Modelling that which is Important
Former US Secretary of Defense Robert McNamara reportedly said:
"We have to find a way of making the important measurable, instead of making the measurable important."
I heard this one morning on Radio 4's "Thought for the Day", and have never tracked down a reliable source, but I have probably quoted this more than almost anything else. I think it is a remarkable, and important, observation.
As marketers, we must strive to model that which is important, rather than making important that which we can conveniently model.
Traditional so-called "response" models do not model response all. They most commonly model the (conditional) probability of purchase, given treatment:
P (purchase | treatment)
But this isn't what affects the return on (marketing) spend. That is affected by the change in purchase probability resulting form a treatment:
P (purchase | treatment) - P (purchase | no treatment).
That is what we model if we want to target so as to maximize expected ROI.
Such models go by various names. Portrait Software (for whom I work), calls them uplift models, and used to call them differential response models. Others call them various incremental impact models, net response models, true response models, true lift models, and various other combinations of these, and other words. But they are all the same.
Uplift models predict that which is important. Traditional "response" models make important that which is easy to model.
Footnote: If you haven't seen Errol Morris's biography of McNamara, The Fog of War: Eleven Lessons of Robert S. McNamara, consider doing so. It's extraordinary. Frightening, compelling, and revealing.