So, apparently, a bunch of direct-to-consumer brands are still grappling with the age-old headache of platform attribution. You know, where Meta and Google high-five each other while simultaneously claiming they alone drove that same conversion. It’s a mess. We’ve all seen the studies where digging into a single transaction reveals that organic search or a Google Shopping click was mysteriously gifted to some other channel. Amidst that digital shouting match, the big question looms: how do we actually divvy up the paid media cash?
The current darling, the supposed savior, is incrementality testing. Run a lift study, figure out who’s actually building demand versus just snagging the low-hanging fruit, and bam – reallocate your spend. That’s the narrative, anyway. Problem is, for many growth-stage companies, this incomplete picture has likely led to some spectacularly bad decisions. The most common blunder? Cutting those dusty upper-funnel channels that, when tested in isolation, fail to show a significant standalone lift. Then, poof, total revenue takes a nosedive because those channels were doing crucial work that no single-channel test could ever grasp.
This entire conversation needs a different starting point. Something more… holistic.
Why Incrementality Alone Doesn’t Answer The Allocation Question
Look, incrementality is genuinely useful for measuring the causal impact of a specific channel or campaign. That’s solid intel. But it’s a far cry from understanding marketing’s contribution to the business as a whole. Picture this: a potential customer sees a Meta ad on Monday. They don’t click, fine. But then, on Wednesday, they search for the brand directly and convert via a paid brand search ad. Meta gets a view-through credit. Google happily claims the last-click conversion. Now, if you run a lift study on either channel independently, you might see a modest incremental contribution. The real story? Both ads did important work, just different kinds. The Meta impression planted the seed of consideration; the branded search paid the piper to close the deal. Yanking either one of them? That’s how you break the customer journey.
And this is precisely where many brands stumble. They pore over incrementality results without the necessary context. Meta’s lift study comes back with a lukewarm performance, so they decide the channel’s just a credit-hog, claiming conversions that would’ve happened anyway. Budget gets reallocated. Six weeks later, guess what? Brand search volume tanks, overall efficiency dips, and the marketing team is left scratching their heads, wondering where all the revenue went.
One isolated lift study on one channel can’t possibly tell you if that channel deserves the budget. It only tells you what happened inside that specific test. That’s why allocation decisions desperately need a metric that actually reflects the entire business.
The Marketing Efficiency Ratio (MER): The Metric This Conversation is Desperately Missing
Marketing Efficiency Ratio, or MER (total revenue divided by total ad spend), is the one metric that conveniently ignores which channel gets the glory. It treats your entire marketing effort as one big investment churning out a single revenue stream. And that’s precisely what marketing is at the business level. It’s also the question CFOs and founders are actually asking when they peek at performance reports. Is the marketing machine actually working?
Now, MER by itself isn’t a magic wand. It can’t tell you how to allocate your budget within that spend. It can also get artificially inflated by seasonality or just a general upswing in organic demand. But it does answer the foundational question that should guide every other measurement decision: Is the combined marketing investment delivering acceptable returns at the business level? Once you have that anchor, the role of every other measurement layer snaps into sharper focus.
The Three-Layer Stack That Actually Works
A truly effective measurement setup comprises three distinct layers, each designed to answer a different, vital question.
MER answers: Is total marketing spend producing the returns this business needs? Is the investment working?
Incrementality answers: If I add or cut spend on this channel, what happens to MER?
Attribution answers: What touchpoints did customers actually engage with, and what does that tell me about channel role? How does this affect the customer journey?
The cardinal sin brands commit is using any one of these layers to answer questions that demand the presence of the others. Cutting Meta spend because brand search ultimately closed the sale? That’s reading attribution as if it’s causation. Blindly trusting Meta’s reported return on ad spend? That’s doing the same thing in reverse. Treating an isolated lift study as the ultimate verdict on whether a channel deserves budget? That’s completely ignoring what that channel might be quietly contributing to your overall MER by positively impacting other channels.
How To Actually Run Incrementality Inside This Stack
Running incrementality tests used to be a much more involved—and frankly, expensive—affair. The good news? The cost has come down significantly. Here are four testing methods, ordered by how easy they are to get started with:
Platform-Native Lift Studies
Meta Conversion Lift and Google Conversion Lift are built right into the existing ad platforms. No extra cost. According to Google’s own documentation, the platform now offers directional lift results for studies with budgets exceeding $5,000 USD and involving at least 1,000 conversions, supported by a shift to Bayesian inference.
