Anyone who’s played a video game knows that sometimes their physics engines don’t quite match reality. Objects float mysteriously, characters clip through walls, and horses climb vertical mountains. The games are still fun—but the disconnect between virtual and actual physics is unmistakable.
Many marketing mix modeling (MMM) solutions have the same problem: they operate on a “marketing physics engine” that doesn’t reflect how marketing actually works in the real world. Just as a game with unrealistic physics creates bizarre outcomes, an MMM with an unrealistic understanding of marketing creates misguided decisions and missed opportunities.
For MMMs to be useful, they must capture the complex, interconnected nature of modern marketing ecosystems, not the simplified version many providers sell.
The reality of marketing in today’s digital landscape
Marketing doesn’t happen in neat silos, though many measurement tools treat it that way. Your customers are seeing your ads on Instagram, searching for reviews, visiting your website, getting retargeted, receiving emails, and possibly seeing your products on Amazon all before making a purchase.
A single marketing campaign doesn’t just drive direct conversions; it influences performance across other channels too. That Facebook video might not immediately convert, but it could drive branded search, organic traffic, and even Amazon purchases days or weeks later.
These cross-channel effects aren’t exceptions, they’re the rule. The average consumer now interacts with more than six different touchpoints before converting. Modern marketing is a complex, interconnected system where efforts in one area create ripples throughout the entire ecosystem.
Limitations of marketing mix models in the market
As the adoption of marketing mix modeling grows, it’s important to understand that not all MMMs are created equal. The market currently offers various approaches to modeling marketing impact, each with their own set of limitations that can prevent marketers from seeing the true picture of their campaign performance.
Traditional MMM limitations
Most traditional MMMs were built on regression analysis techniques developed in the 1960s. While revolutionary for their time, they simply weren’t designed for today’s multi-channel digital marketing reality.
These models often make linear assumptions that oversimplify marketing relationships, treating channels as isolated entities with uniform decay and saturation patterns. They struggle to capture cross-channel effects and typically provide insights only at the channel level, not the campaign level where marketers actually make decisions.
Perhaps most critically, they lack sophisticated causal understanding: the ability to determine not just correlation but actual cause and effect across marketing activities.
Limitations of newer, open-source MMMs
Even as the industry evolves, newer open-source models bring their own challenges. They’ve improved upon older regression models, but they still fall short in critical areas that impact marketing decision-making.
These models also often apply standardized approaches to decay and saturation, failing to account for how different campaigns uniquely impact consumer behavior. Essentially, they’re improvements on a building with a rotten foundation. There are fundamental limitations that prevent these models from accurately reflecting what marketers know to be true about how their paid campaigns function in the marketing ecosystem.
The business impact of using incomplete models
When your MMM doesn’t reflect reality, the consequences are tangible. Brands routinely misallocate budgets, particularly undervaluing top-of-funnel marketing that doesn’t directly drive conversions but significantly influences them.
We’ve seen clients who discovered their platform data wasn’t aligning with their actual revenue numbers, with platforms sometimes reporting combined revenue that exceeded their total revenue. Without an accurate model that reflects real marketing dynamics, these clients couldn’t trust their attribution data and were making decisions based on flawed information.
In other cases, clients have found that campaigns they thought were underperforming were actually driving significant value through halo effects on organic traffic, branded search, and direct traffic. That’s revenue that would be missed by models that don’t account for these cross-channel impacts.
Incomplete models also make scaling with confidence nearly impossible. Without understanding how marketing truly works, increasing spend becomes a guessing game rather than a strategic decision.
Key elements MMMs need to capture real-world marketing
For an MMM to accurately reflect how marketing works in reality, it needs to incorporate several critical elements that match the complexity of today’s marketing ecosystem. These components ensure that the model doesn’t just measure direct impacts but captures the full spectrum of marketing effects.
Halo effects (spillover)
Marketing halo effects—the phenomenon where marketing in one channel drives results in others—are not optional for modern MMMs; they’re essential. When someone sees your CTV ad and later makes an unattributed purchase through branded search, that’s a halo effect. When your Meta campaign drives Amazon sales, that’s a halo effect too.
These effects are particularly crucial for understanding top-of-funnel marketing performance. Without capturing them, brands consistently undervalue awareness campaigns while overvaluing lower-funnel efforts that benefit from the groundwork laid by those awareness campaigns.
Campaign-level granularity
The modern marketing landscape demands insights that go beyond broad channel assessments. Two campaigns on the same platform can perform radically differently—a Facebook prospecting campaign has different saturation patterns, decay curves, and cross-channel effects than a Facebook retargeting campaign.
Effective MMMs need to deliver campaign-specific insights that align with how marketers actually allocate budgets and make optimization decisions. This granularity enables precision that channel-level analysis simply can’t provide.
Custom saturation curves
The traditional understanding of marketing saturation is often too simplistic for real-world application. Different campaigns saturate differently, and many exhibit multiple efficiency peaks. A campaign you think is saturated might simply be in an efficiency trough before reaching another peak at higher spend levels.
MMMs need to model these nuanced saturation patterns for each campaign rather than applying one-size-fits-all assumptions that could lead to premature budget cuts or missed opportunities.
Nuanced decay modeling
The impact of marketing isn’t uniform across time or channels. Some create immediate results that quickly fade; others build slowly with long-lasting impact. Your MMM needs to capture these varied decay patterns instead of applying uniform assumptions across channels.
This nuanced approach to decay modeling ensures you understand not just the immediate impact of your marketing but its extended influence over time.
Causal understanding
Perhaps most important is the ability to distinguish between correlation and causation in marketing data. True causal understanding requires sophisticated modeling of how different marketing elements influence each other and ultimately drive revenue.
Without this causal perspective, you’re left with potentially misleading associations rather than actionable insights. A model that understands cause and effect can identify the true drivers of performance rather than just the coincidental relationships.
The Prescient approach: an MMM that mirrors reality
Prescient AI was built specifically to address these limitations. Rather than adapting legacy models or open-source alternatives, we built our MMM from the ground up with today’s marketing complexity in mind.
Our approach layers multiple sophisticated models that work together to capture the full picture of marketing performance, from direct revenue to halo effects across channels. We refresh these models daily rather than monthly, providing timely insights for rapid optimization.
Most importantly, we deliver campaign-level insights that reflect how marketing actually performs in the real world. Our models understand the nuanced ways different campaigns saturate, decay, and influence other channels, giving marketers actionable intelligence for optimization.
The real world demands realistic models
In today’s complex marketing landscape, you can’t afford a model with unrealistic marketing physics. Gamers can laugh off horses climbing vertical mountains and characters falling through solid ground, but the consequences of marketing decisions based on flawed models are far from amusing—they’re costly.
Your marketing operates in the real world. Make sure your MMM does too.
