Experimentation Framework
A system that structures marketing tests from idea selection to the scaling decision - so results stay trustworthy as AI multiplies test volume.
Experimentation Framework
An experimentation framework is the structure that carries marketing tests from idea to decision: ideas are prioritized by potential upside, initial confidence and cost to run; every test changes a single variable against a real control group; and every result ends in exactly one decision - scale, stop, or iterate.
Why it matters
AI made starting tests cheap - but not the trust in results. Without a framework, teams confuse test volume with learning: 41 tests a quarter, none of which changes a real decision. A clean framework runs to a pre-registered sample size - choosing it after looking at the data means searching for confirmation instead of measuring.
The TYS view
The machine owns the work (setup, variants, QA, first-draft reports); the human owns the judgment: hypothesis, metric definition, whether a result is real, the scaling decision. Structure before acceleration.