Media mix modeling (MMM) has become a buzzword for marketers chasing smarter insights—but is it actually right for your brand right now?
MMM is a statistical model that looks at historical data to estimate how different marketing channels contribute to overall performance. It’s powerful. But it’s also resource-intensive and not always necessary.
When MMM Makes Sense
MMM is most useful when:
- You’re managing 6+ marketing channels
- You need to forecast revenue based on budget shifts
- You want to understand seasonality, promotion impact, or offline-to-online halo effects
- You’ve outgrown attribution and lift tests as standalone tools
MMM is particularly valuable when controlled experiments are hard to run—think linear TV, retail media, podcasts, or print. It gives you a high-level map of how your media efforts interact.
But It’s Not Plug-and-Play
MMM isn’t a dashboard you set and forget. It requires:
- Clean, multi-year data (messy inputs = faulty outputs)
- Cross-functional support, especially from finance
- Time and resourcing to validate, maintain, and refine models
And it must be causal. If an MMM can’t predict future performance or be validated against real-world tests, it’s not worth trusting. Look for consistency with past experiments and check that its outputs align with business reality.
When to Wait
If you’re spending under $5M in media or still running effective lift tests, focus on building your experimentation infrastructure first. Often, early MMM implementations fail because the foundation isn’t there.
The Takeaway
MMM isn’t about proving that media works—it’s about deciding how to spend smarter. It’s a strategy tool, not a scoreboard. Use it when your business complexity demands better forecasting, not because it’s trendy.
When the tools you have stop answering the questions you’re asking, that’s when MMM starts to make sense.
