Marketing Mix Modeling

Why Lower Funnel Channels Can Mislead Your MMM (And What to Do Instead)

Lower funnel channels – branded search, retargeting, affiliate – always look like heroes in your marketing mix. They convert fast, scale cleanly (or so it seems), and look great last-click attribution.  But these channels work because something else made them work first. If your media mix model doesn’t reflect that …

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The Hidden Cost of Over-Controlling for Non-Marketing Variables

A somewhat unpopular opinion we hold at Recast: yes, GDP, consumer sentiment, inflation, and interest rates do impact business performance — but no, you probably shouldn’t try to control for all of them in your media mix model. That instinct to “control for everything” is understandable. It feels like the …

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Are Too Many Control Variables Damaging Your Media Mix Model?

A common mistake in media mix modeling (MMM) is the belief that “more control variables are always better.” This mindset comes from treating statistical models as causality machines. The thinking goes: If we just input everything, we’ll get perfect causal inferences on the other side. That’s just not true. In …

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Why Internal Validation Metrics Are the Wrong Way to Judge Your MMM

The most dangerous media mix model (MMM) isn’t the one that’s obviously wrong—it’s the one that looks right but quietly misleads you. A model can perfectly match past sales trends while being completely wrong about why those trends happened. That’s because internal validation metrics like RMSE, MAPE, and R-squared can …

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Why Building a Robust MMM Is Tough: Tackling Diminishing Returns, Data Limits, and Time Shifts

What should companies doing (or thinking about doing) media mix modeling beware of? At Recast, we’ve seen the biggest MMM challenges fall into three major categories: Let’s break down why these issues are so difficult—and how they can be solved. 1. Diminishing Returns: Why More Spend ≠ More Results One …

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