The problem with slicing revenue like a pie chart, and how our model fixes it

Try to understand a symphony by listening to each instrument alone. You'd hear the violins, then the brass, then the percussion, each one isolated and graded on its own merits. But that's not how a symphony actually works. The swell of the strings changes how the brass lands. The tempo set by the percussion shapes how every other section plays its part. Pull the piece apart into separate tracks and you lose the thing that made it work in the first place: the interaction.

Most marketing mix models (MMMs) make the same mistake with your revenue. They treat it like a pie chart, something that can be neatly sliced into a "baseline" piece and a handful of channel-shaped pieces sitting next to it. It's a tidy way to build a model, but it’s not how marketing actually behaves.

If your model can't tell the difference between a channel creating demand and a channel simply capturing demand another created, you'll end up cutting budget from the channels actually doing the heavy lifting and pouring more into the ones taking credit for it.

Key takeaways

• Traditional MMMs assume revenue can be split into separate, additive pieces (baseline sales plus each channel's contribution), but real marketing doesn't work in isolated silos.
• Prescient's model architecture models marketing as a connected system instead, where spend shifts underlying states like brand awareness and consumer intent, which then show up in sales over time.
• Our model builds in structural rules that match how marketing actually behaves, including funnel direction, flexible response curves, and channel-specific memory and carryover.
• Incrementality test results get folded in as informed priors, with before-and-after accuracy checks so marketers can see exactly what changes and stay in control of the decision.
• In a controlled benchmark against two widely used open-source approaches, Prescient’s model cut attribution error by roughly 70%.
• Our model held up across the entire funnel, including upper-funnel awareness channels where comparison models tend to lose accuracy.

From pie charts to systems

Traditional MMMs rely on what researchers call separable decomposition. In plain terms, that means the model assumes total sales can be cut into independent, additive pieces: baseline organic sales over here, paid media contributions over there, with a clean line between them.

In the real world, that line doesn't exist. A YouTube or CTV campaign can lift organic search volume days later. Brand awareness changes how responsive people are to your ads in the first place. Even the timing of a promotion can shift what looks like "baseline" demand. None of these pieces sit still long enough to be sliced apart.

Our model takes a different approach. Instead of trying to carve up a static pie, it models marketing as a system that evolves over time. Marketing spend acts as an input that shifts underlying business states, things like brand awareness, consumer intent, and how efficiently a given channel is working in that moment. Those states then map to the store sales you actually observe.

Building real-world rules into the model

Standard regression-based models don't have any built-in sense of how marketing actually works, so they'll happily land on a mathematically valid answer that makes no real-world sense. Our model gets around this by encoding structural rules directly into how it's built, rules that reflect how marketing behaves in practice rather than what's convenient to calculate. A few of the most important are:

• Funnel directionality: Prescient’s model recognizes that upper-funnel tactics like YouTube or CTV create demand, while lower-funnel tactics like branded search capture demand that already exists. This keeps lower-funnel channels from taking credit for demand that upper-funnel spend generated.
• Flexible response shapes: Older MMMs force diminishing returns into the model through rigid formulas, whether or not the data supports it. Our model allows channel efficiency to stay linear across active spending ranges, and only shows saturation where the data genuinely points to it.
• Tactical carryover and memory: Rather than applying one static decay rate to every channel, our MMM models how the effects of spend linger and compound differently depending on the tactic, capturing multi-week lag effects unique to each one.
• Contextual efficiency and synergies: Prescient’s model accounts for how seasonality shifts efficiency and how spending in one channel can amplify another, picking up on cross-channel halo effects that a purely additive model would miss entirely.

Grounding results with real experiments

When brands run geo-lift studies or other incrementality tests, a lot of MMMs bolt those results on as after-the-fact parameter tweaks. That can make the model look accurate at the exact spot the test was run while pulling it out of alignment everywhere else, without anyone noticing.

Our model handles this differently. Test results get incorporated as priors that inform how the model understands the underlying system, and the process stays transparent throughout:

• Before-and-after benchmarking: When a brand brings in an incrementality test as a prior, model accuracy gets checked both before and after that test is applied.
• Clear visibility: Marketers can see whether adding the test result actually improves predictive accuracy or creates friction with the observational data.
• Marketer control: The team decides whether to run the model with the test prior included, or to rely on the observational data on its own.

It's also worth noting what our model deliberately leaves out: it relies on system dynamics and experimental evidence, not tracking-based methods like multi-touch attribution or pixel-based last-click data.

What our testing found

To see how our model performs against a known answer, it was tested in a synthetic, agent-based environment across ten independent datasets where the true incremental contribution of each channel was already known going in.

• Roughly 70% lower error: Our model's mean error (RMSE) on true incremental contributions came in around 31,000, compared to 99,000 for a Bayesian MMM baseline and 110,000 for a ridge regression MMM baseline.
• 0.97 accuracy correlation: Prescient’s model's attribution matched actual channel performance with a 0.97 correlation, versus 0.79 and 0.87 for the two open-source baselines.
• Consistency across the funnel: The comparison models broke down on upper-funnel awareness channels, with correlations dropping below 0.60. Our model held steady between 0.90 and 0.97 across every stage of the funnel.

Where Prescient comes in

This kind of accuracy only matters if it actually changes what you do with your budget. Our model is built to give marketers a clearer, more honest read on which channels are genuinely driving revenue, including the upper-funnel work that's historically been the hardest to measure, so decisions about where to spend next quarter are based on what's really happening rather than what a rigid formula assumes should be happening.

If you're ready to see how this plays out, book a demo and we'll walk you through it.

FAQs

What does "incrementality" actually mean in marketing measurement? 

Incrementality is the portion of a sale or conversion that wouldn't have happened without a specific piece of marketing. It's different from simply tracking which channel a customer touched last. A channel can show up in a lot of customer journeys without actually driving much incremental revenue, which is exactly the gap that a strong MMM is meant to close.

How is Prescient’s model different from a typical marketing mix model? 

Most MMMs assume revenue can be split cleanly into separate, additive pieces. Prescient’s instead models marketing as a connected system, where spend in one channel can shift underlying states like awareness and intent that affect how other channels perform. That structure lets it capture interactions that a purely additive model would miss.

Can I still bring my own incrementality test results into the model? 

Yes. Prescient’s model can incorporate geo-lift studies and other incrementality tests as priors, and it shows you the before-and-after accuracy impact so you can decide whether including that test result actually improves the model. The decision stays with your team.

Is the OMEN benchmark study peer-reviewed? 

No. It's Prescient's own research, tested in a controlled synthetic environment with known ground truth so the results could be measured directly rather than estimated. We're transparent about that distinction because trust in a measurement model should be built on clear evidence, not just claims.

Does Prescient’s model work for upper-funnel channels like CTV and YouTube?

Yes, and that's one of the areas where it separates itself most from typical MMMs. In benchmark testing, comparison models lost significant accuracy on upper-funnel channels, while our model held accuracy steady across the entire funnel.