Most marketing measurement is sophisticated-looking fiction. Multi-touch attribution confuses correlation with causation. Last-click models systematically overvalue bottom-funnel channels. The result: billions in ad spend allocated on the basis of numbers that are not merely imprecise but structurally wrong. This series applies the tools of causal inference — directed acyclic graphs, Bayesian hierarchical models, geo-based incrementality experiments, and Marketing Mix Modeling — to the problem of understanding what marketing actually does. These are the methods that separate organizations running on measurement theater from those making decisions grounded in causal evidence.
1 article in this series
Your bidding algorithm gets better every quarter. Your brand gets weaker every year. This is not a coincidence — it's Goodhart's Law applied to marketing, and the compounding damage is invisible until it's too late.