Selected Case Studies

Real examples of how I’ve diagnosed and fixed the systems behind marketing performance — including tracking, measurement, signal architecture, and marketing infrastructure.

Tracking & Signal Architecture Rebuild

Tracking System Rebuild for SaaS Company

The Problem

  • Attribution didn't match across platforms

  • GA4 was inconsistent

  • Conversion signals were incomplete

The team couldn't confidently answer which campaigns were driving revenue.

What I Found

  • No server-side tracking

  • Missing offline conversions

  • Inconsistent event structure

  • Data misalignment across Meta, Google, and Analytics

This wasn't a campaign issue. The tracking and signal architecture was broken.

What I Built

  • Rebuilt GTM (web + server-side)

  • Implemented Meta CAPI and Google conversion tracking

  • Created structured funnel event system

  • Integrated CRM data into ad platforms

  • Standardized UTM and tracking framework

The Outcome

  • Reliable attribution across platforms

  • Recovered lost tracking data

  • Stronger signals for ad optimization

The team moved from guessing to trusting their data.

Full-Funnel Performance System Rebuild

Full-Funnel Marketing System Rebuild

The Problem

Campaigns were running across multiple channels, but nothing was aligned.

  • Data was inconsistent

  • Tracking was broken

There was no clear view of what was actually driving performance.

What I Found

This wasn't an ads problem.

It was a fragmented system:

  • Broken measurement infrastructure

  • Missing and inconsistent signals

  • No unified funnel strategy

  • Disconnected platforms producing conflicting insights

What I Built

  • Rebuilt tracking and measurement architecture

  • Created a unified cross-channel measurement framework

  • Mapped full-funnel behavior and conversion paths

  • Restructured paid acquisition strategy by funnel stage

  • Built dashboards and reporting for decision-making

The Outcome

A fully connected performance system.

  • Clear visibility across the funnel

  • More predictable campaign performance

  • Confident, data-driven budget decisions

Marketing Systems & AI Automation

Building a Structured Marketing System to Power AI & Content Execution

The Problem

  • Content was inconsistent across platforms

  • Messaging varied depending on who created it

  • AI outputs felt generic and off-brand

  • No centralized system for marketing strategy

Content quality was unpredictable and difficult to scale.

What I Found

  • No shared source of truth for messaging

  • Brand voice and strategy were not systematized

  • AI tools lacked structured inputs

  • No connection between strategy → content → execution

This wasn't an AI problem.

It was a systems problem.

What I Built

  • Centralized marketing knowledge base (markdown system)

  • Structured brand voice, content pillars, and messaging frameworks

  • AI-ready input system for consistent content generation

  • Repeatable workflows for content production

  • Automation layer for scalable execution (n8n, Claude, etc.)

The Outcome

  • Consistent messaging across all platforms

  • AI outputs aligned with brand and strategy

  • Faster, more repeatable content production

From ad hoc content → structured, scalable system

Creative-Level Performance System

Identifying Why Content Actually Work

The Problem

The team could see which creative performed well (paid ads & organic) — but not why.

  • Content, campaigns, and analytics were disconnected

  • No way to identify patterns or scale what worked

What I Found

The issue wasn't performance.

It was a lack of structure:

  • No way to analyze messaging or creative

  • No connection between content and outcomes

  • Data existed, but wasn't actionable

What I Built

  • Creative descriptor system (hook, angle, persona)

  • Content ID and UTM framework

  • GA4 restructuring for content-level analysis

  • Signal architecture tied to funnel behavior and revenue

The Outcome

Clear insight into:

  • Which messages resonate

  • Which hooks drive engagement

  • Which angles convert

The team could finally answer:

"Why did this ad win?"

First-Party Data & CDP System

Rebuilding First-Party Data & Tracking Infrastructure

The Problem

Conversion data was inconsistent across platforms.

  • Events didn't match

  • Signals were low quality

  • Attribution couldn't be trusted

What I Found

The issue was deeper than tracking.

The entire event and data system was fragmented:

  • Inconsistent event defintions

  • Duplicate and missing conversions

  • Misaligned browser and server-side data

  • Poor signal quality feeding ad platforms

What I Built

  • Unified event taxonomy across all platforms

  • CDP-based first-party data pipelines

  • Server-side + browser event alignment

  • Deduplication & signal standardization

  • Clean data routing to Meta, Google, and Analytics

The Outcome

  • Clean, consistent data across platforms

  • Stable attribution

  • Improved optimization performance

The system became scalable and privacy-resilient. Ready for a post-iOS26 world.

GA4 Audit & Restructure

Turning GA4 Into a Decision-Making Tool

The Problem

GA4 was installed, but not usable.

  • Data was inconsistent

  • Events lacked structure

  • Performance couldn't be tied to outcomes

What I Found

This wasn't a data problem.

It was a structure problem:

  • Unclear event definitions

  • No funnel visibility

  • Misaligned UTM tracking

  • No connection between marketing and outcomes

What I Built

  • Full GA4 audit & cleanup

  • Structured event and funnel framework

  • Aligned UTM and campaign tracking

  • Content-level performance tracking

  • Custom reporting for real analysis

The Outcome

GA4 became usable.

  • Clear funnel visibility

  • Reliable performance analysis

  • Confidence in what's driving results

HIPAA-Compliant Tracking System

Privacy-Safe Marketing System for Healthcare

The Problem

A healthcare company wanted to scale ads, but couldn't safely track performance.

  • Tracking was broken

  • Compliance risks were high

  • Traditional marketing strategies weren't allowed

What I Found

This wasn't just a tracking issue.

The system was incompatible with HIPAA:

  • PHI exposure risks

  • Blocked or restricted events

  • No compliant optimization signals

  • No reliable measurement framework

What I Built

  • HIPAA-compliant tracking architecture

  • De-identified behavioral event system

  • Privacy-safe CDP implementation

  • Compliant signal architecture for Meta & Google

  • Alternative optimization framework (non-identity based)

The Outcome

Restored tracking without violating compliance.

  • Platforms could optimize using safe signals

  • Performance improved

  • The system became scalable and legally sound

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