Snowflake

Turn the marketing and customer data already in Snowflake into better reporting, measurement, audiences and activation.

What it is

Snowflake is a cloud data platform that allows organisations to store, process and analyse large volumes of data while scaling storage and computing resources independently.

For marketing teams, Snowflake can bring together customer, transactional, analytics and advertising data in a shared environment.

That data can then support reporting and measurement as well as operational marketing use cases such as customer modelling, audience creation, suppression, conversion signals and first-party data activation.

Snowflake also supports secure data sharing and clean room environments, giving organisations additional ways to collaborate with partners without relying on the direct exchange of raw customer data.

Why it matters

Marketing data increasingly needs to move in both directions.

Data from analytics, advertising, CRM and transactional systems flows into the warehouse, where it can be cleaned, connected and modelled. Audiences, customer value signals and conversions then need to move back out to the platforms responsible for activation.

That makes Snowflake more than somewhere marketing reports are stored. For organisations already using it as their enterprise data platform, it can become a central part of the marketing data infrastructure.

Connecting Snowflake effectively with advertising and marketing platforms helps organisations make better use of first-party data while maintaining consistent definitions, governance and consent controls.

What Louder does

Getting marketing data in - GA4 via BigQuery, ad platform connectors, and an honest assessment of build versus buy for the ingestion layer.
Identity and customer modelling - resolution logic, match-rate baselining and the modelled tables activation depends on.
Activation out - reverse ETL to Google Ads, DV360, Meta and The Trade Desk, Customer Match and conversion API feeds, with refresh and suppression handled as part of the design rather than after it.
Cost and warehouse sizing - right-sizing virtual warehouses, auto-suspend policy, and workload separation so one heavy job doesn’t set the bill for everything.
Governance - role hierarchy, masking policies, consent flags carried through to activation, retention and deletion.
Serving layers - modelled marts for Power BI, Looker or Tableau rather than reporting tools pointed at raw tables.

Common challenges

Data ingestion - Connecting GA4 via BigQuery, advertising platform connectors and designing the right ingestion architecture based on your environment rather than forcing unnecessary complexity.
Identity and customer modelling - Building customer models, identity resolution logic, match-rate baselines and the datasets that activation depends on.
Data activation - Delivering audiences, Customer Match lists and Conversion API feeds into Google Ads, DV360, Meta and The Trade Desk through automated reverse ETL pipelines with suppression and refresh logic built in.
Cost optimisation - Right-sizing virtual warehouses, configuring auto-suspend policies and separating workloads so one heavy process doesn’t drive costs across the entire platform.
Governance - Designing role hierarchies, masking policies, consent-aware data models, retention policies and deletion processes that support privacy obligations.
Reporting and serving layers - Creating modelled data marts for Power BI, Looker and Tableau instead of pointing reporting tools directly at raw warehouse tables.