Attribution is where the data problem and the trust problem collide. The model can be correct and still lose the client, because the client does not believe the number. The operating layer failure in AdTech is almost never the algorithm. It is the gap between a defensible measurement result and a sales, customer success, and product organization that can explain it, defend it, and sell it under scrutiny.
We know walled garden measurement, multi-touch and last-touch attribution, incrementality, identity resolution, clean room data, and the operational realities of legacy platform migration. We have built the regression models that isolate what actually moved a result, and we have written the documentation that turns a statistical framework into language an executive, a client, and a regulator will accept.
Proof in this industry
Case study. As fractional Head of Measurement Products, converted the largest skeptical client who was preparing to disengage, made the methodology documentation the standard protocol across sales, customer success, and product, and compressed sales report production from days to minutes through custom GPTs.
Career record. SVP roles at Comscore directing cross-channel ad-effectiveness studies, co-founder of the AdAdvisor privacy-friendly targeting division at TARGUSinfo, and named inventor on US Patent 10,475,047 for ad targeting.