Introduction
Marketing Mix Modeling (MMM) has long been the go-to framework for quantifying business drivers. Yet, despite its widespread adoption, the industry often falls into the trap of celebrating vanity metrics rather than true predictive power. Whether MMMs are built in-house or delivered by consultancies, the critical question remains: are we validating models in a way that actually drives better decisions?
The Illusion of High R²
For years, R² has been paraded as the gold standard of model quality. Consultants proudly present models with R² values north of 90%, as if that alone signals success. But let’s be honest: anyone who has built an MMM knows how easy it is to game this metric. Add enough dummy variables, and you can make the past look perfect. The problem? Business leaders don’t need a model that explains yesterday. They need a model that guides tomorrow. And this is where most MMMs fall short.
Step 1: Demand Predictive Proof
Validation must go beyond statistical fit. A true test of quality is whether the model can predict unseen data. That’s why holdout testing should be non-negotiable. Feed the model data it hasn’t seen before and measure how well it performs. Shockingly, holdout tests are often neglected in MMM projects. Without them, predictive quality is little more than guesswork. If your provider isn’t showing you holdout results, you should be asking why.
Step 2: Sanity Check Against Business Reality
Even the most statistically sound model can fail if it doesn’t align with business logic. That’s why sanity checks are essential. However, too often analysts assume it is enough to validate the logic of static results that merely explain the past. That is a dangerous illusion, especially when the model functions as a black box with hidden structures and obscured data transformations. True validation requires more. You need to pressure-test the model with what-if scenarios that mirror real-world decisions, exposing whether it can guide the future rather than just retell the past.
Run what-if scenarios that reflect real-world decisions:
- If you want to grow brand awareness by 1 percentage point, how much should you invest in media, assuming the same media mix as last year?
- If the model suggests spending less to achieve more, you’ve uncovered a fundamental flaw.
Models must not only predict - they must make sense in the context of how businesses actually operate.
The Thought Leadership Takeaway
The industry needs to move past the obsession with high R² and embrace a more holistic view of validation. True model validation is a two-step process: statistical rigor through predictive testing, and business relevance through sanity checks.
Anything less is just analytics theater.
Marketing leaders should demand more from their MMMs. Don’t settle for models that look good in PowerPoint but collapse in practice. The future of MMM lies in models that not only explain the past but actively shape smarter, more confident decisions for the future.
If you want to know more about how we build and validate our models with full transparency, do not hesitate to get in touch. We believe that models should not only be statistically sound but also business-relevant, open, and trustworthy.