Contact Center AI (CCAI) brings Google’s innovation in conversational AI to solve the most challenging customer service needs while lowering operational costs. More than a thousand customers have deployed CCAI and are steadily turning it on to power their production contact centers.

Today, we’re excited to announce that we’ve made CCAI even stronger with Agent Assist for Chat, now in public preview.

Agent Assist provides your human agents with continuous support during their calls and now chats by identifying the customers’ intent and providing them with real-time recommendations such as articles and FAQs as well as responses to customer messages to more effectively resolve the conversation.

Customers using Agent Assist for Chat have been able to manage up to 28% more conversations concurrently, while also driving up customer satisfaction by 10%. Additionally, we’ve seen them respond up to 15% faster to chats, reducing chat abandonment rates and solving more customer problems.

Agent Assist provides two key components to help agents manage conversations better: 

• Smart Reply provides response suggestions to agents so they can quickly and appropriately respond to customer messages. These suggestions can be taken from your top performing agents as well as modified even further to ensure suggestions properly reflect the tone and voice of your brand. Agent Assist learns when and what recommendations to make by building a custom model that’s trained on your (and only your) data.
• Knowledge Assist unlocks the power of your knowledge base to provide articles and FAQ suggestions to agents in real-time as the conversation progresses. When using Knowledge Assist, agents no longer need to make the customer wait while they navigate multiple applications and data to find the resolution to the customer’s issue — the answer is delivered right to them.  

“We’ve been very impressed by the chat capabilities of Agent Assist,” said Chris Smith, Vice President of Digital Service at Optus, one of the largest telecommunications companies in Australia

Optus has been using CCAI Dialogflow CX to send queries to virtual agents and sees great potential to use Agent Assist to provide recommendations to their customer support representatives. They expect Agent Assist to help minimize repetitive tasks by providing response and typeahead suggestions, helping improve the efficiency of their agents and the quality and consistency of service they provide.

Another customer, LoveHolidays, is using Agent Assist to support their agents and customers in the travel industry. 

“Agent Assist has been a beneficial aid to agents and our customers alike… It gives us the power to flex our contact center staff levels in hours not weeks,” said Eugene Neale, Director of CX Engineering & Business IT at LoveHolidays

Analysts say online chat is becoming one of the most popular ways to reach out to businesses for customer support. IDC research finds that single-function contact centers worldwide are increasingly rare — in 2020, although phone/voice is still responsible for most interactions (at around 18%); email is responsible for around 13% of interactions, and live chat (without automation) is responsible for around 8% of interactions, according to IDC, Toward the AI-Powered Contact Center, Doc # EUR147017320, December 2020.

Deploying CCAI with Agent Assist for Chat

As part of Google’s Contact Center AI suite, Agent Assist provides a seamless handoff from chats managed by your Dialogflow CX virtual agents. If a conversation or customer requires a live agent, Agent Assist will help your team pick it up quickly and drive it to a satisfying resolution. 

Historically, when managers saw contact center volumes increase they had two choices: allow customers to wait longer to speak to someone (lowering customer satisfaction) or bring on more agents (increasing cost to serve).  Deploying CCAI provides contact center leaders with a third choice: equip agents with tools like, Agent Assist for Chat, to efficiently manage customer interactions while maintaining high quality service.

Global CCAI partners support Agent Assist for Chat

Agent Assist for Chat is a set of public APIs that your engineering team can integrate directly into an agent desktop to control the agent experience from end-to-end. For a more out-of-the-box solution, we have partnered with LivePerson and 247.ai to build Agent Assist directly into their agent desktops.

“Integrating our Conversational Cloud directly with Agent Assist means agents can leverage cutting-edge productivity AI to build even further on the massive ROI of conversational commerce, from reduced agent effort and time-to-respond to increased customer satisfaction and revenue,” said Alex Spinelli, CTO of LivePerson. 

More Agent Assist resources

To learn more, check out the Agent Assist webpage. Give Agent Assist a try by training a model and then testing it using the Agent Assist simulator.

With unemployment application submissions reaching record numbers over the past year, state and local agencies in the United States have faced the challenge of processing unprecedented numbers of claims per week. The digital infrastructure most agencies have in place is unable to handle this volume, resulting in constituents waiting longer, and bad actors taking advantage of vulnerable systems. The Department of Labor Inspector General estimates that $63 billion in claims distributed is either an improper payment or fraud. 

Validating claims also requires secure data sharing with other agencies for document and identity verification. Government leaders need a way to allow case adjudicators to quickly and confidently release backlogged claims, integrate with existing systems, and segment legitimate claims from potentially fraudulent ones — all within limited government budgets — securely and at scale.

Implementing a fraud detection solution on Google Cloud

States were under pressure to release payments, while also filtering out potentially fraudulent claims. SpringML and Google Cloud developed a framework to give adjudicators a reliable verification process that quickly filters potentially fraudulent claims, while processing the remaining claims so benefits reach citizens in a timely manner. SpringML and Google Cloud, applied AI-powered machine learning models to detect anomalous patterns in large datasets. Using Google Cloud tools, SpringML implemented a solution to streamline workflows, improve efficiencies, automate processes and identify potentially fraudulent claims.

SpringML used a variety of Google Cloud products to deliver a fraud detection solution, including:

• Google Cloud Storage to store and manage data
• BigQuery to store tabular data and BigQuery Machine Learning (BQML) to conduct machine learning on that data
• AutoML solutions to build predictive models and risk scoring
• Visualization tools such as Looker and Data Studio to present data and help government leaders make informed decisions.

Implementing machine learning to detect improper payments allows agencies to classify claims as “fraud” or “not fraud” based on the number of flags, as well as prioritize the most urgent claims. Deploying intelligent virtual agents to handle frequently asked questions meant that live agents could focus their time on more challenging cases. 

Even once the pandemic is behind us, there will be bad actors trying to take advantage of overwhelmed or legacy systems. We’ve identified a few best practices for agencies managing enormous case loads and looking to improve improper payment analytics: 

• Move your systems to the cloud. Many on-premises legacy systems can’t update their applications and scale to meet the volume of claims. Moving to a cloud environment enables rapid solution deployment and ingestion of large amounts of data without fear of overloading the system. The cloud scales with you–cost-effectively and securely. 
• Understand patterns in the data. The answer is always in the data — we used deep analysis to help uncover suspicious patterns in large data sets. We implemented unsupervised machine learning to learn behaviors and create configurable rules that adjust to new information that comes into the system. We can uncover patterns that are likely associated with fraud – ones that a human might have missed. 
• Use AI/ML tools to automate your existing systems and teams. These tools enable humans to work smarter and more efficiently. We automate anomaly detection and create dashboards for adjudicators to rapidly process claims. We are enabling the Wisconsin Department of Workforce Development by implementing automatic calculations and processing of recharge amounts, resulting in faster processing times and fewer human errors. Proactive fraud detection and timely calculation of recharge payment allowed DWD to ensure the benefits reached the right individuals.
• Build flexibility into your systems. We discovered that fraud patterns change over time. For instance,flags for fraud during March-May 2020 were vastly different from those we found in June-July 2020. Google Cloud tools make it easy to continually update algorithms to detect patterns and integrate external data sources.

Using Google Cloud tools, we can update digital infrastructure and incorporate machine learning best practices to help organizations efficiently process large volumes of claims and identify high probability fraudulent ones. SpringML provides consulting and implementation services and industry-specific analytics solutions that deliver high-impact business value to accelerate data-driven digital transformation. Learn more about fraud detection and how to improve improper payments analytics by watching our webinar. 

Over the past decade, artificial intelligence has evolved from experimental prototypes and early successes to mainstream enterprise use. And the recent advancements in generative AI have begun to change the way we create, connect, and collaborate. As Google CEO Sundar Pichai said in his keynote, every business and organization is thinking about how to drive transformation. That’s why we’re focused on making it easy and scalable for others to innovate with AI.

In March, we announced exciting new products that infuse generative AI into our Google Cloud offerings, empowering developers to responsibly build with enterprise-level safety, security, and privacy. They include Gen App Builder, which lets developers quickly and easily create generative chat and enterprise search applications, and Generative AI support in Vertex AI, which expands our machine learning development platform with access to foundation models from Google and others to quickly build, customize and deploy models. We also introduced our vision for Google Workspace, and delivered generative AI features to trusted testers in Gmail and Google Docs that help people write.

Last month we introduced Security AI Workbench, an industry-first extensible platform powered by our new LLM security model Sec-PaLM, which incorporates Google’s unique visibility into the evolving threat landscape and is fine-tuned for cybersecurity operations.

Today at Google I/O, we are excited to share the next steps not only in our own AI journey, but also those of our customers and partners as well. We’ve already seen a number of organizations begin to develop with and deploy our generative AI offerings. These organizations have been able to move their ideas from experimentation to enterprise-ready applications with the training models, security, compute infrastructure, and cost controls needed to provide their customers with transformative experiences. Our open ecosystem, which provides opportunities for every kind of partner, continues to grow as well. And we are also pleased to share new services and capabilities across Google Cloud and Workspace, including Duet AI—our AI-powered collaborator—to enable more users and developers to start seeing the impact AI can have on their organization.

Customers bringing ideas to life with generative AI

Leading companies in a variety of industries like eDreams ODIGEO, GitLab, Oxbotica, and more, are using our generative AI technologies to create engaging content, synthesize and organize information, automate business processes, and build amazing customer experiences. A few examples we showcased today include:

• Adore Me, a New York-based intimate apparel brand, is creating production-worthy copy with generative AI features in Docs and Gmail. This is accelerating projects and processes in ways that even surprised the company.
• Canva, the visual communication platform, uses Google Cloud’s rich generative AI capabilities in language translation to better support its non-English speaking users. Users can now easily translate presentations, posters, social media posts, and more into over a hundred languages. The company is also testing ways that Google’s PaLM technology can turn short video clips into longer, more compelling stories. The result will be a more seamless design experience while growing the Canva brand.
• Character.AI, a leading conversational AI platform, selected Google Cloud as its preferred cloud infrastructure provider because we offer the speed, security and flexibility required to meet the needs of its rapidly growing community of creators. We are enabling Character.AI to train and infer LLMs faster and more efficiently, and enhancing the customer experience by inspiring imagination, discovery, and understanding. 
• Deutsche Bank is testing Google’s generative AI and large language models (LLMs) at scale to provide new insights to financial analysts, driving operational efficiencies and execution velocity. There is an opportunity to significantly reduce the time it takes to perform banking operations and financial analysts’ tasks, empowering employees by increasing their productivity while helping to safeguard customer data privacy, data integrity, and system security.
• Instacart is always looking for opportunities to adopt the latest technological innovations, and by joining the Workspace Labs program, they have access to the new features and can discover how generative AI will make an impact for their teams.
• Orange is exploring a next-generation contact center with Google Cloud. With customers in 26 countries, the global telecommunications firm is testing generative AI to transcribe the call, summarize the exchange between the customer and service representatives, and suggest possible follow up actions to the agent based on the discussion. This experiment has the potential to dramatically improve both the efficiency and quality of customer interactions. Orange is working closely with Google to help ensure data protection and make sure that systematic employee review of Generative AI output and transparency can be implemented.
• Replit is developing a collaborative software development platform powered by AI. Developers using Replit’s Ghostwriter coding AI already have 30% of their code written by generative AI today. With real-time debugging of the code output and context awareness of the program’s files, Ghostwriter frees up developers’ time for more challenging and creative aspects of programming.
• Uber is creating generative AI for customer-service chatbots and agent assist capabilities, which handle a range of common service issues with human-like interactions with the aim of achieving greater customer satisfaction and cost efficiency. Additionally, Uber is working on using our synthetic data systems (a technique for improving the quality of LLMs) in areas like product development, fraud detection, and employee productivity.

Wendy’s is working with Google Cloud on a groundbreaking AI solution, Wendy’s FreshAI, designed to revolutionize the quick service restaurant industry. The technology is transforming Wendy’s drive-thru food ordering experience with Google Cloud’s generative AI and LLMs—with the ability to discern the billions of possible order combinations on the Wendy’s menu. In June, Wendy’s plans to launch its first pilot of the technology in a Columbus, Ohio-area restaurant, before expanding to more drive-thru locations.

Partnering creates a strong ecosystem of real-world options for customers

At Google Cloud, we are dedicated to being the most open hyperscale cloud provider, and that includes our AI ecosystem. Today, we are excited to expand upon the partnerships announced earlier this year for every layer of the AI stack—chipmakers, companies building foundation models and AI platforms, technology partners enabling companies to develop and deploy machine learning (ML) models, app-builders solving customer use cases with generative AI, and global services and consulting firms that help enterprise customers implement all of this technology at scale.

We announced new or expanded partnerships with SaaS companies like Box, Dialpad, Jasper, Salesforce, and UKG; and consultancies including Accenture, BCG, Cognizant, Deloitte, and KPMG. Together with our previous announcements with companies like AI21 Labs, Aible, Anthropic, Anyscale, Bending Spoons, Cohere, Faraday,  Glean, Gretel, Labelbox, Midjourney, Osmo, Replit, Snorkel AI, Tabnine, Weights & Biases, and many more, they provide the a wide range of options for businesses and governments looking to bring generative AI into their organizations.

Introducing new generative AI capabilities for Google Cloud

To help cloud users of all skill levels solve their everyday work challenges, we’re excited to announce Duet AI for Google Cloud, a new generative AI-powered collaborator. Duet AI serves as your expert pair programmer and assists cloud users with contextual code completion, offering suggestions tuned to your code base, generating entire functions in real-time, and assisting you with code reviews and inspections. It can fundamentally transform the way cloud users of all skill sets build new experiences and is embedded across Google Cloud interfaces—within the integrated development environment (IDE), Google Cloud Console, and even chat. 

For developers looking to create generative AI applications more simply and efficiently, we are also introducing new foundation models and capabilities across our Google Cloud AI products. And to continue to enable and inspire more customers and partners, we are opening up generative AI support in Vertex AI and expanding access to many of these new innovations to more organizations.

• New foundation models are now available in Vertex AI. Codey, our code generation foundation model, helps accelerate software development with code generation, code completion, and code chat. Imagen, our text-to-image foundation model, lets customers generate and customize studio-grade images. And Chirp, our state-of-the-art speech model, allows customers to more deeply engage with their customers and constituents inclusively in their native languages with captioning and voice assistance. They can each be accessed via APIs, tuned through our intuitive Generative AI Studio, and feature enterprise-grade security and reliability, including encryption, access control, content moderation, and recitation capabilities that let organizations see the sources behind model outputs. 
• Text Embeddings API is a new API endpoint that lets developers build recommendation engines, classifiers, question-answering systems, similarity matching, and other sophisticated applications based on semantic understanding of text or images. 
• Reinforcement Learning from Human Feedback (RLHF) allows organizations to incorporate human feedback to deeply customize and improve model performance. 

Underpinning all of these innovations is our AI-optimized infrastructure. We provide the widest choice of compute options among leading cloud providers and are excited to continue to build them out with the introduction of new A3 Virtual Machines based on NVIDIA’s H100 GPU. These VMs, alongside the recently announced G2 VMs, offer a comprehensive range of GPU power for training and serving AI models.

Extending generative AI across Google Workspace 

Earlier this year, we shared our vision for bringing generative AI to Workspace, and gave many users early access to features that helped them write in Gmail and Google Docs. Today, we are excited to announce Duet AI for Google Workspace, which brings together our powerful generative AI features and lets users collaborate with AI so they can get more done every day. We’re delivering the following features to trusted testers via Workspace Labs: 

• In Gmail, we’re adding the ability to draft responses that consider the context of your existing email thread—and making the experience available on mobile.
• In Google Slides and Meet, we’re enabling you to easily generate images from text descriptions. Custom images in slides can help bring your story to life, and in Meet they can be used to create custom backgrounds.
• In Google Sheets, we’re automating data classification and the creation of custom plans—helping you analyze and organize data faster than ever. 

Moving the industry forward, responsibly

Customers continue to amaze us with their ideas and creativity, and we look forward to continuing to help them discover their own paths forward with generative AI. While the potential for impact on business is great, we remain committed to taking a responsible approach, guided by our AI Principles. As we gather more feedback from our customers and users, we will continue to bring new innovations to market, with a goal to enable organizations of every size and industry to increase efficiency, connect with customers in new ways, and unlock entirely new revenue streams.

For over two decades, Google has helped lead the invention of modern cloud systems—defining, designing and deploying warehouse-scale computing as the foundation for reliable, performant, and secure global-scale information services delivered to billions of users around the world. This leadership involves significant innovation across a broad range of systems technologies, including distributed systems, storage systems, databases, analytics, operating systems, wide area and data center networking, cluster computing, ML, video acceleration and more.

Today, we are announcing a significant step in continuing Google’s tradition of innovation and charting its path into the future: the formation of SystemsResearch@Google (SRG). SRG will be a new research team, positioned in the heart of Google’s Cloud and Infrastructure engineering organization, with the mission of shaping the future of hyperscaler systems design for Google and its ecosystem. It is focused on inventing, incubating, and infusing new concepts, designs, and technologies into Google’s applications, systems, and data centers. The team’s position will allow seamless engagement with engineering and product teams, enabling joint exploration in concert with transformative workloads. Beyond Google, the SRG team will look to forge strong relationships with external research communities working on the most pressing systems-research problems.

Critical research at a pivotal time

We are at a time of enormous transition and opportunity, as nearly all large-scale computing is moving to cloud infrastructure, classical technology trends are hitting limits, new programming paradigms and usage patterns are taking hold, and most levels of systems design are being restructured. We are seeing wholesale change with the introduction of new applications around ML training and real-time inference to massive-scale data analytics and processing workloads fed by globally connected edge and cellular devices. This is all happening while the performance and efficiency gains we’ve relied on for decades are slowing dramatically from generation to generation. And while reliability is more important than ever as we deploy societally-critical infrastructure, we are challenged by increasing hardware entropy as underlying components approach angstrom scale manufacturing processes and trillions of transistors.

In the last twenty years, much of the world’s population has gained real-time access to the world’s information and to one another in ways that were previously the stuff of science fiction. The next decade will see computing and associated capabilities undergo an even more profound transformation, bringing real-time insights, sensing, and actuation to trillions of network-connected devices spanning all of the world’s population. Doing so will require fundamental advances in security, reliability, programming models, data analysis, systems for machine learning, networking, storage systems, hardware architecture, and software systems.

SystemsResearch@Google will be co-led by David Culler and Hank Levy, who bring a combination of academic and industrial experience, plus a long history of successful and impactful research in computer systems. Culler is the former Chair of EECS at UC Berkeley, where he worked to create the Division of Data Sciences and became its founding Dean. His research has focused on parallel architectures, clusters, embedded wireless networks, planetary-scale internet services, and sustainability design. He was the founding faculty director of Intel Research Berkeley, co-founded two startups, and worked with Sun Microsystems for a decade. Levy is the former Chair of Computer Science & Engineering at University of Washington, where he worked to create the Paul G. Allen School and became its founding Director. His research has focused on operating systems, distributed systems, computer architecture, and hardware multithreading. Before UW, Levy spent a decade at Digital Equipment Corporation (DEC), where he worked on operating systems and early-generation clustered computer systems; he has also co-founded two startups. Culler and Levy are both Members of the National Academy of Engineering and Fellows of the IEEE and the ACM.

SRG will be located across sites in Google’s Bay Area and Seattle facilities. We are currently building the SRG team, bringing together leading networked systems thinkers from around the world and inside Google. If you are interested in learning more please reach out to us at systemsresearch@google.com.

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}