Engineering™ · Cloud & Infrastructure

Measurement Infrastructure

We build unified measurement infrastructure to determine what actually drives revenue with mathematical certainty.

Unified Data Layer · Google Analytics 4 · Meta Conversions API · Attribution modeling · Consent Mode v2. End-to-end technical event architecture feeding ad algorithms and executive dashboards with clean conversion data.

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Scope

Unified Data Layer Architecture · Google Analytics 4 Technical Audit · Multi-Touch Attribution Modeling · Consent Mode v2 Compliance

Estimated Timeline

3–6 weeks

Platforms

Google Tag Manager · Google Analytics 4 · Meta CAPI · BigQuery Export

Target Fit

Is this for your company?

This service is for you if

✓Meta Ads claims 150 conversions but your eCommerce backend only recorded 90 actual paid orders.
✓You have not implemented Consent Mode v2 and are losing critical ad campaign signal across global regions.
✓Conversion tags trigger on simple button clicks rather than verified server-side order confirmations.
✓You lack a unified data layer, causing ad networks to record mismatched transaction values and currencies.

You probably do not need it if

✕You run zero paid advertising and your web traffic is purely organic with no transactional measurement needs.
✕You only require basic off-the-shelf automated plugins provided by standard Shopify or WordPress setups.

Problem Space

What we solve

Canonical Data Layer Architecture

Standardizing ecommerce events (view_item, add_to_cart, purchase) into a unified data contract powering all marketing tools.

Technical Google Analytics 4 Audit & Fix

Eliminating duplicate transactions, filtering internal traffic, setting cross-domain tracking, and enabling BigQuery export.

Full Consent Mode v2 Compliance

Deploying certified cookie consent banners and configuring dynamic tag behaviors to preserve modeled conversions.

Multi-Touch Attribution Modeling

Evaluating the true mathematical contribution of every paid channel across complex multi-visit customer journeys.

Engineering Process

How it works

01 — Diagnose

Tracking Discrepancy & Forensic Audit

We compare pixel conversion counts against true backend database orders to calculate signal leakage percentage.

02 — Design

Data Layer Specification & Tag Plan

We document the event dictionary, canonical variables (transaction_id, value, currency), and tag firing rules.

03 — Build

GTM Engineering & BigQuery Export Setup

We implement the data layer on site, configure Google Tag Manager containers, and activate BigQuery cloud exports.

04 — Launch

Ad Algorithm Verification & QA

We verify Event Match Quality in Meta and Google Ads, ensure deduplication, and deliver executive reconciliation dashboards.

Deliverables

What we deliver

Upon completion you will have

✓Technical specification document for your corporate Data Layer.

✓Audited, sanitized Google Tag Manager container with zero redundant tags.

✓Professionally configured GA4 property with daily BigQuery raw data streaming.

✓Google-certified Consent Mode v2 cookie banner and compliance architecture.

✓Reconciliation dashboard comparing ad network reported sales vs. cash in bank.

Delivery Plan

Implementation Phases

Tiempo típico de proyecto:3–6 weeks

Week 1Phase 1

Forensic Tracking Audit

Auditing discrepancies between ad network reports, GA4, and core database revenue.

Weeks 2–3Phase 2

Data Layer & Consent Mode Architecture

Event dictionary specification and Consent Mode v2 compliance blueprinting.

Weeks 4–5Phase 3

Technical Implementation & BigQuery Streaming

Site coding, GTM tag structuring, and native BigQuery raw event streaming activation.

Week 6Phase 4

Algorithm Calibration & Team Handover

Purchase deduplication QA, ad network signal verification, and marketing team training.

Pricing Guidance

Estimated Investment

Target Investment

USD 3,600

Includes forensic audit, custom Data Layer specification, GTM/GA4 configuration, Consent Mode v2, and BigQuery export.

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Real-World Proof

Impact Case Study

From 35% Conversion Tracking Blindness to 99% Reconciliation with ERP Sales

Initial problem

D2C eCommerce brand spending $60K/month on Meta and Google Ads losing 35% of tracking data to ad blockers and browser privacy filters, blindfolding ad algorithms.

Technical intervention

Deployment of a canonical Data Layer with unique transaction IDs, Consent Mode v2, and continuous BigQuery streaming.

Outcome achieved

Reconciliation between reported sales and bank deposits reached 99.2%, enabling ad algorithms to optimize on true conversion data and lowering blended CPA by 21%.

Clarifications

Frequently asked questions

Why do we need this if we already have Google Tag Manager installed?

GTM is merely a container. If your underlying website code fails to expose structured data via a canonical Data Layer with transaction IDs and clean values, tags receive incomplete or duplicate data, miscalibrating ad bidding algorithms.

What is Consent Mode v2 and why is it mandatory?

It is Google's mandatory privacy framework. Without Consent Mode v2 properly configured, Google Ads disables remarketing audiences and significantly degrades conversion modeling for paid campaigns.

Why stream GA4 data directly into BigQuery?

Because standard GA4 interface reports apply data sampling and privacy thresholds that hide granular customer journey data. Streaming raw events to BigQuery gives you unconstrained ownership to compute true customer LTV and channel attribution.

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