How to validate your MMM accuracy

After investing significant time and resources into developing a Marketing Mix Model (MMM), how can you be sure it delivers accurate insights that truly reflect reality? MMMs are designed to optimize your marketing budget by identifying where to allocate spend for maximum ROI. However, the challenge lies in validating whether the model captures true causal relationships between marketing actions and sales outcomes, rather than just correlations. This problem centres on ensuring your MMM is not only statistically sound but also aligned with real-world dynamics to guide confident, data-driven marketing decisions.

The Dilemma: Reality vs. Model

At its core, Marketing Mix Modelling is about uncovering the incremental impact of every marketing dollar spent. It’s the difference between your actual sales results with your marketing efforts and what they might have been without those efforts. Ideally, we’d have a clear view of this alternate reality, but in practice, we rely on sophisticated models. And while these models are powerful, they come with challenges, especially when it comes to establishing causality.

MMM is not just about finding correlations between marketing spend and sales. It’s about understanding causality—ensuring that when you invest in Channel X, your sales increase by Y as a direct result. However, the real world is unpredictable, full of external variables that can complicate the picture. This is where the complexity of model validation comes into play.

Validation: The Art of Ensuring Accuracy

Not all validation techniques are created equal. The challenge in validating an MMM lies in confirming that your model doesn’t just produce statistically significant results but that it accurately reflects real-world dynamics.

So, how do you distinguish between a reliable model and one that’s just fitting the data? Here are three strategies to ensure your MMM is not only mathematically sound but also a dependable tool for decision-making.

1. The Power of Experiments: Conversion Lift Studies

Think of it like this: you’re a chef experimenting with a new dish. You prepare two versions—one with a special ingredient and one without—and serve them to two different groups of people to see which they prefer. That’s essentially what a conversion lift study does for your marketing campaigns. It’s the gold standard for understanding causality, providing clear insights into how much your marketing efforts truly move the needle.

In a lift study, you conduct a controlled experiment by showing ads to one group (the test group) and withholding them from another (the control group). The difference in behavior between these groups represents your lift—the true impact of your campaign. By integrating these results into your MMM, you can fine-tune the model, ensuring it aligns more closely with real-world outcomes.

But here’s an important point: if there’s ever a disagreement between your MMM and a well-executed lift study, trust the lift study. Models, no matter how sophisticated, are built on assumptions, while lift studies show you what actually happened.

2. The Test of Time: Out-of-Sample Validation

Imagine your MMM has been providing solid predictions based on historical data. But how do you ensure it’s not just a lucky streak? This is where out-of-sample validation comes into play.

Think of it as putting your model through a time-travel test. You train it on data up to a certain point—say, three months ago—then fast-forward to the present and see how well it predicts what actually happened. If the model can accurately forecast the future based on past data, you’ve got something reliable.

This isn’t just a checkbox exercise; it’s about building confidence in your model. If your MMM consistently delivers accurate predictions, even as your marketing strategy evolves, it’s a good sign that your model is capturing the right causal relationships.

3. The Real-World Test: Dynamic Budget Optimization

Let’s say your MMM suggests increasing your TV ad spend while reducing your investment in Facebook ads. You could take this advice at face value—or you could test it in the real world. This is where dynamic budget optimization comes into play. By adjusting your budgets according to the model’s recommendations and observing the results, you can see if the model’s predictions hold true.

This approach is like giving your model a stress test in the real world. If your overall marketing efficiency improves following these adjustments, it’s a clear indication that your MMM isn’t just theoretically sound—it’s practically effective.

Building Trust: Validating MMM Forecasts

To rely on MMM forecasts, the entire organization needs to have confidence in them. The most effective way to build this trust is through two key practices:

1. Backtesting Forecasts: This involves showing how the model would have performed historically by making predictions based on past data, much like hedge funds test trading strategies.
2. Demonstrating Consistent Accuracy Over Time: Regularly proving that the model’s predictions align with actual outcomes is crucial.

Backtesting: Learning from the Past

Backtesting is done during the initial model build. The model is repeatedly retrained up to specific points in time, and then it forecasts the data it hasn’t seen during training. This method helps verify the model’s reliability by simulating how it would have performed in the past.

To ensure consistent accuracy over time, a robust MMM process should include:

• Automated Weekly Runs: Regular updates to the model help keep it aligned with the latest data.
• Parameter Saving for Forecasting: Saving model parameters each week allows for accurate future forecasts.
• Rolling Forecast Accuracy Tests: Continuously testing the model’s predictions against actual outcomes (e.g., checking the 60-day accuracy on a model trained 60 days ago) helps validate its reliability.

Accurate backtests build confidence that the model will make reliable forecasts in the future and that its parameters, such as ROI estimates, are trustworthy.

Different businesses will experience varying levels of backtest error due to:

• The inherent “noise” in the business, which depends on factors like industry and KPI selection.
• The influence of external factors, such as interest rates or consumer trends, which might not be predicted by the MMM.

If you encounter high forecast errors or significant changes in weekly outcomes, reaching out for expert support can help diagnose and refine your model.

The Takeaway: Validation Isn’t Optional—It’s Essential

In conclusion, validating your MMM is not just a good practice—it’s a critical necessity. Without proper validation, you risk making decisions based on a model that might not accurately reflect reality. The beauty of using techniques like conversion lift studies, out-of-sample validation, dynamic budget optimization, and backtesting is that they ensure your model is not only statistically robust but also practically useful.

When selecting an MMM solution, whether from a vendor or developed internally, the focus should be on how it’s validated. A model that hasn’t been rigorously tested against real-world data is not one you can confidently rely on.

So, before you put your marketing dollars on the line, make sure your MMM has been thoroughly validated. Your future success depends on it.