing from checkpoints. Those monolithic systems were replaced by cheaper, smarter, networked clusters and data repositories where parallel work could be executed and rendered into workable datasets. 

Flash forward to today, and we are seeing those same concepts applied and distributed to edge data-collection points. In this evolution of big data processing, we are scaling up and out to the point where observation data is so massive that it must first be prefiltered, and then preprocessed down to a manageable size and still be actionable. Only then should it be written back to the main data repositories for more resource-intensive processing and model building.

In short, data collection, cleanup, and potentially initial aggregation happens at the edge location, which reduces the amount of junk data sitting in costly data stores. This increases performance of the core data warehouse, and reduces the size and cost of network transfers and storage! 

The edge is a huge opportunity for today’s enterprises. But designing environments that can make effective use of the edge isn’t without its challenges. Stick around for part two of this series, where we look at some of the architectural challenges typically encountered while designing for the edge and how we begin to address them.

Editor’s note: Today’s post is by Mike Francomb, Senior Vice President of Technology, RecruitMilitary and a U.S. Army Veteran. RecruitMilitary is a wholly owned subsidiary of Bradley-Morris, Inc. (BMI), the largest military-focused recruiting company in the United States. RecruitMilitary uses Google Cloud Talent Solution to power its job search experience and connect more organizations with veteran talent.

For seven years, I served in the U.S. Army as a Field Artillery Officer, Military Occupation Code 13A. My time in service included a deployment to Operation Desert Shield / Desert Storm with the 24th Infantry Division out of Fort Stewart, GA, and a variety of front line artillery leadership roles, serving as a logistics officer for my unit and as an instructor teaching new officers how to be professional artillerymen. My day-to-day entailed leading teams of highly trained soldiers and managing logistics and materials to help those soldiers perform at a high level in stressful, fast-paced environments. It was my job to ensure we were ready to handle any circumstance. 

The hardest part about transitioning out of the Army in May 1996 as a highly trained artillery veteran was the fact that, though I felt prepared for any challenge ahead, I wasn’t sure I was making the right choice. I made a common mistake of transitioning veterans, I jumped right into an entrepreneurial venture. Looking back, I wish I’d had access to resources that displayed career options that would help translate my skills for the corporate world, it would have helped me be better prepared and know what my options were. I wasn’t ready to jump from the Army into running a business, and it was a long two years. 

Though my first job out of the Army was challenging, it taught me that I loved the start-up environment, and I joined RecruitMilitary in October 1998 when it was five months old. For the past 21 years, I have been fortunate enough to play an important role in helping RecruitMilitary grow to what it is today, the industry leader in connecting military veterans with organizations.  

RecruitMilitary connects organizations with veteran talent through over 30 products and services, all of which are fueled by our job board. Our job board, with over 1,400,000 members, is core to our business. In fact, if we don’t have an active and growing job board population, we don’t have the supply of veteran talent we need to deliver to our clients across our suite of services. 

With veteran unemployment at a 50-year low, it became increasingly challenging for RecruitMilitary to grow our veteran job seeker database and keep those veterans actively applying to client jobs. Being a data-driven company, we saw our existing search functionality was no longer producing the desired results for clients and began to receive client feedback about decreased candidate activity.

It was clear to us that we needed to begin adopting machine learning and more advanced search capabilities into our products and operations. The HR Tech space is shifting that way fast, and we want to be at the forefront. As we researched paths to take and learned of Google’s operating philosophy leading with AI, and that they were developing a tool for veteran job search, it made a lot of sense to go with a leader.

When Grow with Google announced its commitment to support veterans, we learned that we could add their military occupation code (MOS) translation feature to our job board through Cloud Talent Solution. This feature lets transitioning service members enter their military occupation codes (MOS, AFSC, NEC, or rating) directly into our search bar to see relevant civilian jobs available at client companies. We’re also using Cloud Talent Solution’s remote work functionality to provide an improved job search experience that allows our customers to make remote work opportunities in the U.S. more discoverable on their career sites. We’re excited about this feature, as it enhances our ability to deliver meaningful jobs to important members of our military community, military spouses, and veterans with limited mobility. 

The results of Cloud Talent Solution compared to our previous search are tremendous. Our job seekers are getting a much better experience, and to us that means more veterans are connected to jobs with our clients. We know this because our number of daily job applications has grown by 78 percent. And knowing that we now have a tool that’s going to learn and get better as more of our job seekers use it means that we will reap benefit for work done over time, and so will our clients and veterans who use our job board. That’s tremendous ROI to receive for a lean development staff. 

These are just a few of the types of tools I wish I’d had access to when I was considering my transition in 1996. With the help of technology and resources, like those from RecruitMilitary and Grow with Google, people in the military community, including veterans like myself, can prepare for and build meaningful careers.