Does Your MMM Model Pass the Sign Test?
If Your MMM Coefficients Flip Signs, Your Model Might Be Lying to You
In Marketing Mix Modeling (MMM), accurate attribution starts with accurate coefficients.
Every downstream output be it saturation curves, diminishing returns, or budget optimization is a direct artifact of the model’s coefficients.
That’s why at Aryma Labs, we treat sign consistency as a first principle check for model robustness.
Enter Bootstrapping - A Must Have for Reliable MMM
Bootstrapping simply put is a method of repeated sampling (with replacement) of a sample. The sample chosen is assumed to be a good representation of the population.
Now the question one might have is, what do we gain by bootstrapping?
Bootstrapping should be seen as a method to learn about the sampling distribution rather than a method to estimate the population parameter.
Because our model is technically a 'sample', we can learn a lot about this sample through repeated sampling.
If we take simple linear regression as example, the model is fit to data and is used to make inferences about a larger population, hence the implicit assumption in interpreting regression coefficients is that the sample is representative of the population.
The question then arises about the quality of the sample and its estimate. How can we be sure of the coefficients in linear regression?
Bootstrapping can provide information about the variability of the coefficients.
In MMM context, we ask the question - "If I were to rebuild this MMM model on slightly different data, would I still get the same coefficient signs?"
A clear Example
Let’s say you have TikTok spends from 2022 to 2024 in your MMM model.
Would you trust a model where TikTok’s coefficient is positive in 2022, turns negative in 2023, and flips again in 2024?
Probably not.
Certain variables must have a stable sign over time:
Media spends → Positive
Competition → Negative
Inflation / Tariffs / Covid → Negative
Sign flips are often a red flag for mis-specified models or poor data quality.
Our Approach
Starting last month, we have integrated Bootstrap Coefficient Sign Stability into all client deliverables.
Our reports now show:
- The sign stability of each variable across multiple bootstrap runs.
- Clients can further use our proprietary tool MMM Diagnose to evaluate their model across a dozen calibration metrics.
If you have questions on how much data points are needed to build a good MMM model or how to do this Bootstrapping test?
We recently published our breakthrough paper - How Many Data Points Are Needed for Stable and Accurate Coefficient Signs in Marketing Mix Models (MMM)? – A Bootstrap Approach.
P.S: The image is from our recent client deliverable. Only the names have been altered to protect client's data.
Thanks for reading.
Want to test if your MMM model flips variable signs? Reach out to us