Event tracking chaos: GA4 events that fire inconsistently, custom dimensions that don't populate, or conversion tracking that doesn't match revenue data.
Data fragmentation: Marketing data in GA4, sales data in CRM, product data in your database — no unified view of customer journey or attribution.
Dashboard overload: 15 metrics nobody looks at, reports that don't answer business questions, or dashboards that break every time data schema changes.
Clean event tracking architecture in GA4 where every event has a clear purpose. GTM container organization that doesn't become unmaintainable after 50 tags. Server-side tagging when client-side tracking fails due to ad blockers or consent requirements.
I audit what's broken, fix event implementation, and validate that data flows correctly across your entire stack. No "set it and forget it" — analytics infrastructure requires maintenance.
Connecting GA4 to your CRM, e-commerce platform, and advertising accounts so you can see full customer journey. Attribution modeling that shows which channels actually drive revenue, not just last-click credit.
I build data pipelines that sync automatically, not manual exports that break. UTM parameter strategies that survive URL changes and campaign renames.
Which events matter? What custom dimensions do you actually need? How should event parameters be structured so reports make sense?
I design event taxonomies that scale without creating chaos. Clear naming conventions, parameter documentation, and QA processes so tracking doesn't break after dev pushes.
Dashboards built around business questions, not metric dumps. Reports that show: What's working? What's broken? Where should we invest more?
I use Looker Studio, Tableau, or whatever you have — but the tool doesn't matter if the underlying data is wrong. Fix data quality first, then build dashboards.
How do you know your analytics data is accurate? I implement validation checks: event firing tests, conversion count reconciliation, and automated alerts when tracking breaks.
Regular audits to catch tracking drift, schema changes that break reports, or new features that weren't instrumented correctly.
Multi-touch attribution across channels. Customer journey mapping that shows touchpoints from awareness to purchase. Cohort analysis to understand retention and LTV.
I connect dots across GA4, CRM, and ad platforms so you can see which channels work together, not just which gets last-click credit.
Most analytics consultants build dashboards and reports. I specialize in the infrastructure layer — GA4/GTM implementation, event tracking architecture, and data integration pipelines — that determines whether your data is accurate in the first place. This is the technical foundation that makes reporting useful.
Event tracking fixes take 2-4 weeks once implemented and validated. Data integration pipelines (connecting GA4 to CRM/e-commerce) take 3-6 weeks depending on API complexity. Dashboard builds happen fast once data quality is confirmed — usually 1-2 weeks.
Most GA4 setups have issues: events firing inconsistently, custom dimensions not populated correctly, or e-commerce tracking that doesn't match actual revenue. I audit what's actually flowing through, validate accuracy, and fix gaps before building reports on bad data.
Both. For GTM/GA4 setup, I implement directly. For deeper integrations (API connections, database queries, custom BigQuery pipelines), I work with your data team or provide specs if you have engineering resources. I adapt to your infrastructure.
I work with GA4, GTM, Looker Studio, and whatever you already have. If you need advanced features (BigQuery export, custom attribution models, real-time dashboards), I'll recommend tools, but I start with your existing stack and only add complexity where necessary.
Validation at every layer: GTM preview mode testing before launch, event parameter validation in GA4 DebugView, reconciliation checks between GA4 conversion counts and actual revenue data, and automated alerts when tracking breaks. Data quality is not a one-time setup — it requires ongoing monitoring.
If your analytics data doesn't match revenue reports, or you can't answer basic attribution questions, let's talk.
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