If your measurement strategy delays decisions, it's broken | MarTech

"Measurement should create action, not delay it Measurement exists to inform decisions, not to absolve teams of responsibility for making them. That sounds obvious, but it's not how many organizations behave in practice. When attribution says one thing, an incrementality test says another and a model points slightly elsewhere, the instinct is to pause, ask for more analysis or wait for cleaner data."

"Too often, those challenges prevent teams from getting started. And even when tests are run, they can prevent teams from acting on the results. Common phrases include opportunity cost, confidence intervals and results just represent a moment in time. All of those are reasonable concerns. But the most significant risk isn't that the tests aren't perfect. It's that nothing changes in the marketing program as a result, even if that means rerunning a test that's more likely to get a cleaner read."

"Disagreement between measurement approaches is typical. Treating it as a reason to do nothing is the mistake. At some point, teams still have to decide what bet they're willing to make with imperfect information. Pretending that any measurement playbook will remove uncertainty entirely is a fallacy. Incrementality tests are the most powerful tool in a marketer's toolkit, but they aren't without challenges. Too often, those challenges prevent teams from getting started. And even when tests are run, they can prevent teams from acting on the results."

Marketing measurement has evolved; attribution alone no longer suffices and incrementality testing plus media mix modeling (MMM) are essential. Organizations often stall because measurement results disagree or data are imperfect, causing paralysis and demands for more analysis. Measurement should drive decisions: teams must choose a defensible bet with imperfect information and iterate. Incrementality tests are powerful but practically limited, and the greatest risk is inaction despite test results. MMM yields correlation-based insights that feel less precise than attribution, yet models can still guide spend shifts and forecasting when used as directional evidence.

Read at MarTech