Imagine this: Your company spends $10 million a year on advertising. You have a team managing campaigns across Google, Meta, streaming TV, and programmatic display. You’re tracking clicks, conversions, and cost per acquisition. But if you’re like many brands, you don’t have a clear picture of how these channels actually work together to drive results.

If this sounds familiar, you’re not alone. Most brands spending seven or eight figures annually on media lack sophisticated mathematical models to guide their decisions. They’re essentially flying a multimillion-dollar plane using only the dashboard signals and warning lights.

The Challenge With Platform Metrics

When marketers rely solely on platform-specific data, they fall into a predictable trap. Facebook tells you Facebook is working. Google insists your search campaigns are performing. But these platforms are measuring in silos, each claiming credit for conversions they may have barely influenced.

Search advertising looks particularly impressive because it captures demand at the bottom of the funnel. Someone searches for your product, clicks your ad, and converts. Perfect attribution in isolation. But what if they only knew to search for you because they recently saw your streaming TV ad? Traditional measurement completely misses this possibility and cross-pollination of messaging.

The Value of Media Mix Modeling

Media mix modeling (MMM) takes a fundamentally different approach. Instead of trying to track individual user journeys (which is becoming nearly impossible thanks to privacy changes), MMM analyzes patterns across all your marketing activities and business outcomes over time.

Think of it as correlation analysis on steroids. The system identifies relationships between your media investments and results, accounting for seasonality, promotions, pricing changes, and countless other variables. Over time, it learns which channel combinations deliver the best outcomes and where you’re leaving money on the table.

The insights can be incredibly revealing. MMM often reveals that brands are underinvesting in upper- and middle-funnel activities while overinvesting in bottom-funnel tactics that get all the attribution credit.

It can show you’re running media in fits and starts when consistency would build momentum. It might demonstrate that you can spend significantly more and still generate positive returns.

The Real Barriers to Adoption

So why aren’t more brands using these models? The honest answer is that it requires commitment. You need clean, consistent data across all channels, going back months or, ideally, years.

You need someone internally who champions the effort and actually uses the insights to make decisions. And yes, it requires investment, though AI tools are rapidly lowering the barriers to entry.

There’s also a less comfortable reason: accountability. Media mix modeling creates transparency that not everyone in the marketing ecosystem wants to see. When you can demonstrate, with statistical confidence, which investments are working, it becomes harder to justify spending based on hunches or vendor relationships.

What You Actually Need

The requirements are straightforward but not minor. You need consistent data streams segmented by channel, tactic, and time period. Your ad spend, impressions, clicks, and conversions should be tracked in the same format, going back as far as possible. The same goes for sales data, promotional calendars, pricing changes, and any other variables that meaningfully impact your business.

If you’ve worked with multiple agencies that named campaigns differently or didn’t preserve historical data, you’ll have blind spots. But you can start building clean data from the point you start moving forward. The sooner you begin, the sooner the models become reliable.

As for budget thresholds, brands spending at least a million dollars annually on media should seriously consider implementing MMM. Once you’re in the multiple millions, not having this visibility is bordering on negligent fiscal responsibility.

The Bottom Line

Current marketing initiatives generate more data than ever before. The irony is that most brands are making decisions with less certainty than they had a decade ago. Media mix modeling offers a way forward, using mathematics and pattern recognition to cut through the noise.

The question isn’t really whether these models work. They do. The question is whether your organization has the discipline to implement them properly and the courage to act on what they reveal. Because sometimes the data tells you things you’d rather not hear. But that’s exactly when you need to hear them most.

If you’d like to explore how MMM can help elevate your media investments and campaign performance, please get in touch.

To hear us talk more about this topic, you can also listen to this recent Contrary to Popular Opinion podcast episode, where we address the ins and outs of MMM. You can also find us on Spotify, Apple Podcasts, and YouTube.