When evaluating a marketing mix model (MMM), two terms frequently come up: calibration and validation. While both processes aim to ensure your model delivers accurate insights, they serve fundamentally different purposes. Understanding this distinction helps you ask the right questions when working with MMM providers and evaluate whether their approach is truly rigorous.
Understanding calibration
Calibration is the process of adjusting a marketing mix model to improve its alignment with known performance data. Think of it as fine-tuning an instrument to ensure it produces the right notes. During calibration, the model’s parameters are modified to better reflect what we know about marketing performance from other trusted sources.
Calibration typically happens after the initial model is built but before it’s deployed for decision-making. It uses various data sources to inform adjustments:
- Test data from incrementality or geo tests
- Historical performance data
- Industry benchmarks
- Expert judgment
One critical point that’s often overlooked: using test data for calibration can make MMMs either more accurate or less accurate, depending on the quality of the test data and how well it aligns with the marketing reality your model needs to capture.
When a model is calibrated, its behavior changes. Calibration might adjust how much revenue the model attributes to certain channels, how it handles saturation effects, or how it models the carryover impact of marketing over time. These adjustments can be subtle or dramatic depending on the calibration approach.
Understanding validation
Validation, by contrast, is the process of testing whether a model delivers accurate predictions and insights. Rather than changing the model, validation checks if it works as intended by comparing its outputs to known outcomes. It’s like testing a weather forecast against what actually happens.
Validation should happen both during model development and periodically after deployment. There are several common validation approaches:
Holdout testing:
- Reserving a portion of historical data that the model doesn’t see during building
- Using this unseen data to test prediction accuracy
- Evaluating how well the model generalizes to new situations
Backtesting:
- Testing the model’s ability to predict known historical outcomes
- Checking if the model captures known seasonal patterns
- Verifying the model’s sensitivity to marketing changes
Ongoing performance monitoring:
- Tracking the model’s prediction accuracy over time
- Noting when accuracy begins to degrade
- Identifying when revalidation or rebuilding is needed
Validation is essential because it provides objective evidence of a model’s reliability. Without proper validation, there’s no way to know if your model’s insights reflect marketing reality or just statistical noise.
Quickly understanding the differences
We could go much more in depth about the technical differences between these two critical processes, but we’ll save that for our data science team. For marketers who want to understand how these processes impact their MMM, its ability to provide accurate measurements, and their own ability to optimize their marketing efforts, here are the critical things you need to know about how they differ:
| Calibration | Validation | |
| Purpose | Improve model accuracy by adjusting parameters | Verify model accuracy without changing the model |
| Approach | Actively modifies the model | Passively tests the model |
| Timing | Part of the model development process | Occurs both during development and periodically afterward |
| Impact | Changes how the model works | Doesn’t change the model, it measures its performance |
| Data Usage | Incorporates external data sources to inform adjustments | Compares model predictions to actual outcomes |
How they work together
Calibration and validation are complementary processes that work together to ensure model quality. A robust MMM approach needs both.
The relationship between these processes is often iterative:
- Build an initial model
- Validate its performance
- Calibrate to improve accuracy
- Validate again to ensure improvements actually helped
- Deploy the model
- Continuously validate performance
- Recalibrate when needed
This cycle ensures your model remains accurate over time as marketing conditions change. At Prescient, we’ve designed our platform to support this iterative approach, allowing you to see how calibration impacts validation metrics and make informed decisions about which model version best serves your needs.
Prescient clients will effectively see these two processes working together in our platform. We allow clients to run parallel models—one calibrated with specific data, like incrementality data, and one calibrated without it. They’re given the results of the validation process from both models, at which point they can choose the one they’d like to use within the platform to track and forecast their marketing performance.
Calibration and validation are done differently by MMM practitioners. That doesn’t mean if they do this differently than Prescient that they’re doing it poorly. But you should keep an eye out for these warning signs that a provider isn’t handling these processes properly:
- No clear validation metrics
- Claims that calibration eliminates the need for validation
- Unwillingness to share validation results
- No process for ongoing validation
Common misconceptions
Protecting your marketing organization from poorly tested models might sound impossible if you don’t have a data science background. That’s not true. You can safeguard your marketing efforts effectively by avoiding common misconceptions about models and the processes of calibration and validation. Misconceptions about calibration and validation that can lead to misplaced trust in models include:
- Calibration guarantees accuracy: While calibration aims to improve model accuracy, it doesn’t automatically succeed. Calibration with poor-quality data can actually reduce accuracy, which is why we let clients compare models after calibration and offer them accuracy scores on each. It makes sense to test some of your data for calibration; it doesn’t make sense to assume they’ll always make your MMM better.
- Validation is just a final check: Yes and no. Validation should always be done before you start using a model, but effective validation isn’t a one-time event but an ongoing process that continues throughout the model’s life.
- One can replace the other: Some providers suggest that thorough calibration makes validation less important, or that good validation means calibration isn’t needed. Both claims are false. These processes serve different purposes.
- Perfect models don’t need either: No model is perfect, and even the most sophisticated MMMs benefit from both calibration and validation. Marketing environments change constantly, requiring ongoing verification and refinement.
How this affects your MMM
Think of calibration and validation as two sides of the same coin in marketing measurement. Calibration adjusts your model to better align with what you know, while validation tests whether those adjustments actually improved accuracy. One without the other leaves you flying blind: calibration without validation means you’re tuning based on hope, and validation without proper calibration means you might be testing a fundamentally flawed model.
The relationship between these processes reveals something crucial about how MMMs should work. When you calibrate a model with incrementality data, for instance, you’re making a bet that this data will improve accuracy. But that bet needs to be validated. The test data might be flawed, the model might not be able to properly incorporate it, or the testing conditions might not reflect your actual marketing environment. Without running parallel models and comparing their validation scores, you’ll never know if your calibration made things better or worse.
This is where many organizations get stuck. They invest in expensive incrementality tests, calibrate their models with that data, and assume the work is done. But what if the test results were compromised by external factors? What if the model’s structure can’t properly represent the relationships revealed by the tests? These questions can only be answered through rigorous validation, and that validation needs to be continuous, not just a one-time check.
Where Prescient comes in
The good news is that modern MMM platforms can make this easier. At Prescient, we’ve built our platform around this exact principle: transparency about when calibration helps and when it hurts. When you bring incrementality data or other external sources to our models, we don’t just incorporate them and call it a day. We run parallel versions of your model—one calibrated with the data, one without—and show you the validation metrics for both through a feature called Validation Layer. You get to see, with concrete accuracy scores, whether the calibration actually improved your model’s ability to capture marketing reality.
This isn’t just about being thorough. It’s about making sure your measurement foundation is solid before you make million-dollar budget decisions based on it. Because here’s the truth: a well-calibrated but poorly validated model can be more dangerous than no model at all. It gives you false confidence in recommendations that might be systematically biased.
If you’re currently working with an MMM provider who can’t clearly explain how they validate their models, or who calibrates without showing you the accuracy impact, those are red flags worth taking seriously. Measurement shouldn’t require blind faith. It should be transparent, testable, and continuously validated against real outcomes.
Book a demo and we’ll show you what marketing measurement looks like when calibration and validation work together the way they should.
