Make the Most of Your Google Analytics Data in BigQuery

Google Analytics and BigQuery are a natural fit for data modeling, business intelligence and data activation – especially for retail brands interested in optimizing their overall business performance, acquisition and retention marketing programs, and merchandising and fulfillment operations decisions.

Google Analytics provides a complete understanding of your customers across devices and platforms. Google Analytics gives you the tools, free of charge, to understand the customer journey and improve marketing ROI. BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data. Use built-in ML/AI and BI for insights at scale. Together, the software application and cloud data tooling provide business and data practitioners with an opportunity to analyze and optimize web analytics, digital marketing analytics and behavioral event data to drive profitable growth.

Connect to Google Analytics

The first step toward useful, modeled Google Analytics data in BigQuery is to connect the source and destination systems. There are many legacy tools available in market that handle the ETL or ELT transfer of Google Analytics data to BigQuery, and there are emerging tools that accomplish this transfer while providing value-added services like local data logging, and semantic data labeling and mapping along the way – making Google Analytics data modeling, analytics and activation easier once the data is landed in BigQuery.

To connect to Google Analytics, follow these easy steps

• Open SoundCommerce in any browser. Open the “Intelligent Pipeline” application from the top right navigation menu. Select “Sources” from the left navigation menu. Choose “Add New Source” from within the Sources pane to open the data source library.

• Search or browse to find “Google Analytics” within the data source library.

• Complete the “Connection Setup” form with your credentials and token to securely connect to Google Analytics and begin collecting source data.

Log Google Analytics Data for Flexible Modeling in BigQuery

There are a few more considerations to address along the way. First, what happens if Google Analytics is unavailable for some reason, or the data you’re expecting has been purged by Google Analytics? What happens when Google Analytics changes their API schemas and data scope? What happens if you need to reinterpret your Google Analytics data for a new use case in the future?

You’ll want your Google Analytics data immutably logged locally, just upstream of BigQuery to ensure you have the data and data flow flexibility you need to future-proof your Google Analytics data and models. SoundCommerce provides permanent logging of Google Analytics data upstream of BigQuery to ensure failover and future-proofing. Regardless of how you connect your Google Analytics and BigQuery data, you’ll want a data lake or event log in the middle to ensure data integrity and modeling flexibility.

Define and Label Google Analytics Data for BigQuery

As new technologies arise and best practices evolve, traditional integration tools like ETL and ELT data pipelines are giving way to intelligent pipelines that help prep data for BigQuery starting at ingest. Simply moving JSON from Google Analytics to BigQuery leaves all the work for your data team in BigQuery.

As you onboard your Google Analytics data into BigQuery, you’ll want to create semantic labels and metadata that describe the Google Analytics data for easier unification and modeling across other systems and data in BigQuery.

There are third-party solutions that will catalog your Google Analytics data and generate semantic labels and mappings after you’ve landed it in BigQuery. With SoundCommerce, the Google Analytics data is defined and labeled on its way into BigQuery instead, to avoid this costly rework later. You’ll end up with business-ready entities like orders, customers, products and campaigns, making it much easier to model your Google Analytics data in BigQuery.

Map Google Analytics Entities to BigQuery

Once the raw Google Analytics data has been organized into useful entities, it’s time to map the Google Analytics data into useful tables in BigQuery.

Why do defined and labeled entities from Google Analytics matter so much? The main reason is that Google Analytics data needs to be combined with data from other SaaS and on-premise software systems in useful ways. Landing raw Google Analytics data in BigQuery without this semantic understanding means data engineering and analyst teams must do all of the heavy lifting regarding the meaning of the Google Analytics data and the standardization of the meaning of that Google Analytics data from scratch in BigQuery.

Defining, labeling and mapping the Google Analytics data on the way in means much less effort once the data is landed in BigQuery.

Materialize Google Analytics Data in BigQuery

Next, you’ll establish a secure connection to BigQuery:

• Select “Destinations” from the left navigation menu. Choose “Add New Destination” from within the Destinations pane to open the data destination library.

• Complete the “Connection Setup” form to securely connect to BigQuery to establish a secure destination for your labeled, mapped and modeled data.

That’s it! You now have logged, labeled and mapped data from Google Analytics flowing securely to BigQuery.

Model Google Analytics Data in BigQuery

Once you have well-formed entities from Google Analytics onboarded to BigQuery, it’s time to build useful analytical and behavioral models on the Google Analytics data – and combine the Google Analytics data with data sets from other systems in BigQuery for more advanced, cross-dimensional analysis.

You can build your own analytical models on the Google Analytics data in BigQuery using languages like SQL and Python, organized into model libraries in tools like DBT or Coalesce. With SoundCommerce, you get prebuilt analytical models for Google Analytics running in BigQuery, with ready access to the model source code in DBT.

Host the Modeled Google Analytics Data in BigQuery for Analytics

BigQuery supports reporting and visualization through a wide variety of analytics tools including Sigma, Tableau, Looker, Power BI and Microstrategy to name a few. You can build your own dashboards, tabular views and graphs in any of these tools to reveal insights about Google Analytics in your BigQuery models. SoundCommerce provides pre-built embedded reports in Sigma to reduce the time, cost and risk of BI reporting of Google Analytics data out of BigQuery – so you can start making better decisions and taking better action as soon as you’ve connected Google Analytics to BigQuery.

Host the Modeled Google Analytics Data in BigQuery for Campaign and Customer Activation

Whether your marketing team uses Google Analytics for activation – or uses other tools and channels or both to take action on the data – you’ll want to be able to easily move your modeled Google Analytics data in BigQuery to your most important marketing applications.

If you’ve followed the steps above to properly onboard and model your Google Analytics data in BigQuery, it’s easy to use reverse ETL (rETL) tools like Census or Hightouch to orchestrate the data from there, or use SoundCommerce native orchestrations to push data into common channels and applications like Facebook, Instagram, TikTok, Braze, Klaviyo, Insider or Dynamic Yield to put the Google Analytics data in BigQuery to use!

Getting Your Google Analytics Data Defined, Labeled, Mapped and Modeled in BigQuery is Easy!

SoundCommerce can automate the steps necessary to bring Google Analytics data into BigQuery, addressing the key functions of raw Google Analytics data logging, Google Analytics semantic definitions and mappings, and pre-built Google Analytics data models that are analytics- and activation ready in BigQuery.

Contact us today to get started with Google Analytics in BigQuery!

Technical Resources for Integrating
Google Analytics Data with BigQuery

More information and technical specifications for data collection from Google Analytics is available at:

Google Analytics API Documentation

More information and technical specifications for data ingest into BigQuery is available at:

BigQuery API Documentation