Why You Can't Just "Throw an LLM" at Marketing Incrementality | Haus скачать в хорошем качестве

Why You Can't Just "Throw an LLM" at Marketing Incrementality | Haus

Think AI can automatically tell you what's actually driving your sales? Think again. Haus VP of Product Dylan Daniels breaks down why no machine learning model or LLM — on its own — can solve the incrementality problem. The reason comes down to a fundamental statistical concept: exogenous variation. Without deliberately inducing variation in your marketing through controlled experiments, no amount of data analysis can tell you what's actually causing your results. Consider the Black Friday problem: If you're scaling, for example, Meta and TV spend simultaneously every year, no model can tell you from observational data alone which channel is driving your sales. That's not an AI limitation — it's a mathematical one. What actually works: Holdout experiments that sample the counterfactual (what happens when you don't run marketing) Designed tests that create the exogenous variation needed for causal inference Incrementality measurement that goes beyond correlation to true attribution Haus is the causal marketing platform built on experimentation — not black-box models — so you can make faster, more confident decisions about where to invest. 📊 Learn more: haus.io