Media measurement and attribution
Whether your advertising caused anything, and the three families of method — geo experiments, mixed modelling and attribution — that claim to tell you.
Advertising measurement is the hardest applied causal problem most companies have, because the thing you want to know — what would have happened without the spend — is never observed, and every convenient number available in a dashboard is a correlation dressed as an answer. The terms here separate the ones that support a budget decision from the ones that do not.
Incrementality is the quantity that matters: the conversions that would not have happened anyway. A platform reporting conversions it attributed to itself is not measuring that, and the gap between the two is routinely a factor of two or more on retargeting and branded search. Establishing it takes an experiment — usually a geo experiment — or a model calibrated against one.
Marketing mix modelling is the other family, and its two hardest parameters have entries of their own: adstock, because advertising seen today converts next week, and diminishing returns, because the tenth impression is worth less than the first. Both are shapes fitted from data, both are weakly identified, and both are why an MMM calibrated by experiment beats one that is not.
See our geo testing and marketing mix modelling work.
Terms in this group
Geo Testing at Bell Statistics
We measure what advertising actually caused, with geo experiments and mixed models that are calibrated against them rather than against a dashboard. See how we work.