A supplement brand spent six months scaling their YouTube awareness campaigns based on attribution showing strong performance. Their model assigned 30% credit to YouTube using a time-decay framework—recent touchpoints got more credit, earlier ones got less. The math looked solid. The ROAS justified continued investment.
Then they switched to a measurement platform that actually learned their customer behavior. Turns out YouTube’s real impact was nearly double what their previous attribution showed. Why? Their customers had unusually long consideration cycles where early awareness touchpoints drove decisions months later. The time-decay assumption had systematically undervalued their best-performing channel for half a year.
This isn’t a story about bad marketers or broken tools. It’s about a fundamental distinction most brands don’t realize exists: the difference between attribution that applies assumptions versus measurement that learns your actual patterns.
The assumptions you don’t know are happening
Every multi-channel attribution model operates on assumptions about how credit should be distributed. These assumptions might be simple and obvious, or they might be sophisticated and hidden, but they’re always there:Position-based models assume first and last touches matter most, splitting 40-40-20 regardless of whether that reflects your customer behavior. Linear attribution assumes every touchpoint deserves equal credit. Time decay assumes recent interactions matter more. Even algorithmic attribution that claims to “learn from your data” is ultimately applying predetermined frameworks about how to weigh different channels.Here are just some of the ways multi-channel attribution models apply predetermined frameworks:
First- and last-touch models overweight the extremes while ignoring everything in between.
Linear attribution gives equal credit to all touchpoints regardless of their actual influence.
Time decay models assume recent interactions matter more, which may or may not be true for your business.
But the assumption problem goes deeper than just how conversions get credited. It extends into the very models that power measurement platforms, including marketing mix models that many brands consider more sophisticated than click-based attribution.
Assumptions in marketing mix models
Most marketing mix modeling (MMM) platforms assume all campaigns saturate the same way: that spending more always delivers diminishing returns following a predictable curve. But that’s not how marketing actually works. Some campaigns hit saturation quickly. Others have multiple efficiency peaks. A few might actually get more efficient as you scale past certain thresholds. We learned this firsthand when we evaluated every major open-source MMM model and realized none of them could handle the complexity of real marketing patterns. That’s why we built our own from scratch.
The same assumption problem applies to spillover effects—how your marketing in one channel influences performance in others. Most MMMs don’t measure spillover at all. The handful that do typically apply channel-level assumptions: “Facebook campaigns drive 15% lift in organic search.” But your Facebook prospecting campaigns and your Facebook retargeting campaigns don’t create the same spillover effects. Your CTV campaigns might drive massive branded search lift while your display ads drive direct traffic. These patterns are unique to your brand, your creative, your audience—yet most models apply generic assumptions instead of learning what actually happens.
How to spot the difference
When evaluating any attribution or measurement approach, ask these questions:
“Is this credit distribution based on my actual customer behavior, or predetermined rules?” If the model assigns credit the same way for every brand, it’s making assumptions. True learning means the credit distribution changes based on what your customers actually do.
“Does this understand how MY campaigns saturate, or apply generic diminishing returns?” If every campaign follows the same saturation curve, that’s an assumption. Real patterns show dramatic differences between campaign types, creative approaches, and audience segments.
“Can this measure the specific spillover effects between my channels, or does it use industry averages?” Generic spillover coefficients are assumptions. Learning means discovering that your YouTube campaigns drive 3x more branded search than your podcast sponsorships, or vice versa.
“What happens when my business changes?” If the model needs to be manually recalibrated when you launch new products or enter new markets, it’s not truly learning—it’s just applying different assumptions to different scenarios.
Why assumptions aren’t always wrong (but you need to know what you’re getting)
Here’s the nuance: assumptions aren’t inherently bad. For some brands, standard attribution models provide directionally useful insights. If your customer journeys are straightforward and primarily digital, a position-based model might be close enough. If your campaigns all follow similar patterns, generic saturation curves might be adequate.
The problem isn’t that assumptions exist—it’s that most brands don’t realize they’re using them. They see sophisticated dashboards, clean reports, and confident recommendations without understanding the predetermined frameworks driving those outputs. They make million-dollar budget decisions based on what looks like data-driven insights but are actually generic patterns applied to their specific situation.
You deserve to know whether your measurement is learning your unique patterns or applying industry assumptions. Because the difference between those two approaches might be the difference between scaling your best campaigns and cutting them.
The measurement that actually learns
This is why we built Prescient’s MMM to learn rather than assume. Our models discover how each of your campaigns actually saturates—revealing multiple efficiency peaks, temporary troughs, and the specific spend levels where performance shifts. We measure the unique spillover effects between your channels, showing exactly how your awareness campaigns influence direct traffic, branded search, and even retail conversions. And these patterns update continuously as your marketing evolves, rather than requiring manual recalibration.
We’re not claiming assumptions never work. We’re saying you should know when you’re using them—and have access to measurement sophisticated enough to learn what’s actually happening in your marketing ecosystem.
Want to see the difference between assumed patterns and learned insights? Book a demo to discover what your attribution might be missing.
