Speaking the Same Language: How Incrementality Is Changing Marketing and Finance Conversations

Why Finance Cares About Uncertainty

Moving beyond attribution theater to measurement that CFOs actually trust

Marketing and finance teams have historically operated with different success metrics, different planning horizons, and fundamentally different views on what constitutes proof. This misalignment has real costs: campaigns get cut during budget reviews, growth investments get delayed, and teams spend more time defending past decisions than planning future ones.

The gap isn’t new. What’s changed is that more companies now have a practical way to bridge it: incrementality testing. Over half of US brand and agency marketers used incrementality testing in 2025, according to EMARKETER and TransUnion data, and 36.2% plan to invest more in it over the next year.

But adoption numbers don’t tell the full story. The more interesting shift is how these tests are changing the conversations between marketing and finance—and what that means for organizations trying to prove marketing’s contribution to business outcomes.

The Attribution Theater Problem

Most marketing measurement relies on attribution: matching conversions to ad exposures based on user behavior. Click on an ad, buy the product, and that ad gets credit. It’s clean, it’s trackable, and it’s fundamentally misleading.

Attribution conflates correlation with causation. If someone clicks a branded search ad and then purchases, did the ad cause the purchase? Or would that person have bought anyway, since they were already searching for the brand by name?

Facebook might report a 3.7x ROAS on a campaign. That number represents all purchases made by people who saw or clicked the ads. It doesn’t represent purchases that happened because of the ads—the purchases that wouldn’t have occurred without the advertising spend.

Finance teams understand this distinction intuitively. When they ask “what’s the ROI on this campaign,” they’re asking a causal question: what did this investment cause to happen? Attribution models answer a different question entirely: what did we track among people who saw our ads?

This gap creates what could be called attribution theater—the presentation of correlation metrics as if they prove causation. Marketing teams report ROAS numbers with confidence intervals, build elaborate dashboards, and forecast based on platform-reported metrics. Finance teams nod along but remain skeptical, knowing these numbers tend to overstate impact.

The result is chronic mistrust. Marketing can’t prove their claims. Finance can’t validate investments. Both sides retreat to their corners, frustrated.

What Incrementality Actually Measures

Incrementality testing flips the measurement approach. Instead of tracking who converted after seeing ads, it measures what wouldn’t have happened without the ads.

The methodology resembles clinical drug trials. Split a comparable population into test and control groups. Show ads to the test group. Show nothing (or different ads) to the control group. Measure the difference in outcomes.

If the test group generates 1,250 purchases and the control group generates 1,000 purchases, the campaign drove 250 incremental purchases—a 25% lift. That’s what the advertising caused. Everything else would have happened organically.

Google recently lowered the minimum budget for incrementality tests to $5,000, down from previous thresholds approaching $100,000. The platform uses Bayesian statistical methodology, which requires less data than traditional frequentist approaches. This makes causal measurement accessible to mid-market advertisers, not just enterprises with massive budgets.

The key distinction: incrementality tests answer finance’s question directly. They prove causation, not just correlation.

Why Finance Cares About Uncertainty

Here’s what makes incrementality different in finance conversations: it quantifies uncertainty.

Traditional marketing reports present point estimates. “Facebook delivered 3.7x ROAS.” One number, stated with confidence. Finance teams know better than to trust single-point estimates for anything—revenue projections, cost forecasts, risk assessments all come with ranges.

Incrementality tests produce confidence intervals. “We estimate Facebook’s incremental ROI is between 3.2x and 4.5x.” This acknowledges that true incrementality is unknowable—we can only estimate it within a range, with a certain level of confidence.

Counterintuitively, this uncertainty makes the measurement more credible to finance. The confidence interval signals intellectual honesty. It acknowledges the limits of measurement and quantifies the precision of the estimate.

For financial planning, ranges are more useful than false precision. A CFO can model scenarios using the low end of the range (3.2x) for conservative forecasts and the high end (4.5x) for aggressive growth plans. One number doesn’t allow for scenario planning.

This shared language—estimates with confidence intervals rather than precise-looking but unreliable point estimates—creates common ground. Both teams can discuss decisions using the same measurement framework.

The Forecasting Problem

The attribution theater problem becomes especially acute during budget planning. Marketing teams extrapolate from platform-reported metrics. Finance teams model cash flows based on those extrapolations. Forecasts consistently miss.

Why? Because inflated attribution numbers get plugged into financial models. If Facebook reports 5x ROAS but true incrementality is 3x, scaling spend based on the 5x number will disappoint. Revenue won’t materialize as projected. Budgets get cut. Trust erodes further.

BrandAlley, a UK-based fashion eCommerce company launching over 1,000 campaigns annually, faced exactly this issue. They implemented incrementality testing through marketing mix modeling to understand true causal impact across channels. The results showed material differences between platform-reported performance and actual lift.

Armed with better numbers, they could forecast accurately. Finance could trust the projections. Marketing could defend budgets with causal evidence rather than correlation metrics.

The difference isn’t just about measurement accuracy. It’s about breaking the cycle of missed forecasts, budget cuts, and eroded trust. When both teams use the same causally-valid metrics, forecasts improve, and organizations can plan with confidence.

Implementation Challenges

Adopting incrementality testing isn’t frictionless. According to research from Skai and Path to Purchase Institute, a third of CPG brand marketers measure incrementality only at a basic level. The top barriers are concerns about accuracy (44% of respondents), difficulty applying incrementality across different ad types and retailers (43%), and limited tools or technologies (41%).

These concerns are legitimate. Not everything can be tested easily. Brand campaigns that run continuously for awareness may not have natural holdout groups. Small-budget campaigns may lack statistical power to detect lift. Some channels, like linear TV, present geographic and technical constraints.

There are also opportunity costs. Every incrementality test withholds advertising from control groups, potentially sacrificing sales during the test period. For companies operating on thin margins, this represents real financial risk.

But the alternative—continuing to make decisions based on misleading attribution data—carries risk too. The organizations seeing success are those that acknowledge these constraints upfront and build testing into their planning cycles.

What Worked for Finance Buy-In

Organizations that successfully bridged the marketing-finance gap using incrementality followed several patterns:

Start with joint education. Get both teams aligned on what incrementality measures, why it matters, and what the limitations are. No surprises.

Frame tests as measurement investments. Finance teams understand that better data improves decisions. Position incrementality testing as infrastructure that improves capital allocation, not as a marketing expense.

Test where disagreement exists. Focus initial tests on the channels where marketing and finance most disagree about performance. Resolving those debates quickly demonstrates value.

Establish regular testing cadence. Quarterly tests for major channels, less frequent tests for smaller channels. Predictable schedule reduces friction.

Document what can’t be measured. Some effects—long-term brand building, word-of-mouth, customer lifetime value beyond immediate conversion—don’t show up in incrementality tests. Acknowledge this explicitly.

This last point matters. Incrementality testing measures short-term direct response. It doesn’t capture every marketing benefit. But being explicit about what you’re not measuring builds credibility for what you are measuring.

The Bigger Shift

The adoption of incrementality testing reflects a larger change in how organizations think about marketing.

For decades, marketing operated somewhat separately from core business operations. It was a creative function, difficult to measure precisely, judged partly on intuition and brand health metrics that didn’t translate directly to P&L impact.

That model worked in an era of limited measurement capability. You couldn’t easily run controlled experiments at scale. You couldn’t quickly test creative variations. You relied on annual brand studies and hoped for correlation between brand metrics and sales.

The shift toward incrementality-based measurement represents marketing becoming more integrated with business operations. Marketing claims can be tested the same way product changes get A/B tested or pricing strategies get validated.

This doesn’t mean eliminating creativity or intuition. It means having a reliable way to prove which creative risks paid off, which channels drove real growth, and which investments should be scaled.

Looking Forward

The incrementality testing market has matured quickly. Platforms like Measured, TransUnion, Rockerbox, and Sellforte now offer incrementality-as-a-service. Data clean rooms like Amazon Marketing Cloud and Snowflake provide privacy-safe environments for running tests. AI tools help automate reporting, with half of US brand and agency marketers adopting AI or machine learning for this purpose.

The IAB recently released guidelines for incremental measurement in commerce media, outlining when experiments, model-based counterfactuals, econometric models, and hybrid approaches work best. Industry standardization is happening.

As tools improve and costs decrease, incrementality testing will likely become baseline capability rather than advanced technique. The question will shift from “should we test incrementality?” to “how do we integrate incrementality insights into planning workflows?”

For the marketing-finance relationship, this matters. When both teams trust the same measurement methodology, conversations become about strategy rather than measurement validity. Instead of debating whether the marketing numbers are real, they can debate which opportunities to pursue.

That’s not just better measurement. It’s better business decision-making.