If you’re freezing coefficients in your media mix model, your model is broken. It’s one of the most common, and damaging, shortcuts in MMM. Something looks off in your results, so instead of investigating the cause, someone manually locks in a coefficient they “trust” and reruns the model until things …
Why Freezing Coefficients in an MMM is a Bad Practice Read More »
Garbage in, garbage out. If a media mix model has any chance of working, the model has to be fitted on high-quality, accurate data. Keep in mind: marketing mix models are holistic models operating at an aggregate level, not at an individual channel level. What that means is that if …
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 …
Are Too Many Control Variables Damaging Your Media Mix Model? Read More »
Marketers often look for stability in their media mix models (MMM), assuming that if a model produces consistent results over time, it must be reliable. But stability alone doesn’t guarantee a good model. The real test isn’t whether an MMM produces the same results over time – it’s whether those …
Taking Your Media Mix Models from Stable to Truly Robust Read More »
The marketing measurement space has seen a surge in open-source marketing mix modeling (MMM) tools like Google’s Meridian and Meta’s Robyn. As an MMM vendor, we love this. It has brought in more people to consider MMM as part of their measurement mix and to start thinking about incrementality as …
Running an Open-Source MMM Is Easy. Trusting Its Results Is Hard. Read More »
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 …
Why Internal Validation Metrics Are the Wrong Way to Judge Your MMM Read More »
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 …
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 …
The 3 Hurdles of Estimating a Bayesian MMM: Saturation, Divergences, and Multimodality Read More »
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. …
Optimize Your Marketing Mix Modeling with the Ideal Data Window Read More »
Many businesses face tough challenges when it comes to measuring marketing incrementality because the nature of their product implies conversions that happen at multiple stages. Marketing activity drives new users or customers to the first stage, and then some proportion of those customers “convert” into a subsequent stage. This may …
