Marketing Mix Modeling

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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The 3 Hurdles of Estimating a Bayesian MMM: Saturation, Divergences, and Multimodality

At Recast, we’ve built our media mix modeling (MMM) platform based on Bayesian methodology because we believe it provides the most powerful and flexible way to understand marketing performance. It lets us incorporate prior knowledge, account for uncertainty, and generate forward-looking predictions – which is what you need from your …

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Optimize Your Marketing Mix Modeling with the Ideal Data Window

One of the most critical factors in building an accurate media mix model comes down to choosing the optimal historical window that it will digest. Too little data, and you risk missing important historical trends. Too much data and you introduce outdated information that no longer reflects current marketing performance. …

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