Marketing decisions need measurement that holds up

Most analytics implementations begin with installing a tool and end with a dashboard nobody trusts. The failure is not the tool: it is that no one wrote down which decisions the data was supposed to inform, so everything was collected and nothing was validated.

We start from the decision. That determines the events, the properties they carry, the identity model and the retention needed. Implementing less, deliberately, produces data that can be reconciled and defended when a number looks surprising.

A test needs a sample size determined by baseline conversion rate and the smallest effect worth detecting. Many published tests never reach it, get stopped when the line looks good, and report a difference that is ordinary variation. Repeatedly checking a running test compounds the problem, because with enough looks almost any comparison eventually crosses a threshold.

We calculate the required sample before launch, fix the duration to whole business cycles, and analyse once at the end. When the available traffic cannot support a valid test, that is stated plainly and the change is evaluated another way rather than dressed up with statistics that do not apply.

Last click undervalues everything that created demand; first click ignores what closed it; multi-touch models distribute credit using assumptions that are rarely examined. None of them is the truth, and treating a model output as fact leads to defunding channels that were doing real work.

We present attribution alongside its assumptions, use consistent definitions across channels, and prefer held-out geographic or audience tests when a budget decision is large enough to justify the effort. Knowing which numbers are measured and which are modelled is more valuable than a single confident chart.