The Mirage of Certainty
In the marketing analytics world, “causal MMM” has become the phrase of the moment. Vendors promise that by layering experiments on top of a marketing mix model, they can “prove causality” and make results more trustworthy.
It’s an appealing story, but one that misunderstands both the math and the mission. When you try to calibrate one approximation (a marketing mix model) with another approximation (a geo-holdout or lift test), you don’t make it more accurate. You compound the inaccuracy.
Anyone who tells you they know exactly what worked is lying to you or to themselves. Marketing doesn’t operate in a lab. It’s a complex, dynamic system with noise, lag, and human behavior. It’s probability, not physics.
Causal MMM sells certainty where none exists. It claims to prove the unprovable by combining short-term experiments with long-term econometrics. In doing so, it often overfits to short-term noise and mistakes confidence intervals for truth. The result is not more clarity, but less of it.
The Digital Bias Behind “Causal MMM”
Much of this causal wave was born inside digital performance teams. It’s the same culture that built multi-touch attribution and incrementality testing: fast, experiment-driven, and obsessed with short-term lift.
But MMM was never meant to answer performance marketing questions. It was designed for strategic allocation and long-term planning. While experiments can be useful for validation, they cannot replace models built to capture how marketing creates outcomes across time, channels, and conditions.
Bringing digital incrementality logic into MMM is like trying to plan next year’s budget using yesterday’s click-through rate. The scope, the timescale, and the purpose simply don’t align.
The Scooby-Doo Reveal
Peel back the mask on most causal MMMs and you’ll find the same old cast of characters: performance marketers in new costumes. It’s a Scooby-Doo moment. Pull off the “causal” mask, and underneath are the same MTA folks saying, “We would’ve gotten away with it too if it weren’t for those pesky brand marketers.”
Causal MMM is not a new science. It’s a rebranding of an old mindset. It’s deterministic thinking masquerading as econometrics, treating marketing as a collection of isolated transactions instead of a portfolio of compounding investments.
This isn’t how brand building works.
What Experiments Miss
Experiments are powerful, but they tell you only what happens in a small, controlled window. They cannot explain long-term demand formation or brand preference. They cannot tell you what happens at the top of the funnel, where awareness and perception grow invisibly.
The problem with causal MMMs is that they elevate experiments to gospel truth. They treat the bottom of the funnel — clicks, conversions, and short-term lift — as the full story. In doing so, they erase the most important drivers of growth.
You can’t A/B test your way into becoming a household name. You can’t randomize word of mouth. You can’t measure cultural momentum in a two-week holdout.
Our models at Keen quantify exactly what those experiments miss. We measure the full stack of marketing impact, from immediate activation to long-term brand equity. Across hundreds of brands, we consistently find that the power of brand is the difference between tens or hundreds of millions in profitable growth versus flat performance.
Why Calibrating with Experiments Doesn’t Fix Your Model
Independent modeling analyses have shown that when MMMs are calibrated to match short-term experimental results, their out-of-sample accuracy declines.
Here’s why:
1. Time-Horizon Mismatch
Experiments measure short-term, single-channel lift. MMMs model multi-channel, long-term contribution. Using one to correct the other creates structural bias.
2. Unit Mismatch
Experiments measure percentage lift. MMMs operate in dollars or profit contribution. Aligning the two introduces scaling errors that ripple through forecasts.
3. Noise Amplification
Experiments are themselves approximations subject to contamination from bots, delivery bias, and contextual drift. Feeding that noise into an MMM compounds it.
4. Overfitting the Past
A calibrated model often fits last quarter’s test beautifully and misses next quarter’s reality entirely. The moment you force your model to memorize a moment, you sacrifice its ability to generalize.
The best-performing MMMs don’t rely on calibration at all. They’re validated by their ability to forecast the future, not retrofitted to explain the past.
Validation vs. Calibration
Calibration adjusts parameters until the model matches historical or experimental outcomes.
Validation tests whether the model can predict new outcomes it hasn’t seen before.
One is curve-fitting. The other is truth-testing.
At Keen, we build for validation. Our models are trained on actual business outcomes, revenue, sales, and profit, across billions of dollars in execution data. They’re tuned to predict what will happen, not to re-explain what already did. That’s why we consistently forecast within four percent of actual results across more than 400 brands, even in volatile economic conditions.
If our models weren’t tuned correctly, we’d have to be improbably lucky — over and over again.
The CFO’s Lens: From Cost to Investment
Most CFOs still see marketing as a cost. The best ones see it as an investment.
When you can quantify both the short- and long-term impact of marketing, you turn it into an investment class — one that behaves much like a financial portfolio.
Think of it as your 401(k) of marketing:
- Some contributions pay back quickly through short-term activation.
- Others compound quietly through brand equity.
- Together they produce predictable, measurable returns when modeled correctly.
Keen’s approach measures both sides of that curve. We quantify the short-term lift and long-term decay so brands can see exactly when and how increased budgets drive higher consumption and more profitable growth.
This isn’t rocket science. It’s just disciplined math applied to the quantifiable principles of marketing.
Experiments Can Validate, But They Can’t Calibrate
Experiments have value, but not as a repair tool. They can validate that your model is directionally correct, but they cannot rewrite its structure.
Properly run geo tests and holdouts are short-term, noisy, and context-specific. They tell you how a single channel behaved in a single window of time. That’s useful input, but it is not the blueprint for your full marketing system.
At Keen, we’ve tested every modeling variant, including experiment-calibrated approaches, and the outcome is always the same: models forced to “match” short-term lift almost always lose long-term predictive power.
Our philosophy is simple:
Use experiments to validate, not calibrate.
Use outcomes to train, not assumptions.
Forecast Accuracy: The Only Causality That Matters
The ultimate measure of causality isn’t theoretical. It’s practical.
If a model can consistently predict the future accurately, transparently, and repeatably, it’s already capturing the true causal structure of the market. That’s what Keen does.
We model the entire ecosystem of spend, response, decay, and interaction to show marketers and CFOs not just what happened, but what’s next — with statistical confidence.
Causality isn’t proven in a lab. It’s proven in the ledger. Forecast accuracy is the only real causal test.
The Bottom Line
Causal MMM may sound like the next frontier, but in practice it’s a step backward, a digital reflex that confuses experimentation with explanation.
Keen’s forecasting-first system skips the false comfort of calibration and moves straight to decision-making. We measure outcomes, validate forecasts, and continuously learn from real results, turning marketing into a predictable investment strategy that compounds over time.
Because confidence, not calibration, is what drives growth.
Forecast Accuracy Over False Certainty
Keen is an AI-powered marketing mix modeling platform built to forecast, analyze, and optimize marketing performance across every channel. Unlike models that rely on short-term experiments or “causal” calibration, Keen measures what truly matters — validated, forecast-accurate results tied to real business outcomes. Our system quantifies both short-term activation and long-term brand impact, turning marketing into a predictable investment strategy that compounds over time.See how forecast accuracy outperforms calibration. Request a demo today.
