Server-Side Tracking
GTM Server, Stape, or custom server-side event capture engineered to restore measurement accuracy that browser restrictions destroy. iOS 17, Safari ITP, ad-blocker-resistant.
Module · Analytics & CRO
Engineered measurement that survives privacy restrictions.
Server-side tracking, multi-touch attribution, predictive LTV and churn models, AI decision support, BI dashboards on Looker and Metabase. Plus the experimentation program that converts measurement into actual revenue lift — not just dashboard updates.
What's engineered
Six engineered capabilities.
Data Warehousing
BigQuery or Snowflake warehouses engineered with proper event taxonomy, data quality monitoring, and the schema architecture that makes downstream attribution and modeling possible.
Multi-Touch Attribution
Engineered attribution models that reflect the full customer journey — not last-click, not first-click, but mathematical credit allocation across the touchpoints that actually closed revenue.
Predictive LTV & Churn
Customer lifetime value forecasting and churn probability models engineered against your warehouse data. Acquisition decisions made on what customers are mathematically worth, not what they cost.
BI Dashboards
Looker, Metabase, or Tableau dashboards engineered against the warehouse — the right cadence for the right audiences. Executives see executive metrics; operators see operator metrics. No spreadsheet sprawl.
CRO & Experimentation
Engineered A/B testing program — hypothesis generation, statistical rigor, server-side variant assignment, mathematical analysis. Conversion rate optimization that compounds, not lifts that disappear next quarter.
Why this matters now
Beyond GA4 default reports.
Most teams operate on GA4 default reports plus a couple of dashboard views — measurement just sufficient to feel measured, not measurement engineered to inform decisions. We architect the full closed-loop layer: server-side capture, warehouse-grade storage, attribution that reflects real journeys, models that predict instead of describe.
Engineered for compounding decision velocity.
When time-to-decision drops from weeks to hours, the rest of the operation moves at a different cadence. A growth team that can answer "did this campaign work" in real-time, not next quarter, runs more experiments, ships more iterations, compounds more learning. The analytics layer is what determines that velocity.
Mathematical certainty, not vibes.
Most CRO programs run tests that don't reach statistical significance and call them wins. We engineer experimentation with proper sample size calculation, sequential testing where appropriate, and clear stopping rules. When we report a lift, the math actually says it's a lift — not last quarter's noise dressed up as signal.
Tooling deployed
The stack we engineer on.
Tracking infrastructure: GTM Server (self-hosted on GCP), Stape, Segment, custom Cloudflare Worker capture, GA4 with proper event taxonomy.
Data warehousing: BigQuery, Snowflake, Postgres for OLTP needs, dbt for transformation pipelines, Fivetran or Airbyte for source connectors.
Attribution & modeling: Custom multi-touch models, Robyn for MMM, Python ML pipelines for LTV and churn prediction.
BI & visualization: Looker, Metabase (self-hosted), Tableau, custom dashboards on Next.js + Recharts where flexibility matters.
Experimentation: GrowthBook, PostHog, Optimizely, custom server-side testing infrastructure for sensitive experiments.
Engineer your measurement layer
Get an engineered analytics architecture for your stack.
Discovery Audit includes a full analytics review — tracking accuracy, attribution gaps, warehouse state, model quality, and the highest-impact engineering moves to restore mathematical certainty in measurement. Six weeks, fixed scope, your roadmap.