thomas androws_
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Expertise

What I work on.

Six areas of web analytics, from collecting clean data to reporting it and measuring how your brand shows up in search and AI answers.

01

GA4 implementation and audits

I design the measurement plan first, then the events, parameters and key events that serve it. Audits cover data layer quality, consent behavior, duplicate and missing events, attribution settings and BigQuery export health.

  • Measurement plan and event taxonomy
  • Consent Mode v2 aware configuration
  • Custom dimensions, key events and audiences
  • Audit of existing GA4 properties
02

Server-side GTM

Server-side tagging moves part of your measurement to infrastructure you control. It can improve data quality and governance, but it adds cost and complexity, so I help teams decide whether it is worth it before they build it.

  • Web and server container architecture
  • First-party collection and tag clean-up
  • Cost and benefit assessment
  • Migration without losing history
03

BigQuery and data modeling

The raw GA4 export is powerful but awkward. I turn it into sessionized, documented tables that analysts and dashboards can trust, with definitions anyone can check.

  • GA4 export setup and monitoring
  • Sessions, events and user models in SQL
  • Joins with CRM and enrichment data
  • Cost-aware query design
04

Looker Studio reporting

A dashboard should answer a decision, not display every metric. I build Looker Studio reports around the questions teams ask, with documented calculated fields and clean sources.

  • Decision-first dashboard design
  • Documented calculated fields
  • BigQuery-backed, fast reports
  • Stakeholder-ready layouts
05

Search and AEO analytics

Search is now split between classic results and answer engines. I measure both: Search Console for Google, and GA4 plus referral analysis for traffic from AI assistants.

  • Search Console analysis and reporting
  • AI referral traffic tracking in GA4
  • Structured data and answer-first content
  • Brand and web team reporting
06

AI-assisted analytics

I use Vertex AI and large language models to speed up analysis, QA and documentation, while keeping every number traceable back to the data that produced it.

  • LLM-assisted QA and documentation
  • Vertex AI workflows on BigQuery data
  • Guardrails and audit trails
  • Practical use cases, not demos

FAQ

Common questions.

What is the first step in a GA4 implementation?

Define the measurement plan and design the data layer before you tag anything. Tags are easy to change later, but a weak data layer forces you to re-tag the whole site.

When is server-side GTM worth it?

When you need more control over what data is sent to vendors, want to improve data quality and governance, or have a clear business case that justifies the extra hosting cost and complexity. For many smaller sites a well built web container is enough.

Why export GA4 data to BigQuery?

The BigQuery export gives you raw event data that is not sampled and can be joined with other sources such as CRM data. It lets you build documented models that dashboards and analysts can trust.

Can you track traffic from AI assistants like ChatGPT or Perplexity?

Partly. Referral traffic from AI assistants can be grouped in GA4 using a channel group or regex on the source, and Search Console covers Google search. Impressions inside AI answers are not fully measurable yet.

Talk about your measurement