Our methodology
Methodology
How we separate effects from correlations — and what that gives you.
Built from live B2B mandates, not from generic AI demos. The monitoring stack, the diagnostic logic and the impact-measurement approach were developed across live engagements with real client constraints. The work does not begin where the demos end — it began there years ago, and continues to be refined by what survives commercial reality.
Effects, not correlations
We do not report correlations and call them effects. The discipline of separating one from the other sits at the centre of how we work.
Difference-in-differences, where it fits
Where the data supports a clean comparison — sufficient time window, structurally comparable cases, identifiable control group — we measure intervention impact using a difference-in-differences design adapted for B2B visibility and content work.
Honest inference, where it does not
Where a clean causal test is not available, we say so, and we work with the strongest inference the situation allows. Methodological honesty about what can and cannot be proven is the precondition for everything else we do.
Evidence before scale
Visible early evidence on what is working — before you commit budget to scaling it.
Evidence before sunk cost
Visible early evidence on what is not working — before it quietly consumes your budget.
Defensible in front of finance
A line of reasoning each marketing investment can defend in front of a finance counterpart.