Same Metrics, Shared Truth: How Incrementality Testing Aligns Finance and Marketing
by
Isaac Lee
Finance and marketing often approach the same metric from completely different angles. Neither is wrong; they're each doing their job with the tools available to them. The challenge is that without a shared causal foundation, the same number can mean different things depending on where you're sitting. Incrementality testing changes that, not by forcing a single view, but by giving every team a common ground to build from.
Customer Acquisition Cost (CAC)
Finance typically approaches CAC from the top down — summing all marketing spend and dividing by total new customers acquired to arrive at a blended business-level figure. This is reasonable for a team assessing overall unit economics. But blended CAC is too broad to make channel-level decisions from.
On the other hand, marketing needs channel-level detail. The challenge is that channel-level CAC often relies on platform-reported or click-based attribution, and every platform has a natural tendency to report its own contribution in a favorable light. That doesn't make the data useless, but it does make it useful for directionality at best.
The result is two teams working from two versions of the same metric — one too broad to optimize from, one too biased to fully trust.
David Protein felt this tension as they were scaling up: Platforms were reporting that CPAs were dropping, even while internal analysis from Finance showed CAC rising across the board.
How incrementality testing helps
Incrementality testing corrects the denominator (CAC) this metrics depends on. By measuring only customers who genuinely wouldn't have converted without the ad spend, it leaves a true count of incrementally acquired customers — something that both marketing and finance can surely agree on.
For finance, the blended CAC threshold is now built on a number they can defend. For marketing, channel-level CAC is no longer inflated by platform self-reporting or by click-based reporting that favors bottom-funnel channels.
Both teams are working from the same causal foundation. As David Protein found, this correction can be significant: a 36% overestimation of DTC orders under traditional attribution is a meaningful difference in what CAC actually looks like — and in the confidence both teams can build on top of it.
LTV:CAC
LTV:CAC inherits every problem from CAC, but the LTV side of the equation carries its own uncertainty. Most LTV calculations assume customers acquired across different channels have similar retention profiles. That assumption is rarely tested. A customer acquired through high-intent search might repurchase three times a year. A customer acquired through a broad awareness channel might never return. Averaged together, the LTV figure obscures more than it reveals.
Finance uses LTV:CAC to set growth strategy while Marketing uses it to justify channel spend. When LTV is a blended average and CAC is platform-reported, neither team is fully confident in the number they're presenting.
How incrementality testing helps
Incrementality improves both sides of the fraction. On the CAC side, it corrects the denominator. On the LTV side, it identifies which channels are acquiring customers who genuinely wouldn't have found the brand otherwise — and whether those customers show stronger retention and repurchase behavior than average.
A channel that looks expensive on CAC alone might produce customers with significantly higher LTV. A channel that looks cheap might be capturing existing demand from customers who were never going to come back. Incrementality gives brands the ability to sort those customers into the right buckets in a manner that they can trust.
ROAS and iROAS
Finance tends to think about ROAS from the top down — total revenue divided by total ad spend. It's a reasonable starting point, but too broad to drive channel-level budget decisions. It doesn't tell you which channels to scale, reduce, or reallocate.
Marketing wants that channel-level granularity. The challenge, as we've covered, is that platform-reported attribution is inherently biased toward channels closest to conversion. Neither team ends up with numbers they can fully trust — and that makes it genuinely difficult to agree on budget changes. Finance is reluctant to approve increases for channels that don't show up clearly in the numbers. Marketing struggles to make the case for upper-funnel spend that attribution systematically undervalues.
How incrementality testing helps
iROAS gives both teams what they've been missing: a channel-level view of returns that isn't distorted by click-based or platform-based reporting. By measuring true incremental demand per channel, both finance and marketing can look at the same granular, trustworthy number — and make budget decisions from a shared foundation rather than different versions of the truth.
The Underlying Thread
Finance and marketing are ultimately working toward the same goal — a business that grows profitably and sustainably. The challenge isn't a lack of alignment on that outcome. It's that without a shared measurement foundation, each team is working from numbers that reflect their own vantage point rather than a common reality. That makes genuine cooperation harder than it needs to be, even when the intent is there.
Incrementality testing provides that common ground — and with it, the conditions for a different kind of working relationship. It's an improvement that isn't just about measurement, but about culture.