"If you torture the data long enough, it will confess to anything." – Ronald Coase
Coming out of Google’s recent announcement regarding its push for Marketing Mix Modeling (MMM) as a privacy-friendly measurement solution, it’s tempting to think this approach is the future of marketing analytics. It takes years of historical data, applies some fancy statistical techniques, and spits out insights on how different marketing channels drive business outcomes. Sounds great, right?
The problem is, unless you fully understand the nuances—meaning both the pros and the (potentially major) cons—MMM can be downright dangerous. It's a powerful methodology that can make a significant impact when used correctly—but here's the thing: it's very easy to misuse, and when that happens, it’s not just a slight misstep. You can make all the wrong decisions while thinking you’re making the right ones.
The Atlas Problem: Seeing the Big Picture While Missing the Details
MMM is like using an atlas to navigate a city. It helps you see the broad layout—where the highways are, the general direction of traffic—but it completely misses the potholes, construction zones, and red lights that affect your actual journey.
The biggest trade-off? Granularity and feedback loops.
MMM works at an aggregated level, meaning it doesn’t look at user-level data. Instead of seeing how specific campaigns, keywords, or audience segments perform, you get a broad, channel-level view. That might be useful for big-picture budget allocation, but it means your ability to make granular, timely optimizations is severely limited.
Think of it this way: if you were optimizing a race car, would you rather have real-time telemetry on speed, braking, and tire wear—or a summary report two months later telling you what generally worked? MMM gives you the latter, which is why you can, on average, make fewer and less-informed decisions than if you were working with more detailed, real-time data.
The Issue with Linear Thinking in a Non-Linear World
Another major pitfall of MMM is its reliance on linear statistical principles.
Most MMM models assume that increasing spend leads to proportional increases in revenue. But the real world doesn’t work that way. What about market saturation? Competitive shifts? Diminishing returns, where spending more on ads starts cannibalizing existing demand instead of creating new conversions? You can build frameworks into your model to estimate these effects, but at the end of the day, it’s still a guess—unless you have a crystal ball, in which case, quit hogging it. I need to know when Notre Dame will win its next national championship.
A perfect example: imagine a company doubling its ad spend and expecting sales to double as well. If only. In reality, past a certain point, spend efficiency starts dropping, competitors react, and customer acquisition costs increase. MMM isn’t built to handle these nonlinear dynamics, which means it can lead to severely flawed budgeting decisions.
A 10,000-Foot View—Not a Day-to-Day Tool
At the end of the day, MMM is a high-level strategic tool, not an optimization tool. It’s like looking at a map from 10,000 feet in the air—it tells you the general direction you’re heading, but it won’t help you navigate the twists and turns of the actual road.
Would I use it to set long-term budget allocations? Maybe.
Would I use it to optimize campaigns day-to-day? Absolutely not.
Instead, the best approach is to pair MMM with more granular, user-level tracking methods—things like, incrementality testing, by-user level tracking data, multi-touch attribution (MTA), and real-time conversion tracking. That way, you get the big-picture insights without losing the details that actually drive smart marketing decisions.
Final Thoughts on Marketing Mix Modeling (MMM)
MMM is cool. It can give you broad insights into marketing effectiveness. But if you don’t know exactly what you’re looking at—or worse, if you take its results at face value without questioning them—it can lead you straight into a decision-making disaster.
So before you rely on an MMM model, ask yourself: Am I looking at an atlas when I really need a GPS? If so, it might be time to zoom in.