Building foundations

Whether you've never used Cloud before or are starting to have more advanced requirements, we have the expertise to help. BigQuery is a fantastic tool that extends the usefulness of your web analytics data whilst also allowing you to connect other sources together to form the basis of dashboards and models.

BigQuery

Whether you're just getting started with more advanced questions or looking to avoid sampling in GA4, we can support with setting you up with BigQuery and start writing simple queries

Automation

Once you're starting to more regularly refresh insights and use the data, you'll need support with building pipelines and automating queries. We can help you get to grips with Dataform to ensure your processes run smoothly and regularly.

Connecting data sources

Using GA4 data in BigQuery is just the start. Where you have long lead-times and want to connect lead data or simply want to connect web analytics data with media or CRM data, we can support with ID stitching and start building views that let you answer your critical business questions.

Building on foundations

Once you've got the data model in place, it's time to start using the data in earnest. Whether that's layering on top a dashboard to help align stakeholders or starting to look at models for things like attribution and audience propensities, we have the team to support.

Reporting

With the data solution set-up, we can connect BigQuery to dashboarding tools like Looker Studio to build media, CRM or exec-level dashboards and help answer critical questions - particularly where lifetime value or long lead times are a focus.

Modelling

There's lots of options when it comes to modelling, from building out your full data-set and looking at consent modelling to forecasting and helping to inform business cases based on what you expect to see moving forwards.

Audiences

The other key use case for BigQuery is to inform a more advanced audience strategy. We can start with building out propensity audiences based on an expected action all the way through to next-best-action modelling.

FAQs

The world of BigQuery and Cloud can seem like a significant and technical leap into the unknown but it doesn't have to be. Getting set-up with a Google Cloud project, integrating GA4 and putting a limit on any costs is quick and simple to do.

Is BigQuery expensive?

The short answer is - it depends on how it's being used. But the easiest answer to reassure you is that you can put a spend limit on your project to ensure you stay in control of the costs.

Digging deeper, there are two main ways in which you incur costs with BigQuery. First is storage. And the second is through using the data, known as Compute costs. More details can be found here.

I just want to store web analytics data and run more advanced queries, what would that cost?

What's needed to support modelling work?

It depends on what you want to model.

For modelling unconsented users, it's pretty simple, we just need the data from consented users, which we can get from the standard BigQuery export from Google Analytics.

For forecasting, likewise, a couple of years of historical data of the metric you want to forecast is all that's required.

When it gets into more advanced models like lifetime value, the short answer is that the more discreet data points we have on customers, the better we can train our model.

Beyond audience modelling and forecasting work, did you know there was a BigQuery integration with Google Search Console? It allows for a deep-dive into that long-tail of search terms that people are searching for. We can support with clustering and analysis of these terms as part of a dedicated project within our SEO team.

Likewise, if you're finding GA4 a chore when it comes to sampling for post-test analysis within CRO, speak to our team about our solutions and how we can support you with time-saving analysis.