Marketing Mix Modeling

Media mix modeling is a waste of money unless you do something with it. A great model sitting on a shelf is a model that has failed. And this happens too often – the leadership team signs the contract, the analysts get the recommendations, they share them in a meeting, […]

A brand we were working with missed its forecast by $10M. Immediately, you’d think that Recast made a bad forecast but, when we looked into what happened, the planned mix was $15M TV / $25M Facebook vs the executed mix which was $5M TV / $50M Facebook.  That’s a fundamentally

There are two types of “accuracy” under media mix modeling, and it’s easy to confuse them, which leads to many marketing teams not having an aligned measurement program.  The first is attribution accuracy: how the model assigns credit across channels in historical data. This is the world of decompositions and

A redditor recently asked us how to break into causal inference when their current company doesn’t offer opportunities to work on it. This is a common roadblock for data professionals trying to build expertise in a new area, so we’re sharing our response publicly for anyone facing the same challenge.

Every planning cycle ends with the exec team asking questions like the above, and most marketing teams have no hard data to answer them. That’s because most teams plan one budget and execute another – not because they’re sloppy, but because, well, reality forces them to. CPMs spike: maybe a

The math and science part of media mix modeling is extremely hard to get right. But even when you do, many MMMs will still fail because the system around them rewards the wrong outcomes.  There is enormous pressure in marketing measurement to show that things worked. Marketers want to prove

One of the most overloaded concepts in media mix modeling right now is “model refresh.” Most senior marketers hear that word and reasonably assume it means the model has been updated to reflect reality. New data went in. Old assumptions were challenged. Channel performance was re‑estimated. That’s what a refresh

Most media mix models get judged on the wrong things. We see this often when a media mix model returns a high R², and it’s blindly taken as a big sign of confidence. But in-sample goodness-of-fit metrics are not useful for evaluating MMMs.  They can be interesting to look at

Imagine this: your MMM refreshes, and suddenly last week’s top-performing channel is now your worst. You had just told your CFO that Meta was your most incremental channel and we should increase our budget there because your MMM told you that… and now the model is saying the complete opposite.