A global migration without the gaps.
Royal Canin is a Mars company and one of the best-known pet nutrition brands in the world, making breed and health-specific food for cats and dogs. Its website runs across many markets and local variations and draws a very large global audience every month.
Universal Analytics was being retired, and the whole global site had to move to GA4. Many teams across markets depended on that measurement day to day, so the switch could not break their reporting.
The site ran in many markets and variations, each with its own history of tagging. A migration done market by market would have left inconsistencies everywhere.
Marketing reporting was only part of the need. Raw analytics data also had to reach internal data teams working in Azure and Databricks.
Global analytics schema
We designed one analytics schema for the entire operation, covering events, parameters and naming. Markets could add local needs on top without changing the shared core.
GA4 migration through GTM
We migrated from Universal Analytics to GA4 through Google Tag Manager, applying the schema consistently across markets and site variations. Both systems ran in parallel during the transition.
Validation at every phase
Data accuracy was checked against legacy metrics at each phase of the rollout. Differences were explained or fixed before a market moved on, so teams could trust the new numbers.
Ingestion automation
We built Python and SQL workflows to ingest and transform analytics data from BigQuery on GCP. Jobs were scheduled and monitored so data arrived on time without manual steps.
Pipelines to Azure and Databricks
We worked with the internal data teams on pipelines connecting BigQuery to Azure and Databricks. Raw analytics data now flows into the enterprise platforms they already use.
Training and documentation
We delivered analytics training and documentation across subsidiary companies. Shared guides replaced knowledge that had sat with a few individuals.
- The migration kept measurement continuous across markets.
- Analytics data flows through automated pipelines into enterprise systems.
- Standard analytics practices were adopted globally.
- Siloed knowledge became documented, shared practice.
- Data teams in Azure and Databricks receive the raw data they need.
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