Nov 17, 2025
CMO playbook: How to handle these 9 CFO concerns about incrementality
Pranav Piyush
,
Co-founder, CEO
Connect with Pranav
Nine of your CFO’s toughest questions about incrementality, answered
Every marketing dollar is a test, whether you measure it or not.
CFOs want proof. Marketers want confidence. Incrementality delivers both.
This field guide is your shared compass for when finance leaders ask about marketing measurement. It answers those questions in the only framing that matters: dollars and impact.
Think of this as your handbook for translating marketing’s cause-and-effect into finance certainty.
How incrementality leads to proof (in finance terms)
Incrementality measures marketing’s true financial impact: the revenue that only exists because of marketing. It separates what marketing caused from what would’ve happened anyway.
In finance terms, it isolates marketing’s net contribution. Instead of counting clicks and impressions or letting ad platforms like Meta or Google be the judge of impact, incrementality quantifies how much marketing actually moved the top line.
Think of it as the marketing equivalent of an audit: it proves which of your spend generates returns and which isn’t pulling its weight (at least for now).
Incrementality is measured through controlled experiments and statistical modeling; it’s a mix of randomized tests (causation) and Marketing Mix Modeling, or MMM, which estimates outcomes across channels (correlation).
Top CMOs now start every board prep with their incrementality readout. Because accurate measurement leads to proof and better financial planning.
That proof unlocks a shared confidence and a common language for marketing and finance:
“We’re not talking about ad clicks or views. We’re talking about cause and effect in dollars. Say sales went up by $1M last quarter. Traditional attribution might credit all of it to marketing. Incrementality shows us maybe $600K would’ve happened anyway, but $400K was caused by our campaigns. That’s the true return on our spend.”
The 9 most common CFO concerns about incrementality — answered
What you’ll hear: “I want precision.”
Answer #1: You’re right. But we want decision-level precision, not click-level illusion.
Click data feels precise, but it’s precise about the wrong thing. Click data counts events. But counting clicks is not the same as measuring causal impact. It’s like weighing a thermometer instead of taking your temperature.
Incrementality shows casual impact, not events that don’t necessarily cause impact. Instead of, “This ad was clicked on 327 times,” you get: “Your video investment last quarter drove a +14% incremental lift in new revenue.”
Answer #2: Click-level precision is unreliable because it obscures reality.
Clicks fail to account for the dynamic and nuanced nature of marketing and buyers’ journeys. Incrementality isn’t perfect to the decimal point. Rather, it shows your range of reality.
Those real results give us the confidence to estimate and allocate our next dollar: “If we add $500K to YouTube this year, we’re likely to see a 6–10% lift in quarterly revenue, or about about $600K to $1M on a $10M base.”
What you’ll hear: “The company isn’t mature enough for this kind of measurement.”
Answer #1: All it takes is the maturity to make smart decisions.
No 5-person data science team needed. You just have to be able to run clean tests, plus have good creative and the discipline to act fast. Small brands can start with a few lightweight geo or audience holdouts or pulse ups and see results in 60 days.
Answer #2: Iteration speed matters more than size.
Start simple, learn fast and scale what works. For a smaller brand, you need to aim for higher lift in your test designs. You’re not trying to optimize by 10%, you’re trying to grow 10x. This type of measurement is built for that type of growth.
What you’ll hear: “I can’t afford to fail and waste money.”
Answer #1: You might already be wasting money with unproven assumptions.
Most marketing budgets leak 20% or more into channels that look good in attribution platforms but drive zero incremental sales. A well-structured test might “cost” 5% of spend, but it prevents 30% from being misallocated for months.
Answer #2: Testing is a budget-constrained driver of value.
Finance pays auditors. Product pays QA. Ops pays for safety stock. Testing is marketing’s version of that. This is a necessary check on the marketing function, that will pay for itself many times over in future value.
Answer #3: Smart testing doesn’t always mean new spend.
Many incrementality tests use existing budgets through holdouts or pulse-ups, reducing spend to expose waste, or increasing it briefly to validate opportunity.
Tip: Once you save the business money with a test, propose using those savings for larger experiments that have the potential to drive even more growth and savings.
Answer #4: About 80% of your tests will fail.
This is why we set a constrained budget around testing. As with any scientific method or experimentation, not every single trial will result in success. But “failed” tests are just as informational as successful ones. They tell you what not to do and allow you to move on to the next experiment.
What you’ll hear: “We already get what we need from our click attribution.”
Answer #1: Click-based or multi-touch attribution (MTA) is useful to observe customer paths. It’s not great proof of ROI.
Attribution only counts the touchpoints that have clicks, which means it misses entire parts of the journey. What about all the channels that don’t generate clicks, like CTV, YouTube, Instagram, or TikTok? Plus everything beyond digital?
Clicks map a visible journey, but they will never show whether those touchpoints are the reason customers came.
Answer #2: Attribution can be part of your measurement stack. Just don’t use it for “attribution.”
Many teams “triangulate” measurement using MTA, MMM, and incrementality. That’s fine, as long as each tool knows its job. MTA should be used for observation, not proof. Attribution, by definition, is supposed to mean cause and effect. Clicks alone can’t prove either.
If removing a click wouldn’t change the outcome, was it really impactful? Incrementality answers that question.
Answer #3: In-platform measurement can create a false sense of certainty.
Meta, Google, and other ad platforms each have their own version of impact, and it often favors them. We don’t let the platforms grade their own homework. Incrementality is how we verify what’s actually driving business results, independent of any platform’s story.
What you’ll hear: “Testing is too slow.”
Answer #1: Today’s models refresh with daily or weekly data, but confidence takes time.
Today’s APIs constantly feed in new data, and machine learning refreshes models regularly. Early signals can start to emerge in days. However, statistical significance often requires a few weeks of consistent data.
Acting too early is a risk. A test that takes four weeks but delivers confident proof is faster and more valuable than months of guesswork based on incomplete conclusions.
Answer #2: The biggest waste of time is not testing.
Every day without a controlled readout is another day of blind spend. Testing speeds up your time-to-confidence.
What you’ll hear: “I can’t convince other execs.”
Answer #1: Start with a money-saving win.
Run one “obvious” test, like branded search. When you pulse it down and it saves real dollars, you’ll have proof.
Tip: Once you prove you’ve saved the business money, propose using that money for bigger experiments that have potential to drive even more growth and savings.
Answer #2: Speak their language.
Translate results into finance terms: reallocation, payback period. When measurement becomes about optimizing your budget, execs will listen.
Answer #3: Frame this as an opportunity to accelerate growth with your existing budget.
If you test and claw back 10–25% of your budget, then redeploy it to channels and campaigns that are incremental, you just spiked your growth with the same resources.
What you’ll hear: “We don’t need both MMM and incrementality.”
Answer #1: They answer different questions.
MMM gives you a portfolio view of how channels work together across time. Incrementality gives you proof at the tactical level. Estimate, then validate.
Answer #2: Together they form a constant feedback loop of truth.
MMM tells you where to look. Incrementality confirms if it’s real. Finance gets both the confident estimate and the real-world truth.
What you’ll hear: “I can’t afford to turn down a channel and lose three months of sales.”
Answer #1: You don’t have to lose out! Just pulse up, instead.
Instead of turning off spend, start with a limited, but conclusive, pulse-up test in a specific set of geographies.
Answer #2: Start where the risk is lowest.
Prove the testing method on smaller things first. Specific creative variants, low-funnel channels. Then scale it up. Testing should teach, not terrify.
What you’ll hear: “The test looked good, but I’m not seeing results in the business.”
Answer #1: Tests are limited by design.
Confident results in a test phase likely won't show up in the overall business until it's been fully implemented at scale. Look at the test as giving you the confidence to act.
Incrementality captures short-term causal lift in a specific geo or audience segment. To see the business impact, go from test results to scaled implementation and then back to your MMM and contribution margin. That’s how single tests turn into strategy.
Answer #2: Right-size the test, or risk false confidence.
A flawed design can create “false positives” that overstate marketing’s impact, or “false negatives” that hide it. Small samples, short timeframes, or weak controls distort reality in both directions.
When tests are properly designed, they reveal the true impact of marketing — not just in isolated lift, but in downstream business outcomes: contribution margin, overall growth, and validation through MMM.
A final note
Every marketing dollar is already an experiment. The only question is whether you evaluate it with sound measurement.
Without incrementality, that experimentation is uncontrolled and inconclusive. With incrementality, experiments become proof: verified cause and effect that both marketing and finance can trust and use to forecast future investment.
We all want the same things: growth we can trust and proof we can act on. This guide was created to bring marketing and finance to a shared understanding, and a shared path forward.
