More measurement tools exist today than at any point in this industry’s history. More AI agents are being pitched into the stack than ever. And the distance between what a plan recommends and what actually gets bought hasn’t moved.
Jeremy Bloom, CEO of Marketecture Media, sits down with Bradley Keefer, CRO at Keen, to break down why more rigor hasn’t closed the gap, and what actually would.
What you’ll learn
- Why AI-native marketing measurement is different from a traditional MMM report, and where that distinction actually matters
- What incrementality testing gets right that attribution models miss, and where the two overlap
- Why scoring channels one at a time gets the split wrong even when every individual number is right
- How CMOs are actually using AI in marketing decision-making, versus how it’s being pitched
- Where AI genuinely helps in measurement, and where nobody can check its work
- Who’s accountable when an agent makes a call nobody can verify for eighteen months
- What marketing accountability actually requires
- The three-question diagnostic for whether a faster model produces a better decision, or just faster confirmation that nothing is going to change
The stack got more expensive. The outcome didn’t change.
Brands added a planning tool. Then forecasting. Then an incrementality vendor, and something to reconcile platform reporting against all of it. Each piece was built by a different company for a different job, running on a different cadence, with no handoff between them. You can pay for all of it and still walk into the October budget meeting with three answers that disagree.
That’s not a measurement gap. It’s a decision gap, and more rigor hasn’t closed it. The debate between MMM and multi-touch attribution is a symptom of the same problem: more methods, same unresolved question of what to spend next.
The next dollar isn’t the last dollar
The marginal ROI on a typical flighted plan sits below a dollar, meaning brands are spending past the point of profitable return in windows they’re already running. Optimized, it climbs several times higher. That gap is what it looks like when a good recommendation gets produced, seen, and not acted on, the same gap that keeps showing up when teams can’t demonstrate marketing ROI to finance.
Keen AI Cortex is the engine behind that gap closing: calibrated priors from real transactions and commercial context, combined with your own data, so one model covers attribution and incrementality across every tactic, without the two-quarter wait or the cost of going dark to get there.
