Built with BigQuery: How Mercari US turned BigQuery into an ML-powered customer-growth machine

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.