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 Too much data and you introduce outdated information that no longer reflects current marketing performance.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
Product Update: Multi-Stage Models Read More »
Media mix modeling (MMM) isn’t theoretically hard to do, but it’s really hard to do well. Every step of building an MMM is a potential place for it to break, inject bias, and can lead to inaccurate results. One of these “steps” we find that is often not discussed enough
Why Model Configuration Makes or Breaks Your MMM Read More »
Not all MMMs are created equal. At Recast, we’ve embraced a Bayesian approach to MMM because it allows for more flexibility and actionable insights than traditional methods. However, the success of any Bayesian MMM hinges on one critical element: good priors. This article explores why priors are so important, how
Why Good Priors Are the Key to Accurate Bayesian MMM Read More »
You spent millions on a brand awareness campaign – great! But now you’re left with a tough question: how do you measure if it worked? Unlike performance marketing, where you can measure ROI in days or weeks, brand campaigns often deliver results over months or even years. And, like every
Why Measuring Brand Campaigns with MMM is So Hard – And What You Can Do About It Read More »
