GA4, GTM & Measurement Engineering
Analytics & Conversion Tracking
Most analytics setups I audit are quietly lying: conversions counted twice, forms firing events that never reach a report, consent mode misconfigured, and half the traffic sitting in 'Unassigned'. I implement GA4 and Google Tag Manager the engineering way — a documented measurement plan, a clean data layer, server-side options where they matter, and tracking you can actually defend in a budget meeting.
What's Included
Comprehensive solutions tailored to your specific needs
GA4 implementation & migration
Property configuration, event schema, conversions, audiences and reporting structure — built from a measurement plan, not defaults.
Google Tag Manager engineering
Clean container architecture, data-layer-driven triggers, naming conventions, versioning and documentation.
E-commerce tracking
Full purchase funnel events with transaction IDs, item-level parameters and revenue that reconciles with your order data.
Consent Mode v2
GDPR-correct consent state handling for GA4 and ad platforms across EU and UK traffic.
Cross-domain & SPA tracking
Correct session continuity across domains, subdomains, payment providers and client-side routed applications.
Analytics forensics
Audits of inherited containers and properties — what fires, why, and what should be removed.
Problems I Solve
Symptoms this engagement is built to fix
Conversions counted twice — once by a GTM tag and once by a hardcoded pixel nobody remembered installing.
GA4 reporting 'Unassigned' and 'Direct' for traffic that clearly came from campaigns with UTM parameters.
Form submissions that show in the CRM but never appear as conversions in GA4 or Google Ads.
Consent banners that either block everything (destroying data) or fire tags before consent (a compliance risk).
E-commerce revenue in GA4 that doesn't match the shop's actual order totals.
A GTM container with 80 tags, no naming convention, and nobody who knows what half of them do.
Measurement plan before a single tag
Every engagement starts with a measurement plan, not a container. We define the business questions first — which actions indicate a qualified lead, which micro-conversions predict purchase, which channels need attribution — and map each one to a concrete event with defined parameters. Only then does anything get implemented. This order matters because the failure mode of most analytics setups is not technical, it's conceptual: teams install tags reactively over years, and end up measuring everything and understanding nothing. The measurement plan becomes the document every future change is checked against.
A data layer that developers and marketers both understand
Reliable tracking lives in the data layer, not in CSS-selector triggers that break every time someone restyles a button. I implement a proper dataLayer contract: named events with documented parameters, pushed by the application code on form success, add-to-cart, checkout steps and lead actions — then GTM reads from that contract. This makes tracking resilient to design changes, testable in CI, and portable: the same data layer feeds GA4, Google Ads, Meta and any future platform without re-instrumentation. For WordPress, Shopify and WooCommerce I work with the platform's native hooks; for Laravel and React applications I implement the pushes in the application itself.
Consent mode and GDPR-correct configuration
For EU and UK traffic, consent handling is not optional decoration — it's the difference between usable data and a legal liability. I configure Google Consent Mode v2 correctly: tags held until consent state is known, ad_storage and analytics_storage mapped to the banner's actual choices, and consent state respected on every page including SPA route changes. Where appropriate I implement consent-aware modelling so reporting stays useful without firing tags pre-consent. I also audit the banner tools themselves — Cookiebot, CookieYes, Complianz and custom builds each fail in their own characteristic ways.
Conversion tracking that survives scrutiny
A conversion number is only useful if it matches reality. I de-duplicate conversion paths (GA4 events vs Google Ads conversion imports vs platform pixels), implement transaction IDs so refunds and resubmissions don't inflate e-commerce numbers, and verify every conversion with real test transactions — not just Tag Assistant previews. For lead-generation sites, I distinguish lead events (form submitted) from qualified-lead events (validated, non-spam, CRM-confirmed where a webhook exists) so ad platforms optimise toward real business outcomes instead of spam submissions.
Debugging the setups nobody wants to touch
A large share of this work is forensic: inherited GTM containers with years of accumulated tags, GA4 properties created by a plugin with default settings, and reports nobody trusts. I audit container-by-container and property-by-property: what fires, when, with what data, and whether it should exist. Dead tags are removed, naming conventions are imposed, workspaces and version history are documented, and you get a written map of the final state — so the next person who opens the container (including future you) isn't starting an archaeology project.
What You Receive
Deliverables & expected outcomes
- Written measurement plan mapping business questions to events
- Configured GA4 property with documented event and conversion schema
- Clean, documented GTM container with naming conventions and version notes
- Data layer specification implemented in your platform or application
- Consent Mode v2 configuration verified with real consent states
- End-to-end test evidence: every conversion verified with real transactions
Illustrative Engagement
A representative scenario
The problem
A lead-generation business was optimising Google Ads toward a 'conversion' that fired on button click — before form validation. Reported conversions were triple the actual lead count, and the ad platform had spent months bidding toward spam.
The approach
We rebuilt tracking around a data-layer event pushed only on validated submission, de-duplicated the legacy GTM tag and the hardcoded gtag, wired the CRM's qualified-lead webhook back as a separate conversion, and imported only the qualified event into Google Ads.
The outcome
Reported conversions dropped to match CRM reality. Cost per qualified lead fell by roughly a third over the following quarter as automated bidding finally optimised toward real leads.
Composite scenario based on typical engagements. Replace with a real, attributed case study once published.
My Process
A proven methodology for delivering exceptional results
Measurement plan
Business questions mapped to events, parameters and conversions before anything is implemented.
Data layer & implementation
Application-level event pushes, GTM container rebuild and GA4 configuration.
Verification
Every conversion tested with real transactions and test leads — across devices, consent states and browsers.
Documentation & handover
A written map of the final setup so the system stays understandable after I leave.
Why Choose Me?
With 7+ years of experience and a track record of delivering results, I bring expertise, reliability, and a results-driven approach to every project.
- Conversion numbers that match your CRM and order data
- Tracking that survives redesigns because it lives in the data layer
- GDPR-correct consent handling without losing all measurement
- Ad platforms optimising toward qualified leads, not button clicks
- A documented setup the next developer can actually understand
- One engineer across GTM, GA4, your CMS and your application code
Starting at
€397
GA4 + GTM audit with a written measurement plan. Full implementation quoted per scope.
Frequently Asked Questions
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