Allocation · Discipline 3 of 4
Where should the next dollar go?
How should scarce marketing resources be allocated? Marginal rather than average returns, channel interaction, marketing mix modeling, and budget decisions made under real uncertainty.
Where this sits
Each rung depends on the one below it. This discipline answers the highlighted one.
01
Observe
What happened?
02
Describe
Where did conversions appear to come from?
03
Estimate
What changed because of marketing?
04
Explain
How did channels and outside factors contribute?
05
Decide
What should happen next?
06
Learn
What uncertainty should we reduce next?
Nothing written here yet
The roadmap below is real, but this discipline has no finished work. Rather than publish thin pages to fill it out, depth is going elsewhere first.
Planned
The shape of this discipline, published as a roadmap. These are titles and scope, not finished work. They are here so you can see where this is going, not to suggest it has arrived.
Marginal Return, Not Average Return
The channel with the best historical ROI is frequently the one closest to saturation. Why budget decisions need the slope, not the level.
Paid Search and SEO Are Not Independent
Branded paid search often captures demand that organic would have captured anyway. Designing the test that measures total branded conversions rather than paid ones.
What a Marketing Mix Model Can and Cannot Do
MMM is a good allocation tool and a weak causal one. What it needs to be trustworthy, and why Bayesian priors do not create identification.
Calibrating an MMM to Experiments
The practical bridge between slow causal evidence and fast allocation models: using experimental estimates as priors and as validation.
Adstock, Saturation, and Honest Response Curves
Lagged effects and diminishing returns are where most mix models smuggle in their conclusions. How to tell a fitted curve from an identified one.