Chapter 01
Go-to-market

Building growth systems from the ground up.

This is not a CV. It is the three things I have done again and again, in different companies and markets, and what each one really takes when you do it properly.

01 / Team systems

From a handful of people to teams in the tens.

I have built complete marketing organizations from scratch, more than once: hiring the first people, then growing single-digit teams into departments of tens of people that I directed and managed.

The hiring is only half of it. The other half is the org design: departments by channel, ad operations and analytics as disciplines of their own, plus the stack, the reporting and the operating rhythm that let a growing team move fast without losing control.

In practice

Hiring
Directing and managing
Team design
Channel departments
Ad operations
Analytics
Training

02 / Market entry

Launching where no operating playbook exists.

I have led around 15 market launches end to end: research and go-to-market planning, team setup and training, technology and product coordination, first tests, technical launch, soft launch, then scaling and continuous optimization.

Not all of them were new products. Some were whole new product categories, and many were in countries I had never operated in before, each with its own regulation, channels and audience. The playbook was written on the ground every time, never copied.

It has always fascinated me to start something in a part of the world I didn't know yet and watch it grow and scale. My way of building the team stays the same: local experts who know the market inside out, working alongside veterans who bring the know-how.

03 / Performance science

Performance marketing, run as data science.

For me performance marketing is an experimental discipline, not traffic buying. The core is machine-learning models that predict customer lifetime value (LTV) early, so budgets follow the value a campaign will bring over time rather than the cost of the first conversion.

Around those models sit tools that measure engagement and user satisfaction, cohort analysis, statistical A/B testing and automated experimentation, all running on a proper analytics infrastructure. Together they drive return on ad spend (ROAS) optimization and decide where the next dollar goes, and they keep re-deciding as the market moves.

In practice

Predictive LTV models
Machine learning
Engagement and satisfaction
Cohort analysis
A/B testing
ROAS optimization
Analytics infrastructure

Where the work happened

Employers and clients across my career: Google, AIG, Entain, evoke plc, Hard Rock Digital, Virgin Bet, LiveScore, Casumo, GSN Games, Buff, Lin Health, Vonder, Messenger Plus!, Reimage.