Marketing Mix Modeling: Why Now Is the Time to Pay Attention to It

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In marketing, we’re surrounded by acronyms, and we often throw them around without a second thought, as if everyone knew exactly what they mean. One of the most important (and least understood) is MMM, or Marketing Mix Modeling. If that sounds academic, distant, or too technical to you… this article is for you.

For years, MMM has been the “secret” behind many important strategic decisions at major brands. However, with the rise of digital and the obsession with real-time attribution, this approach gradually fell by the wayside. Until now.

Why is it back in style?

The answer is simple: because we need it. At a time when the advertising ecosystem is more complex than ever, MMM offers something that’s in short supply: a comprehensive strategic vision. It’s no longer enough to just look at CPA or ROAS in Google Ads. We need to understand what role each channel—digital and non-digital—plays in driving sales growth.

In addition, tools such as Robyn or Meridian, developed by Meta and Google, respectively, have democratized access to this type of modeling, making it more affordable, interpretable, and actionable. However, this does not mean that implementing these models is a simple or immediate process.

It’s not magic—it’s statistics

The MMM isn’t a magic wand; it’s a statistical model that analyzes historical data to estimate the impact of each channel on sales. It relies on econometric and mathematical techniques, but its real value lies in how it translates that data into business decisions.

Thanks to MMM tools, we can visualize—literally—how media such as television, radio, outdoor advertising, and sponsorships contribute to the client’s results. Not only that, but we can also measure their marginal ROI and estimate response and decay curves (how long the effect of an ad lasts).

What is this for?

To make better decisions, to figure out whether it’s worth investing more in outdoor advertising or if your budget is overloading certain channels, to use data to show that TV works (or doesn’t), or to talk to the CEO or CFO on an equal footing, with an Excel spreadsheet in hand and a smile on your face.

It also helps drive sales. Because yes, this is marketing, but it’s also business. And when you can show that for every euro invested in a channel, you get a return of double that amount or more, the conversation changes.

And what about the MTA?

This isn’t about choosing between MTA (digital attribution) and MMM; they’re complementary approaches. MTA tells you who made the pass during the play; MMM helps you understand how the entire season went. One focuses on the microdata, the other on the big picture. Both are necessary to avoid making short-sighted decisions.

Who should lead this?

Although MMM has its roots in the world of data, it needs professionals who understand the business. People who know how to translate the insights the model provides into decision-making and strategy—not just for marketing, but for the business as a whole. That’s why, at MIO One, we approach these projects from a consulting and strategy perspective with a fully cross-functional vision, drawing on a diverse range of professionals, such as data scientists and media buying experts.

Because, in the end, what matters isn’t just building models, but knowing what to do with them.

Ignacio Boza Growth & Marketing Consultant MIO One