Taking Your Media Mix Models from Stable to Truly Robust

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 results hold up when faced with new data, real-world changes, and different validation methods.

Instead, you want your model to be robust.

In this article, we’ll break down the key differences between stability and robustness, go through some common red flags that might come with an unreliable MMM, and walk you through the rigorous validation process we use at Recast.

Stability vs. Robustness

Many marketers confuse stability with robustness, but they are fundamentally different concepts.

1. What Is Stability?

Stability means that a model’s results don’t change much over time. This might sound like a good thing, but stability alone can be misleading. A stable model that produces the same results every time could still be completely wrong.

2. What Is Robustness?

Robustness means that a model’s results don’t change unpredictably when minor adjustments are made – but it also means that the model can adapt when real, meaningful shifts happen in the data.

Many MMM vendors artificially enforce stability by freezing coefficients, using overly restrictive priors, or running their model on a fixed dataset that never gets updated.

This is not robustness – it’s cheating. If a model’s results change drastically when you add or remove a few weeks of data, at least one of those versions is wrong. If a model is truly robust, it should hold up under pressure.

Is your media mix modeling robust? Spotting red flags.

The danger of an unreliable MMM isn’t just that the model produces incorrect insights, but that those insights seem reasonable enough to trust. So, how do you know if your model is robust? Here are some red flags you should keep an eye for:

• Huge Swings in Channel Performance After Model Updates
  
  • If last week’s best-performing channel is now suddenly underperforming, the model is overreacting to minor data changes. This often means the model is too sensitive and not picking up true causal relationships.
    
• Vendor Excuses for Avoiding Model Refreshes
  
  • If your MMM vendor hesitates to rerun the model with fresh data, that’s a problem. Ask: them why they don’t want to test it under new conditions?
    
• The “Frozen Coefficient” Trick
  
  • Some vendors lock coefficients to “force” stability, but this prevents the model from learning from new data. A good model should update its estimates without flipping unpredictably.

The Recast Approach to Building a Robust MMM

Robustness is something we think a lot about at Recast. Here’s the rigorous testing process we follow – with special emphasis on Stability Loops, which help us make sure small changes in data don’t lead to wild swings in results.

1. Configuring the Model for Your Business

Every business has a unique marketing mix, so a one-size-fits-all model won’t cut it. We customize each MMM to reflect the specifics of each business: how your channels interact, how your budget is allocated, and what constraints exist. 

Before anything else, we run sanity checks to ensure the model aligns with fundamental assumptions of real-world dynamics.

2. Parameter Recovery

To test whether the model is working as intended, we generate synthetic datasets where we already know the true parameters –things like channel ROI and diminishing returns. 

We then run the model on this data to see if it can recover them correctly. If it can’t get the right answers when we already know the truth, it won’t be reliable when we feed it real-world data.

3. Holdout Testing for Predictive Accuracy

To test if the model can predict future outcomes, we hold back 30, 60, or 90 days of data and have it predict what should have happened during that time. 

If the predictions don’t line up with actual performance, it’s a sign that the model is overfitting to historical patterns.

4. Running Stability Loops

Then, we run what we call Stability Loops. Here’s how it works:

1. We run multiple versions of the model, each time incrementally adding or removing small portions of data (for example, adding or subtracting a single week of data).
   
2. We check whether the results remain consistent or if they swing wildly between runs.
   
3. If small tweaks in the dataset drastically change the estimated ROI for a channel, something is wrong with the model.
   

A robust model should evolve as new data comes in, but those changes should make sense. If your MMM says Meta is your best-performing channel this week and then suddenly ranks it last the next week, your model isn’t robust—it’s overfitting to random fluctuations in the data.

5. Weekly Updates

We update our models weekly – not because we think you should be making radical budget shifts every week, but as proof that our model is robust. We put our neck on the line by continuously refreshing the model, making sure our results stay stable.

TL;DR:

• A stable MMM can still be completely wrong. What matters is whether it’s robust enough to handle real-world marketing conditions.
  
• Before trusting an MMM, ask: If I rerun this model with slightly different data, will I get the same insights? If the answer is no, your model isn’t robust.
  
• Recast’s approach:
  • Frequent updates to prove robustness
  • No hidden tricks like frozen coefficients or cherry-picked datasets.
  • A rigorous testing framework (parameter recovery, holdout testing, Stability Loops) to make sure the model produces repeatable, accurate results.