Patterson Consulting

I'm Ian Patterson. I build measurement infrastructure for marketing teams.

I spent a decade working across marketing, data, and technology. Starting in the hospitality industry, moving into social media and influencer marketing, then into full-stack engineering, and eventually leading data and technology as VP at Allied Global Marketing, a major entertainment marketing agency.

That non-linear path is the point. I've sat in the creative meetings and the engineering standups. I've presented attribution models to CMOs and debugged data layer implementations at midnight before a launch. I've built the dashboards that executives use to make budget decisions and the pipelines that feed them. Most consultants in this space come from one side: either the marketing strategy side or the data engineering side. I come from both, which means I can translate between the two in a way that's rare and genuinely useful.

In parallel with consulting, I run Tuna Melts My Heart, a major pet influencer brand built around Tuna, a Chiweenie with an exaggerated overbite and 2 million Instagram followers. It's a real business with multiple revenue streams (merchandise, brand partnerships, licensed content, and live events) and it serves as a live testing ground for everything I build.

The 2025 and 2026 Tuna calendars were produced using AI-generated imagery from fine-tuned FLUX models. 5,000 units sold, substantial profit, on a creative production cost of $400. That's not a case study I wrote. That's a case study I lived.

Based in Atlanta. Working with clients across the US and UK.

What I Believe

Measurement infrastructure is not a project. It’s a capability.

Most companies treat tracking and analytics as a one-time setup. Then the data drifts, the tracking breaks, and nobody notices until a budget decision goes wrong. I build systems that monitor themselves, document themselves, and get smarter over time.

AI should be infrastructure, not an afterthought.

Every layer of the stack I build has AI-native capabilities: automated campaign taxonomy, natural language reporting, semantic data enrichment. Not because AI is trendy, but because these functions are genuinely better when they’re embedded in the data pipeline rather than bolted on after the fact.

You should own your data and your methodology.

Platform-reported metrics serve the platform’s interests. Black-box attribution tools serve the vendor’s interests. Everything I build lives in your warehouse, runs on your infrastructure, and uses methodology you can inspect and understand. When the engagement ends, the system keeps running.