Analytics that hold up without third-party cookies.
Advanced analytics implementation: events designed for the business's questions, cross-domain tracking, identification from first-party data and export to the warehouse.
From user actions to insights.
Proper analytics, done once.
Most analytics implementations stay half-finished. Events fire, but schemas drift over time. Cross-domain tracking is set up, but user identification is vague. Reports look reasonable, but nobody has checked they match the reality of sales.
We build events for the questions the business really needs answered, not copied from a ready template. We set up cross-domain tracking with clear identification rules, bring in first-party data and send every event to the cloud warehouse, so attribution and cohorts run without the platform's retention limits.
The implementation is documented and version-controlled. Changes go through review and data quality is monitored continuously. The knowledge stays in the documentation and the code, not in the heads of those who built it.
What changes for you.
Events for the business
An event schema designed for the questions that must be answered: product interactions, funnel stages and touchpoints across all channels.
Cross-domain tracking
Cross-domain and cross-subdomain tracking with clear identification. Journeys that pass through the site, the shop and post-purchase pages are recorded as single sessions.
First-party identification
Hashed first-party data is built into measurement, so user-level insight holds up against browser restrictions and consent.
Warehouse export
Every event is exported to the cloud warehouse. The platform's retention limits stop being a restriction: long-term analysis, attribution and cohorts run on the warehouse.
Consent respected
Consent mode is set up by region. The implementation respects consent state on every event, improving both compliance and measurement quality.
Documented and reviewed
Schema, event definitions and decisions are documented in code. Changes are reviewed with pull requests, and knowledge doesn't depend on any one person.
How the implementation runs.
Audit
We assess the current implementation, the schema, tracking coverage, cross-domain behaviour and the gap between what you think is recorded and what is actually usable.
Design
A target for the schema, identification rules, consent handling and the export architecture. The plan is documented before any tag goes live.
Build
Events go live with schema checks. Identification and the warehouse export are set up, and data quality monitoring starts from day one.
Verification
Parallel running and cross-checking against real data from the sales platform. We resolve differences before the old implementation is retired.
Common questions.
Measure properly.
Let's talk about analytics that hold up through browser changes, consent changes and team changes.
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