1. The Weather Forecast Lesson
When the weather forecast says there’s a 70% chance of rain, no one writes a think piece about how “meteorology is flawed.”
We don’t expect perfection from it. We expect direction. We know the model is probabilistic, not prophetic. It’s based on the best data available, meant to help us make smarter decisions: bring a jacket, plan a backup, don’t cancel the trip.
That’s exactly how marketing leaders should think about Marketing Mix Models (MMMs).
They’re not crystal balls. They’re weather forecasts for your budget.
MMM doesn’t exist to tell you precisely what will happen next quarter. It exists to help you prepare for the most likely conditions and to make better, faster adjustments when those conditions change.
Yet the industry has started arguing about whether MMMs are “accurate enough” or “scientifically valid.” That misses the point entirely.
The goal isn’t perfection. It’s perspective. The goal isn’t to be right all the time. It’s to have a model that can help inform budget improvements to explore, faster.
2. The Wrong Debate
As MMMs gain momentum, a criticism is surfacing:
“It’s just correlation.” “It can’t prove causality.” “It doesn’t predict the future.”
All true, and all beside the point.
Marketing mix modeling was never designed to be perfect. It was designed to be useful for budget allocation planning. (Something attribution models have been overextended to try doing.)
It’s a way to understand directional relationships between spend and outcomes across many variables at once. The value isn’t in predicting an exact number, it’s in surfacing which levers seem worth testing next.
💡 “MMM isn’t about predicting the future. It’s about finding smarter bets to test next quarter.”
If you use it as a strategic compass you start to see how powerful it really is.
3. What Good MMMs Actually Do
A good MMM doesn’t try to prove what caused every dollar of revenue. It helps you spot patterns that are strong enough to explore.
It helps you:
- See which channels appear to drive meaningful lift over time
- Detect early signs of diminishing returns in over-saturated channels
- Spot underinvested areas that could scale further
- Model “what-if” budget investment scenarios before spending a dollar
From there, it gives you hypotheses worth testing:
“We might be overspending in paid search.” “Events might have more impact than we thought.” “Brand spend looks under-leveraged at this stage.”
These aren’t certainties. They’re probabilistic ideas meant to guide strategy conversations.
MMM’s job isn’t to make the final call. It’s to make the next conversation smarter.
4. The Calibration Loop
The best teams don’t build a model once and treat it as gospel. They build a feedback loop.
1️⃣ Run the model: identify potential under- and over-performing channels.
2️⃣ Shift spend experimentally: act on those insights with small, measured bets.
3️⃣ Recalibrate next quarter: update the model to see what held, what weakened, and what new relationships are emerging.
That’s how MMM works best: as a learning system, not a verdict machine.
Over time, this iterative approach creates compounding intelligence. Each quarter’s model gets a little sharper, not because it’s more “accurate,” but because it’s better informed by recent changes and tests.
💡 “The goal isn’t to be right. It’s to get less wrong, faster.”
5. The Realists vs. the Purists
The biggest divide in the MMM debate isn’t between data scientists and marketers. It’s between realists and purists.
- Purists want statistical perfection: provable causality, controlled conditions, complete data.
- Realists know business decisions don’t wait for perfect data. They need direction today.
The purists are right that MMM can’t isolate every causal variable. But the realists are right that you don’t need that to make better budget choices. (It’s not brain surgery. It’s budget optimization.)
B2B marketers, in particular, live in a world of incomplete data: human sales teams, long buying cycles, overlapping touchpoints. Waiting for perfect clarity means falling behind.
You don’t need precision to improve confidence. You just need a model that gives you a better starting point.
6. How to Keep MMM Grounded
If you want your MMM to stay useful, not overhyped or under trusted, follow a few simple principles:
✅ Treat outputs as hypotheses, not truth. Every result should spark a question, not end one.
✅ Run models frequently. Quarterly updates create tighter feedback loops and build trust in the process. (Monthly for most of our clients has felt too often.)
✅ Involve cross-functional teams. Finance, sales, and marketing all bring context that makes the model stronger.
✅ Track external signals. Watch branded search, intent data, and engagement trends to validate or challenge model directionality.
✅ Prioritize directional consistency over statistical purity. Look for patterns that keep showing up, not perfect coefficients that never exist.
💬 “If you’re using MMM to justify the past, you’re missing its power to shape the future.”
7. Closing Thought
A media mix model isn’t meant to be a courtroom. It’s meant to be a classroom.
It doesn’t hand down verdicts. It helps you learn faster. It doesn’t prove cause and effect. It reveals where to look next. It doesn’t predict the future. It prepares you for it.
Just like the weather forecast, it won’t always be right. But if you use it well, you’ll make smarter choices, stay better prepared, and get caught in fewer storms.
The goal isn’t perfection. It’s progress.