What is BigQuery used for in marketing?

It is a cloud data warehouse where you bring together data from GA4, Google Ads, your CRM and your ecommerce platform. You can analyse raw GA4 events, build custom data models and use it as the central source for your dashboards.

Do I need BigQuery, or are GA4 reports enough?

GA4 reports are enough for many projects. BigQuery becomes useful at greater scale, for example when you combine many data sources, analyse longer periods or build reports that standard tools cannot produce. We start from your situation and your scale.

Which data sources can you connect?

GA4, Google Ads, your CRM and your ecommerce platform are the core. Other platforms and financial or operational data can be added where the system allows it, so the final scope depends on what can be integrated.

How does the data get into BigQuery?

Through connections we implement for you. They run automatically, so nobody exports files by hand and pastes them into spreadsheets.

Do you build the reports as well?

Yes. We create Looker Studio dashboards for you, for your management and for your teams, designed around your goals rather than from generic templates.

Can you train our team, or run everything for us?

Both are possible. You can choose training so your team learns to run the set-up, or you can have us take it over completely.

Can we measure ROAS and LTV across channels?

Yes. Measuring ROAS and LTV across several channels is exactly the situation this service is built for. Joining cost data from your ad platforms with sales data from your CRM or shop in one place makes cross-channel analysis much easier. The scope depends on what can be integrated.

How much does a BigQuery implementation cost?

There is no price list, because the cost depends on the scope: which sources you connect, what can be integrated and whether you choose training or a full takeover. Contact us to talk through your data and get a quote.

BigQuery

BigQuery implementation for marketing: one source of truth

You have GA4, ads, a CRM, an online shop and campaigns, but no single version of your results. We join that data in BigQuery, automate the connections and build reports that are current and fast, without spreadsheets, so your team works from facts instead of guesses.

Talk about your data GA4 and GTM implementation

Google Premier Partner 2026
Top 3% of Google Ads agencies in Poland
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Campaigns in 25 export markets

Why BigQuery for your marketing data?

BigQuery implementation for marketing means bringing data from GA4, Google Ads, your CRM and your ecommerce platform into one place, tidying it, automating it and turning it into conclusions. The question we start with is simple: do you have data, and do you use it?

Google BigQuery stores and analyses data in the cloud. It lets you work on the event export from GA4, process large datasets and join them with other systems. At greater scale, standard reports may no longer be enough, especially when you combine many sources, analyse longer periods or build custom reports. That is the point where a data warehouse starts to pay off.

We do not produce a pretty report. We join, simplify and automate the data, so that marketing works faster, cheaper and more precisely. Anything less than getting the most from your data is a cost: lost time, money and team potential.

What BigQuery gives your marketing team

One source of truth

Data from GA4, Google Ads, your CRM and your ecommerce platform in one place.

Joined-up data

Instead of chaos, a full picture of the user, from the first click to the sale.

Reports without waiting

Current, fast reports, with no spreadsheets to update by hand.

Decisions based on facts

Your team does not guess. It knows.

From concept to finished reports

We cover the whole path, from the first idea of how your data should fit together to dashboards your management and teams use every day.

We design the data structure
We decide where the data comes from, how to join it and what to watch out for.
We implement the connections
GA4, Google Ads, your CRM and other sources are connected automatically, with no manual exporting and pasting.
We build the data warehouse in BigQuery
Fast, stable and designed with growth in mind.
We create Looker Studio reports
Dashboards for you, for your management and for your teams.
We train you, or take it over
You can choose to learn to run it yourself or have us take over the whole thing.

The data we can join

The final scope depends on what can be integrated. In larger projects we also plan for validation, storage and unification of the data.

Source	What it brings to the warehouse
GA4	Traffic, behaviour and conversions, including raw events from the GA4 export
Google Ads	Costs and campaign results
CRM	Lead statuses and sales results
Ecommerce platform	Transactions and order data
Other platforms and systems	Results from other ad platforms, and financial or operational data, where the system allows it

Typical uses of BigQuery in marketing

analysis of raw events from GA4
joining marketing, purchase and CRM data
custom summaries and data models
a central data source for your dashboards
analysis of marketing effectiveness over a longer period

Reporting that supports decisions

Data is usually scattered: costs in your ad platforms, behaviour in GA4, sales in your CRM or shop. Separate reports make it hard to see the whole process. We design dashboards around your goals, not off-the-shelf templates. A dashboard can show spend, revenue, ROAS, CPA, the number and quality of leads and progress against your targets.

The aim is not the most charts. It is an answer to one question: what works, what does not and where to invest. For more on dashboards and API integrations, see our web analytics service. If the data shows high traffic but weak sales, our CRO audit looks at why.

Foundations first

BigQuery is not necessary in every project, and the choice depends on scale. Not every company needs a data warehouse and elaborate dashboards from day one. If your GA4 and tag set-up is not reliable yet, fix that first with a proper GA4 and Google Tag Manager implementation, then extend it.