What makes an MMM model “good”? A data scientist’s perspective

When millions in media spend hang in the balance, one modeling error can cost your team its budget — and your brand its growth trajectory.

Marketing mix modeling (MMM) is supposed to solve a lot of our marketing measurement challenges, but what do you do when your results don't look anything like your predictions? 

Suddenly, you’re questioning everything. Is this model reliable? Did we use the right variables? What went wrong? 

To answer these questions, you need to look deeper, not just at what the model shows, but how it's working behind the scenes.

Why MMM model quality matters

The quality of your MMM model is critical — it's the engine behind your most important marketing decisions. When the foundation is weak, everything from campaign planning to budget allocation becomes a gamble. A poor-quality model can lead to misleading insights, wasted marketing spend and missed opportunities across your marketing mix.

Nielsen found that most brands were underspending by a median of 50% to reach their optimal ROI, highlighting the cost of poor measurement and suboptimal budget allocation [Nielsen 2023, p. 22]. This underscores how incomplete measurement frameworks don’t just reduce insight — they actively suppress growth potential.

Inaccurate models can derail even the most promising marketing strategies. You risk underinvesting in high-performing marketing channels while pouring budget into marketing tactics that don’t move the needle on business priorities. 

Reliable marketing mix models, on the other hand, allow you to:

• Forecast the impact of future marketing campaigns with confidence.
• Identify the marketing mix elements that truly influence customer acquisition and sales.
• Make informed decisions that improve immediate marketing performance and support long-term brand equity.

At its core, marketing mix modeling is about more than regression outputs — it’s about precisely measuring marketing impact and turning that insight into smarter, more effective strategy. A stronger model leads to stronger decisions.

So, which metrics should you be using to assess the quality of your MMM model?

Core metrics for assessing MMM quality

When assessing your MMM model, focus on the metrics that reflect its predictive power and fit your business context. That means going beyond the frequently used and familiar R-squared.

Instead of R-squared alone, look to other metrics like normalized root mean square error (RMSE) and adjusted R-squared to get more nuance. Here’s why:

R-squared measures how much of the variation in your outcome (like sales) is explained by your inputs, such as marketing spend, promotions and seasonality. It shows you how well your model communicates the pattern of data, but it doesn’t show how accurate the predictions are.

It can look impressive, but a high R-squared doesn’t always mean a high-quality model. If you have too many irrelevant variables, R-squared will look inflated. 

The chart below shows a simple linear regression, where the x-axis is a predictor variable (independent variable) and the y-axis is a predicted variable (dependent variable). The blue line is the regression line — the model’s prediction — and the dots are the actual observed values. The vertical dashed lines show the residuals or the differences between the observed values and the predicted values.

In MMM, where you’re juggling many marketing mix elements — TV, digital marketing, out-of-home and more — adding more predictors will almost always increase R-squared, even if they don’t improve the model’s true predictive power.

Also, if you don’t have enough data variability (hello flat sales), you might end up with a misleading result. 

That’s where other regression model metrics come in. Normalized RMSE shows you the root mean square error relative to the mean, so scale is baked in. That helps tie the effectiveness of the model to real-world accuracy because it shows you the average prediction error relative to actual sales. Adjusted R-squared can also give you further insights into the effectiveness of your model by correcting for overfitting. It does this by penalizing models that add irrelevant variables that don’t make the model more accurate.

To keep track of the most important metrics when evaluating your MMM model, here’s a quick-reference table summarizing what each one tells you — and where to be cautious.

MAPE, RMSE and predictive accuracy

R-squared tells you how well the model fits the past. But MAPE and RMSE tell you how well it can predict the future, and that’s what really matters for marketing decision-making.

MAPE shows the average percentage error between predicted and actual outcomes, while RMSE reflects the average magnitude of those errors. Both are key indicators of how well an MMM model can generalize to unseen data, not just fit the historical record.

While there are no universal MAPE benchmarks, industry experts often consider values in the 5–15% range reasonable for well-specified models with clean data. Lower MAPE and RMSE values are generally preferred because they indicate more reliable predictions.

Multicollinearity and variable significance

Too many variables, too much overlap. When predictors in your MMM model are highly correlated — say, TV and YouTube during a seasonal push — you get multicollinearity, which weakens the model’s reliability and warps your marketing analytics.

This makes it difficult to pinpoint which marketing channels are actually driving results. Instead of insights, you get noise.

Evaluating variable significance helps cut through that. Keep the variables that add value. Ditch the ones that don’t.

In a nutshell: reducing multicollinearity and focusing on only the most significant variables ensures your model highlights the real drivers of marketing performance, not statistical artifacts.

Once the basics are solid, machine learning can take your model quality to the next level, driving smarter, faster evaluation at scale.

Evaluating MMM models using supervised machine learning

Supervised machine learning powers many MMM models — and for good reason. It helps uncover real relationships between marketing efforts and business outcomes, not just statistical noise.

The process is straightforward:

1. Split your marketing data into training and holdout sets.
2. Train the model on historical marketing activities and sales data.
3. Test it on unseen data to validate predictive accuracy.

If the model performs well on the holdout set, it's a strong signal that your MMM can generalize, not just explain the past but forecast future marketing performance.

This kind of testing protects against overfitting and strengthens your ability to measure marketing effectiveness. It ensures your model captures how different marketing mix elements influence key business metrics.

What to remember: A well-tested model gives you confidence that your marketing mix decisions are based on patterns that hold up, not just in hindsight but in future campaigns.

But even strong technical performance isn’t the finish line. If your MMM can’t drive clear, actionable decisions, it’s not doing its job.

Business actionability: using marketing mix modeling to drive smarter decisions

You want your model to help guide future actions. A model that can’t guide budget decisions — no matter how well it explains the past — that's not what it's supposed to be doing. 

Here’s an example of how Funnel applied this principle with a fashion brand we worked with.

Using a unified measurement approach that combines MMM with platform attribution and view-through data, the team applied a triangulation method to validate the impact of upper-funnel campaigns across Meta, TikTok and display prospecting. This gave our client the confidence to reallocate budget toward upper-funnel tactics.

After reallocating the budget, our client saw a 77.7% increase in revenue, steady CPA and an 8% lift in average basket value, showing that data-backed media mix decisions can directly improve business outcomes.

Here’s another way to look at it: your model might identify that TV ads correlate with sales spikes, but it might not clarify whether a 10% increase in TV spend would lead to the same result next quarter. That’s where predictive accuracy and business actionability come into play. Models that can recommend real, data-backed changes — like reallocating budget or optimizing a specific channel — are far more valuable than models that just look good.

Role of geolift tests and real-world validation

Geolift tests are one of the most effective ways to validate MMM predictions in real-world conditions. By isolating marketing activities in specific regions, these tests strip away noise, letting you measure true incremental impact.

One of the most well-known applications of this kind of testing came from Uber. Faced with rising acquisition costs and mounting attribution uncertainty, the company ran a geo-based incrementality test across Meta ads in the U.S. and Canada. The results were clear: the ads were virtually non-incremental. Backed by both MMM and holdout results, Uber turned off the campaigns, ultimately saving $35 million and reallocating that spend toward higher-performing channels like Uber Eats and driver acquisition.

Unlike pure model diagnostics, geotesting shows whether your MMM holds up outside the spreadsheet. If your model predicts a sales lift from TV or digital marketing, a geotest can prove whether that lift actually happens in-market.

What to takeaway from this? Don’t rely on metrics alone. Validating your MMM with geolift and holdout tests ensures your model reflects real marketing performance, so you can act with confidence across your broader marketing strategy.

Want even more confidence? Another method for validating MMM is to use synthetic data to create a hypothetical model. 

Using synthetic data for model validation

Using synthetic data can help you determine if the model captures what you already know to be true. Here’s how to do it:

• Create a synthetic MMM data set, with spends per channel.
• Generate a synthetic MMM model, with respective coefficients per channel (and input variables, including non-marketing effects) and channel attribution.
• Use the synthetic MMM to generate the target KPI (conversions or revenue) values by plugging in the spend per channel.
• Split the resulting data into training and test sets.
• Go through the whole modeling process with the synthetic data.
• Measure how close the resulting MMM comes in terms of attribution per channel to the synthetic MMM.
• Calculate channel-level MAPE.

Even with solid validation in place, MMM isn’t foolproof. To build models that truly drive marketing effectiveness, it’s just as important to know what mistakes to avoid.

Common pitfalls in MMM and how to avoid them

Even the most technically sound MMM can go wrong if you’re not aware of common traps. The following missteps can undermine marketing effectiveness, distort business outcomes and lead to bad decisions.

Overfitting vs. underfitting

Overfitting happens when your model clings too tightly to the training data, picking up noise instead of true marketing signals. You’ll see inflated R-squared values but poor predictive accuracy on test data. To fix it, use cross-validation methods and apply regularization techniques like Ridge or Lasso to simplify the model.

Underfitting is the opposite: the model is too simple to capture real relationships. This shows up as low R-squared and high error across both datasets. To improve, expand your feature set, consider interaction effects or explore non-linear transformations.

In both cases, your goal is generalizability — a model that accurately predicts unseen data, not just historical patterns.

Misinterpreting model outputs

Statistical significance doesn’t always mean business significance. A variable might be mathematically important but irrelevant to your marketing strategy. Work closely with your marketing analytics team to ensure your model drives real, measurable impact.

Model design tradeoffs

When designing your model, you have to strike the right balance between interpretability and complexity — which direction to swing depends on what your MMM goals are.

If you need to communicate results that non-technical stakeholders understand, simpler models — such as linear regression and time-series analysis — are easier to audit, explain and trust. These models often work well when relationships are linear and you have well-thought-out inputs.

On the other hand, more advanced models — like gradient boosting, random forests or Bayesian models — are better for capturing nonlinear interactions and saturation effects. Basically, you get more nuance, which can power better decisions. However, they’re also harder to interpret and can obscure how specific channels influence outcomes.

Best practices for building a ‘good’ MMM model

Avoiding common mistakes can help you keep your model on track, but there are also a few best practices you should follow to help you get the most out of MMM.

1. Align business questions and objectives: Align model design with your business questions. Every variable, transformation and test should help answer something that actually matters to your marketing strategy.
2. Start simple: Begin with a straightforward linear regression to understand baseline relationships before layering in complexity. Trying to capture every nuance from the start often leads to overfitting or confusion. Build confidence in the fundamentals first.
3. Factor in diminishing returns: Marketing isn’t linear. The impact of spend typically tapers off after a point, and ad effects often decay over time. Incorporating these patterns will make your model more reflective of real-world behavior.
4. Data quality beats data volume: You don’t need a massive dataset — just the right one. Consistency, accuracy and proper alignment between spend and results are more important than having millions of rows.
5. Know the limits: Marketing mix modeling works best for businesses with steady investment levels and longer sales cycles, especially in consumer-facing industries. It’s less suited to short-term tests or B2B environments with complex conversions.
6. Use the tools available: Machine learning and automation can accelerate model updates, flag anomalies and surface insights faster — all without sacrificing control.
7. Don’t use MMM in isolation. Lean on triangulation — combining MMM with multi-touch attribution and incrementality testing — to fill in the gaps of MMM.

Insights you can trust, decisions you can defend

A solid MMM model won’t just report what happened — it will show you why and what to do next. That level of clarity doesn’t come from guesswork or surface-level metrics. It comes from rigor, iteration and a clear understanding of how the model performs under pressure. 

Whether you’re building the model yourself or reviewing one someone else built, the goal is the same: a faster time to reliable insights that drive decisions you can back up with evidence.

When the stakes are high and the budgets bigger, the difference between “just OK” and “good” modeling becomes a competitive edge.

So, whether you’re auditing an existing model or starting from scratch, start with clarity, validate with rigor and always optimize for actionability. That’s how MMM becomes a strategic advantage — not just a statistical exercise.