Marketing Mix Modeling

Not all MMMs are created equal. At Recast, we’ve embraced a Bayesian approach to MMM because it gives us flexibility to encode what we already know about the business, and an honest accounting of how confident we are in each answer. However, no Bayesian MMM is trustworthy without a careful […]

In marketing, every forecast comes with some level of uncertainty – whether you’re predicting sales growth, estimating the impact of a new campaign, or adjusting your media mix. There’s no crystal ball and, too often, marketers fall into the trap of treating forecasts as absolute, when in fact, they represent

“I ran an incrementality test showing positive lift but didn’t see the results in the bottom line.” Sound familiar? I think we’ve all had this experience: we set up a nice, clean incrementality test to measure the effectiveness of a specific paid media channel. We get the results back: 5%

Does the carry-over effect problem mean you can’t refresh your media mix model more than once a quarter? No.  Sadly, I’ve seen a number of “experts” claiming that because marketing effects have long carry-over periods you can’t refresh a media mix model frequently. This is simply wrong and just doesn’t

Is your incrementality testing partner hiding something from you? We’ve seen it too many times: brands come to us very proud of running an incrementality study and they share the results with us and say something like: “Look, we ran an incrementality study and there was a 3% lift and

Correlation vs causation is a huge marketing measurement problem that traditional methods often fail to account for and can lead to biased conclusions and misguided budget allocation.  This is where Directed Acyclic Graphs (DAGs) come into play. DAGs offer a powerful, visual approach to mapping out and clarifying causal relationships

Over the past few decades, the scientific community has grappled with what is now known as the replication crisis—a widespread recognition that many significant results published in academic research cannot be replicated under rigorous testing. This crisis has shown critical flaws in traditional statistical methods, particularly the over-reliance on P-values,

Can you trust your media mix model? That is an obvious but not asked enough question that everyone using MMM should consider – in-house, with a vendor, it doesn’t matter. As you try and answer it, you’ll run into overfitting, which is one of the biggest problems in MMM. Overfitting