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 Too much data and you introduce outdated information that no longer reflects current marketing performance.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 …
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 »
1. Introduction This article will sound counterintuitive – Recast is an MMM vendor (within our incrementality system). So why are we saying you should not outsource your model – entirely? Because we’ve heard many stories of blind reliance on external teams which has resulted in misaligned incentives, unchecked errors, and …
Why Marketers Must Own Their MMM Results, Not Outsource Them Entirely Read More »
Two learnings from 2024: it was a great year for incrementality, but MMM took a much bumpier path. First, incrementality became a hot topic last year. Everyone’s talking about running experiments – and that’s amazing. We’re seeing marketing teams build out awesome experimentation roadmaps and learning agendas and sharing those …
The Rise of Incrementality: What 2024 Taught Us and Our 2025 Predictions Read More »
It will surprise you to hear this from us, but media mix modeling (MMM) is not the right solution for every brand – especially those in the early stages of growth. We typically identify three key indicators that suggest a brand should hold off on MMM: It’s okay to wait. …
MMM Isn’t Always the Answer: What Early-Stage Brands Should Prioritize First Read More »
