Like most fast-growing SaaS companies, we use a ton of data to track and spur our growth at Supermetrics. 

A big part of the credit here goes to our 3-person growth team, who are in charge of tracking and analyzing our marketing and sales performance as well as sharing their insights with the rest of the company.

And since we’re huge advocates for data warehousing as a solution for centralizing all your marketing and sales data, we wanted to shed some light on how we do these things ourselves.

So fasten your seatbelt and get ready, because you’re about to learn the ins and outs of data warehousing from our Senior Data Analyst Anni Kuittinen and Head of Growth Miika Kenttämies.

The previous solution

When Miika started at Supermetrics in June 2019, we were using a combination of: 

  • internal reporting tools
  • a customer data platform
  • a ton of marketing tools including (but not limited to) 
    • Facebook Ads 
    • Google Ads
    • LinkedIn Ads 
    • Google Analytics
    • SEMrush

You get the point…

Sure, we were lucky in that we were able to get reliable trial and sales data from our CDP and internal reporting tools. Yet, the growth team wanted to drill one level deeper and centralize all this data so that we could start attributing trials and sales to specific channels, campaigns, and activities. 

As Anni puts it: “The data extraction was, at worst, just looking at the native UI and then copy/pasting all the results into a spreadsheet if no connector for that data source existed. You can imagine how painful that was, especially with multiple solutions.”

And since we wanted to make our marketing and sales data work harder for us, the growth team knew they needed to start by centralizing it.

Finding a scalable solution for data storage and analysis

“As a growing company, our data needs are constantly changing. The volume of data we’re generating grows with us. And as our reporting became more complex, we needed a more flexible data model,” Miika explains.

Instead of only focusing on the current business needs, Miika and the team knew that they needed to choose a solution that would scale together with the fast-growing business. 

For these reasons, Google BigQuery was quickly identified as the right platform for the job. 

“BigQuery allows us to store as much data as we want, use different data models for different reports, and pull the exact data we need in minutes. If that’s not impressive, I don’t know what is,” Anni sums up.

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Setting up data transfers into BigQuery

Since we were already building Supermetrics for BigQuery at the time, automating our data flows into the data warehouse was a breeze.

“With the help of Supermetrics, we started pulling data from different tools such as Google Analytics, Facebook Ads, and our internal reporting systems. These extractions provided us with a large dataset to work with. No data truncation was needed as we could store data in its raw form. All our data is stored in its original form for analysis,” Anni explains.

“Supermetrics for BigQuery is easy to use and we were able to get the data flowing immediately. We don’t have to reinvent the wheel, as we have our own connectors to use. Our data is constantly updated, so we can focus on analyzing the data and building new dashboards,” Anni continues.

The biggest benefit of Supermetrics is that it eliminates the need for repetitive manual work. Miika says, “Instead of writing the same query every time we want to pull in new data, we can save queries and use them later.”

Supermetrics ❤️ Google BigQuery: 3 awesome things we’re now able to do

Next up, let’s look at some practical examples of what we do with the data we store in BigQuery.

1. Internal reporting with automated Google Data Studio dashboards

Most of the inhouse reporting at Supermetrics is done using Google Data Studio. Miika explains, “All our Google Data Studio dashboards are built with the expectation of being used for a long time. And as new data flows automatically into BigQuery through the Supermetrics connectors, the changes are instantly reflected in our dashboards.”

Instead of the growth team spending all their time on manually building weekly or monthly reports for the marketing and sales teams, all they have to do is to build a Data Studio dashboard once and then share the link with the relevant people. This way, the teams can base their decisions on fresh data whenever they need it.

2. Attribution modeling

Since we want to understand the typical buying journeys and the touchpoints people go through before they start a Supermetrics trial and end up as customers, the growth team ended up building an attribution model based on our data in BigQuery. “We take Google Analytics data and combine it with our internal backend data. We can see where the traffic is coming from and how people are converting,” Anni explains.

Attribution modeling helps the marketing team double down on the channels, campaigns, and tactics that are working, and scrap the activities that are not bringing in business. Blending data from different sources gives the stakeholders a complete picture of the customer journey, helping them to see how users find the product. 

3. Ad hoc analysis

At Supermetrics, we believe that everyone should have access to data. 

It’s not just something we like to say, we practice it here everyday. Anyone that needs data can ask for access to our BigQuery instance. “Sometimes, people inside the company want to do data analysis on their own. And when that happens, they can just pull data from the data warehouse into Google Sheets using our connector,” Anni adds.

Having data easily available allows teams to test their own hypotheses. And if they want to start building their own Data Studio dashboards instead of relying on the growth team, they’re welcome to do that. Using Supermetrics’ native Data Studio connector, internal teams can instantly pull data from BigQuery into their dashboards.

“BigQuery grows as we grow”

As Supermetrics grows as a business, so too do the possibilities of using our data. Since Google BigQuery supports different languages like R and Python, our data analysts can create predictive models to support the sales and marketing teams going forward.

To conclude, Miika notes that, “Google BigQuery offers us a powerful solution that scales up with us and helps us avoid any unnecessary copy/paste work. All of the data is consolidated and no longer siloed. Google BigQuery is a tool that grows as we grow. I can’t imagine going back”.

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