BigQuery for measurement

Use BigQuery to bring raw marketing and analytics data together for more flexible reporting, attribution and measurement.

For broader BigQuery cloud and data capabilities, see BigQuery

What it is

BigQuery for measurement gives organisations access to the underlying data from platforms such as Google Analytics 4 (GA4), Google Ads and Search Ads 360 (SA360).

Instead of relying entirely on platform reporting, teams can bring raw and granular data into BigQuery, model it consistently and connect it with other marketing and business data.

This creates a measurement environment where organisations can build their own reporting, attribution, audiences and analysis around the questions that matter to their business.

The challenge isn’t simply getting data into BigQuery. It’s structuring, governing and maintaining it so teams have a reliable source of measurement data they can actually use.

Why it matters

Standard analytics and advertising interfaces are designed to answer common questions, but they can’t provide every view of performance an organisation needs.

BigQuery gives measurement teams greater flexibility to work with granular data, combine information across platforms and apply consistent business definitions.

It can also support more advanced measurement, including attribution, customer journey analysis, audience development and modelling, while reducing reliance on the way individual platforms choose to report performance.

Done well, BigQuery becomes a trusted measurement foundation that connects marketing activity with wider business outcomes.

What Louder does

Export configuration - GA4, Ads and SA360 exports configured, scheduled and monitored.
Modelling - attribution models, conversion modelling, audience definitions built on the raw data.
Reporting infrastructure - Data Studio (formerly known as Looker Studio, Power BI or Looker layers pointed at curated marts.
Cost governance - query optimisation, slot management, scheduled materialisations.
Operational support - schema changes, monitoring, integration with broader cloud data environments.

Common challenges

Querying raw event tables daily. Without materialised marts, every report scans gigabytes. Costs balloon, queries slow, and analysts give up.
No data model. Teams query the GA4 export directly forever, never building a clean fact-and-dimension layer. Six months in, every analyst has their own version of “sessions”.
Forgetting schema drift. GA4 event schemas change with product changes. Without monitoring, reports break silently.
Mixing exports without modelling currency or time zones. GA4 reports in UTC, Ads in account time zone, SA360 in advertiser time. Without a normalised model, joins produce subtly wrong numbers.