
How to Build a BI Dashboard With Google Data Studio and BigQuery
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How TeamSnap Improved Return on Ad Spend Significantly

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Anyone who has ever coached or played on a sports team, or had a child involved in sports, knows how difficult scheduling and logistics can be. From game and practice schedules, to uniforms and who’s bringing the snacks, it can be a lot for coaches, administrators, parents, and players to manage.
It’s no wonder that TeamSnap, a sports team, club, and tournament management app, has exploded in popularity worldwide. By syncing events to everyone’s personal calendars and providing messaging and payment tracking, TeamSnap makes communication and organization easy.
TeamSnap markets its app to coaches, players, and clubs via targeted YouTube ads. It also uses Google AdWords and DoubleClick to advertise on search results and run programmatic campaigns. These methods have been highly effective, helping TeamSnap grow to millions of users worldwide and become one of the most popular apps in the iOS app store.
As its business and data grew, TeamSnap was challenged to track ROI and measure the customer journey across channels and devices over time. The company’s marketing budget grew quickly, making it even more important to spend wisely. With data in Google Analytics 360, DoubleClick Campaign Manager, Google AdWords, and Salesforce, TeamSnap needed a way to link and correlate those data sources in a scalable, timely, and cost-effective way to understand the true impact of its digital marketing across websites and mobile apps.
To avoid the painstaking manual process of pulling data from multiple sources, TeamSnap began using Google Analytics 360, which integrates with Google BigQuery, to provide a fully managed big data analysis service. TeamSnap analyzes the data using Tableau, which connects directly to Google BigQuery for fast analytics and helps the company share and collaborate on that information with self-service ease.
The combination allows TeamSnap to easily track the activity of millions of users with self-service ease, without worrying about the scalability or availability of the big data platform.
“Before Google Analytics 360, Google BigQuery, and Tableau, tracking our return on ad spend was difficult because we had so much data,” says Ken McDonald, Chief Growth Officer at TeamSnap. “We didn’t always have insights to make the best choices. We don’t have that problem anymore because we’ve moved to real-time reporting. We find additional revenue growth opportunities almost daily.”
Making Ad Dollars Work Harder
TeamSnap now automatically imports unsampled Google Analytics 360 logs into the Google BigQuery data warehouse. To import data from other sources such as Google AdWords, DoubleClick, and YouTube, TeamSnap uses Google BigQuery Data Transfer Service. With all relevant data consolidated in Google BigQuery, TeamSnap can use Tableau to perform advanced analytics on its digital marketing, executing ad-hoc analyses in seconds, while eliminating data sampling issues, to improve accuracy. These analyses can also be reused and shared with internal and external stakeholders via Tableau Online, promoting governed reuse and consistency.
“Using Google Analytics 360 and Google BigQuery with Tableau to track our return on ad spend is ideal,” says Ken. “It’s easy to use SQL to query the data or explore it with drag-and-drop ease.”
With Google BigQuery, Ken and his team can bring all the data from the TeamSnap billing systems, internal CRM, and other Google services into one straightforward dataset that everyone uses. With Tableau, users are able to perform self-service analytics on this data and provision analyses via shared dashboards that communicate the same consistent truth across the company. These dashboards provide a single view of the business to discover new patterns and questions worth analyzing.
All of this results in enormous time savings because no one is re-inventing the wheel. “Using these tools, we immediately reallocated $300,000 of ad spend that was performing poorly, generating 200% ROI in the first two days,” says Ken.
Ken now spends his time analyzing data instead of trying to pull it all together, identifying pockets of inefficient spend in real time and reallocating those marketing dollars toward better performing campaigns.
“Before, we could only focus on the largest campaign-level datasets because it was so time consuming to pull the data,” he says. “With Google BigQuery and Tableau, we can examine our advertising ROI much more granularly and reallocate more than $10 million in ad spend annually to grow the company faster and more efficiently.”
More Effective A/B Testing
To make sure it is delivering the best customer experiences, TeamSnap uses Google Optimize to run A/B tests on its website. It uses Google BigQuery and Tableau to verify and supplement these findings by measuring longer-term customer behavior across devices, spanning both web and mobile apps.
By pulling in data from Google Optimize, Google Analytics 360, Salesforce, and in-house billing and CRM systems, and understanding it with Tableau, TeamSnap has increased the accuracy and effectiveness of its A/B testing, gaining a more complete picture of customer onboarding and activity. In some cases, it found that short-term indicators it previously trusted were actually poor predictors of long-term behavior.
“Integration between Google Analytics 360 and Google BigQuery is seamless, giving us much more confidence in our A/B testing,” says Ken. “We’re constantly finding new and interesting ways to use our digital marketing data. Often, making a simple change can increase revenue by hundreds of thousands of dollars a year.”
Improving Product Quality
TeamSnap also uses Google BigQuery and Tableau to improve its own product, tracking customer activity at such a granular level that usability and functionality issues can be exposed and addressed faster. It’s also increasing customer engagement by verifying that potential customers are coming in through the right onboarding path—for example, a coach versus a player, or a consumer versus a club or other sports business. Using A/B testing to make sure customers are routed to the appropriate flow, TeamSnap drove $4 million in additional customer value each year.
“We initially chose Google BigQuery and Tableau to help with marketing, but we realized quickly that they could help us on the product side as well,” says Ken. “Most of the testing we do is about making things better and easier for our customers, and we’re accelerating that process with Google BigQuery and Tableau.”
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How Real Companies Are Innovating with AI Today—and the Benefits They’re Seeing
What do you get when you mix Target, women’s swim wear, and AI? “Joy!” says Mike McNamara, CIO and CDO, Target.
McNamara is just one of the many stories of real businesses conquering old challenges, and new disruptive industry challenges, with artificial intelligence.
McNamara, Nick Rockwell, CTO, The New York Times, Larry Colagiovanni, VP New Product Development, eBay, and Dirk John, CIO, LATAM Airlines, demonstrate how AI solutions allow businesses to find opportunity in chaos, innovation under tough conditions, and revenue in the most unlikely of places.
AI at Target
McNamara shows how AI is being applied to solve some of the most basic, yet critical, challenges that retailers like Target–and any business with large inventories—face. (start at 4:04)
AI at The New York Times
Nick Rockwell, CTO, The New York Times, shares how AI and big data tools are being used to leverage old assets and drive business ideas from them. (start at 12:18)
AI at eBay
Larry Colagiovanni, VP New Product Development, eBay, talks about how AI is helping eBay drive conversational commerce, help buyers sift through 1.1 billion items, and personalize the shopping experience.(start at 28:07)
AI at LATAM Airlines
Dirk John, CIO, LATAM Airlines, discusses how LATAM Airlines adopted advanced analytics tools in just a few weeks to accelerate the company’s understanding of their customers’ needs.(start at 35:58)

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As organizations look to revolutionize how they analyze and utilize data, modernizing the data-centric technology stack is critical to success.
Today, the traditional stack poses several challenges—too many steps, too many tools, and too many integrations—all leading to operational complexity, time delays, and high cost. Simplifying the data pipeline, data lifecycle, and data stack offers organizations improved efficiency as well as cost savings, and provides them more value by freeing up resources to focus on deriving insight through the analysis of data.

As organizations begin to transform their approach to analytics, modernizing the analytics stack is a top priority.
To address both the data supply and data demand, data teams must look for ways to simplify and optimize the data pipeline. That means transitioning traditional solutions, like data warehouses and business intelligence tools, to modern architectures built with scalability, agility, and availability in mind.
And what follows with a modernized stack is an ideal data experience rich in actionable insight, API-driven application integration, and the ability to address the real-time needs of a dynamic business.
To find out the advantages to modernizing data warehouses and how this can be accomplished, download, ESG’s report: Modernize the Data Stack to Transform the Data Experience.
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Architecting and building a data lake on GCP with open source tools
Creating data lakes is often the first step towards maximizing value from data by generating insights for the business.
Hadoop data leaks, are today, the most common ones that are found on-premise and many of Google’s customers are moving these to the Google Cloud Platform. And the trend is accelerating.
In this video, Google Cloud Strategic Cloud Engineer, Roderick Yao, will teach you about the growing challenges in managing on-prem data lakes and what is driving the growth of open source implementations on the cloud.
He will walked you through how to architect, migrate, and secure your own open source data lake on Google Cloud using a mix of managed services and open source tools.
During the course of this presentation, Yao will go over:
- Why run data lakes on Google Cloud
- Designing and migrating data lakes
- Security and governance
How Modern is Your Data Warehouse? Find Out With This Test

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As more and more businesses turn to advanced data analytics to help them make smarter decisions, run real-time analytics, and improve business operations, an increasing number are modernizing their data warehouses to make it all possible.
For many businesses, knowing how to modernize means understanding where their data warehouse sits on the spectrum between traditional and cutting edge.
To help, we collaborated with TDWI to offer the data warehouse maturity assessment. By answering a few questions on their current warehouse environment, businesses can get a customized report with helpful tips and recommendations.
A report card for your data warehouse
The data warehouse maturity assessment evaluates five different categories, assigning your business a rating for each that spans from “traditional warehouse” to “advanced/visionary.”
- Data diversity: How well does your warehouse support disparate data types, and does it provide users with a current and holistic view of data from a variety of internal and external sources?
- Infrastructure agility: How well does your warehouse support the various needs of your organization by allowing access from anywhere, ingesting data in real time, and separating storage from compute to enable independent scaling?
- Analytics support: How easy is it to adopt sophisticated analytical techniques at scale, such as machine learning, predictive analytics, deep learning, and IoT analytics?
- Collaboration and sharing: Can teams within your organization and beyond share information and work together quickly and easily using data from your warehouse, and is your data strategy aligned with your business goals?
- Security and governance: Are there tools and techniques in place to control data access, support data governance, and ensure compliance with data security standards?
In addition to the assessment you’ll also receive a detailed report with guidance and best practices that can help you determine your next steps in your modernization journey.
Getting started
Fill out a brief form to launch the survey, then take 15 minutes to answer a series of questions about the platforms, systems, and hardware you use to integrate, manage, and store business data for reporting and analytics. You’ll see your results right away.
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