Last updated 2026-06-10
We have administered Google Analytics deployments since the firm opened in 2009, through Urchin-era page tracking, Universal Analytics, and the GA4 rebuild that quietly invalidated most companies' historical assumptions. The pattern across all of them: analytics is rarely wrong because the tool is bad. It is wrong because nobody owned the configuration, events accumulated without definitions, and the numbers drifted from the business until nobody trusted a report.
Our work on the platform is making the numbers trustworthy and then making them useful. That means GA4 property architecture and an event taxonomy that mirrors how your business actually makes money, server-side tagging that survives browser privacy controls, and reporting that ties channels to revenue. The practice is described in full under analytics management.
What we run on Google Analytics
GA4 property and event architecture
Properties, data streams, and a documented event taxonomy designed around how your business earns, not default events.
Server-side tagging
Tag Manager server containers that keep measurement accurate as browsers and consent rules strip the client side.
Ad platform conversion plumbing
Verified conversion feeds into Google Ads and the social platforms so bidding algorithms learn from real outcomes.
Attribution and channel reporting
Reports that reconcile GA4, platform claims, and revenue records into one read on what each channel actually contributes.
BigQuery export and AI-assisted analysis
Raw event data exported and queried, with AI-assisted analysis surfacing funnel leaks and segment behavior on demand.
Anomaly detection and tracking QA
Automated checks that catch broken tags, missing events, and suspicious shifts before a month of data is compromised.
What AI changes in analytics work
GA4 is itself a response to AI-era measurement: modeled conversions and modeled behavior fill the gaps consent banners and browser privacy leave behind. Modeled data is usable, but only if the observed data feeding the models is clean, which raises the stakes on event design, consent mode configuration, and server-side tagging. Garbage in is now garbage modeled, with more confidence.
On the analysis side, AI removed the excuse for monthly-report archaeology. Anomaly detection flags tracking breaks and traffic shifts in hours, and natural-language analysis over the BigQuery export answers questions that used to die in a backlog: which segments actually convert, where the funnel leaks, what changed after a release. Analysts still frame the questions and sanity-check the answers, because a fluent summary of misconfigured data is still wrong.
How a measurement engagement runs
It starts with an audit: property configuration, tag coverage, event definitions, consent handling, and a reconciliation of what GA4 claims against your CRM and revenue records. The gap between those numbers is usually the first finding, and explaining it precisely is the first deliverable. Rebuild work follows, event taxonomy, server-side tagging through Tag Manager, conversion imports to the ad platforms, in whatever order the gaps dictate.
After the rebuild, measurement becomes a managed function: definitions documented, changes versioned, anomalies investigated when they appear rather than when they are noticed. Most clients arrive at this work through a channel engagement, ads or SEO that could not be judged with the existing data, and it underpins everything else in our AI optimization practice. Clean measurement is the prerequisite, not a nice-to-have.