Nearly a decade in marketing, and the journey has been a deliberate one — starting in branding and creative, moving through SEO and performance marketing, and arriving in recent years at marketing measurement, causal inference, and data science. Having sat on every side of the marketing table, I've developed an appreciation for both the creative and business dimensions of advertising, alongside the quantitative capability to measure its true impact. That combination — understanding what makes a campaign compelling and being able to rigorously evaluate whether it worked — is what I find most useful to the clients and teams I work with. Most of my current work is focused on making marketing measurement more rigorous — closing the gap between what gets credited and what actually drove the result. That means moving beyond standard attribution and into methods that can isolate true causal impact: Marketing Mix Modelling, geo-experimentation, incrementality testing, and causal inference frameworks applied to real media budgets. The goal is always the same: transform the data into something that leads to a clear, confident business decision. I've managed over $1M in monthly ad spend across Google, Meta, LinkedIn, and Bing — across different sectors and geographies. That hands-on experience across the full performance marketing stack is what keeps the measurement work grounded in commercial reality. Core areas: Marketing Measurement & Effectiveness — MMM (Meridian, Robyn, PyMC, LightweightMMM), media mix optimisation, budget allocation Causal Inference & Experimentation — GeoLift, CausalImpact, CausalPy, DiD, Synthetic Controls, ITSA, RDD Performance Marketing — Google Ads, Meta, Bing, LinkedIn, YouTube — strategy through to execution Analytics & BI — Python, SQL, BigQuery, Looker Studio, Power BI I write regularly on marketing measurement and growth data science — the gap between what the industry claims to measure and what it actually measures is still wide, and worth talking about honestly.