• by Rebecca Visconti , Yesterday

If you are still treating incrementality testing as an occasional exercise, you are leaving money and growth on the table.

I grew up in the era of multitouch attribution (MTA), a once elegant, data-rich way to assign credit across touchpoints. But the foundation that made it work has eroded under signal loss, platform walls, and privacy rules. Leaders across industries need a new way to validate impact, cut through the noise and make confident decisions.

That is where incrementality testing comes in. Here is a four-step framework leaders can use to replace guesswork with proof, build a future-ready measurement system, and gain a competitive edge.

Understand why the old map no longer works. MTA thrived when we could stitch together stable, person-level data across platforms. That’s  no longer possible. Privacy regulations, cookie deprecation, walled garden restrictions, and device fragmentation have broken the chain of visibility.

Attribution models now run on incomplete maps, which increases the risk of over-valuing certain channels and misallocating budgets. What once felt like precise truth is now closer to an educated guess.

This is not just a marketing problem, but a universal measurement issue. Any team relying on fragmented digital signals, from sales enablement to ecommerce, faces the same reality: When the data is incomplete, decision confidence drops.

Shift from credit assignment to causal proof. Instead of asking, “Which touchpoint gets the win?” the better question is, “What actually moved the needle compared to doing nothing?”

Relying solely on attribution today is like navigating with a map missing half the roads. Incrementality testing offers a more reliable approach by isolating the true causal impact of a tactic and filtering out noise from overlapping campaigns, seasonality, and organic demand.

This method works across industries. Whether testing a new store layout, piloting a product line, or rolling out a customer service change, incrementality shows the real lift created.

Make incrementality an always on practice. Incrementality testing is both a safeguard and a growth driver. It protects budgets from overnvestment in low-impact tactics, reveals high-ROI opportunities that attribution may miss, and creates a clean dataset to inform forecasting and modeling.

Too many organizations treat it as a periodic check-in. The real advantage comes from making it always-on, running continuously to detect shifts early, adjust quickly, and build a library of learnings by channel, audience, and season. The more cycles you run, the sharper your decision-making becomes.

Use incrementality to power predictive models. Incrementality testing does not replace predictive modeling. It strengthens it.

Bayesian media mix models (MMM) thrive on prior knowledge. Feeding them incrementality data gives them a grounded starting point, improving their accuracy even when datasets are noisy or incomplete. The combination of causal insight and predictive modeling gives leaders the confidence to plan with precision.

The Payoff for Leaders

The leaders who will win the next decade are those who treat measurement as a living system, continually informed by experimentation and adaptive modeling. Attribution will still play a role, but it will be one piece of a larger framework grounded in incrementality.

If your organization is not building this muscle now, you are not just behind. You are guessing. And in today’s environment, guessing is too expensive. The organizations that move first will set the standard that others struggle to match.