Model calibration may sound like a technical term reserved for data scientists, but it’s a crucial concept for any marketer relying on marketing mix modeling (MMM) for insights. In simple terms, model calibration is the process of adjusting your MMM to better align with known performance data. But like many aspects of marketing measurement, model calibration is a double-edged sword—it can dramatically improve your model’s accuracy or significantly reduce it, depending on how it’s done.
We created this guide to help marketers understand what model calibration is, how it works, and what you should know before making decisions about calibrating your marketing mix model. You should know upfront that we’re simplifying. This isn’t a guide to model calibration for data scientists; you don’t need any understanding of machine learning models to understand what we’re doing and why it matters.
What model calibration actually is
Model calibration is the process of fine-tuning a marketing mix model to better reflect real-world performance. Think of it like tuning a piano—you’re adjusting the machine learning model to ensure it produces the right “notes” (or in this case, accurate predictions and insights).
When data scientists calibrate an MMM, they’re essentially making adjustments to how the model weighs different marketing channels and activities. They’re telling the model: “When you see this pattern, you should produce that output.” These adjustments aim to make the model’s outputs (its predictions) more closely match observed results from test data or other trusted sources.
Model calibration differs from validation, which is the process of checking if your model works well on new data. While model validation tells you if your model is reliable, calibration actively changes how your model works. We have a deep dive on [calibration vs validation] if you want to understand each process and their differences further.
The types of data used for model calibration
Not all calibration data is created equal. Different types of data bring different strengths and limitations to the model calibration process.
Incrementality test data:
- Aims to measure the lift created by specific marketing activities
- Generally focused on a single channel or campaign
- Usually conducted over a limited time period
- Designed to try to isolate the impact of specific variables
- Potentially affected by external factors or events
Geo test data:
- Aims to compare results across different geographic areas
- Can test various spend levels or campaign approaches
- Limited by regional differences that can’t be controlled for
- Typically runs for weeks or months, not years
- Potentially affected by external factors or events
Historical performance data:
- Based on your brand’s actual marketing results over time
- Includes seasonal patterns and long-term trends
- Captures real-world complexity
- May include confounding variables
External benchmarks:
- Industry averages and standards
- Competitor performance data
- Third-party research studies
- Helpful when your own data is limited
Each data source has its place, but marketers should understand the limitations. Incrementality and geo tests, while valuable, often capture marketing effects in isolation and over shorter timeframes than MMMs are designed to model. This mismatch can sometimes lead to model calibration that reduces, rather than improves, model accuracy (more on that further down).
How model calibration works in practice
The model calibration process varies across providers, but the fundamental steps are similar. Here’s what typically happens when calibrating machine learning models:
First, the base model is built using your historical data—sales, marketing spend, seasonality factors, and other relevant variables. This uncalibrated model represents the mathematical relationships between your marketing activities and business outcomes.
Next, calibration data (like test results) is introduced. The model compares its predictions to this new data source. For example, if your incrementality test shows that Facebook ads drove a 15% lift in sales, but the model predicted 10%, there’s a gap to address.
Although we’re simplifying, the model then considers the calibration data and entertains the possibility of adjusting to this new viewpoint. This might mean changing how much weight it gives to Facebook ads, adjusting how it models carryover effects, or modifying how it handles saturation. But it might also be unable to adjust to the change you’re essentially asking of it.
Finally, the newly calibrated model is tested against actual historical data to see if these adjustments improved overall accuracy. Did the changes make the model better at predicting what actually happened?
This seemingly straightforward process hides considerable complexity. The adjustments aren’t just simple multipliers—they can fundamentally change how the model interprets relationships between variables, potentially improving accuracy for the calibrated channels while reducing it elsewhere.
Model calibration methodologies across the industry
The marketing measurement industry takes various approaches to model calibration, each with different philosophies and methods.
Some providers advocate for aggressive calibration, adjusting their models to precisely match test results. The argument is that experimental data represents “ground truth” and should override the model’s initial patterns.
This approach risks something called “overfitting,” a situation in which the model becomes too specialized to the particularities of the test data (including any noise or anomalies present in that data) rather than capturing the true underlying relationships. When providers force their machine learning models to precisely match test results, they’re essentially telling the model to prioritize fitting that specific test data perfectly, potentially at the expense of the model’s ability to generalize to other situations.
Others take a more conservative approach, using calibration data as a guide but giving more weight to the original model structure. These providers argue that test data captures only a snapshot of marketing performance and may miss important long-term effects.
More sophisticated model calibration techniques include Bayesian and multi-source calibration. These aren’t mutually exclusive. A calibration can be both Bayesian and multi-source. Bayesian refers to how the calibration is being done, while multi-source specifies what is being used.
Bayesian calibration:
- Updates model parameters based on both prior beliefs and new evidence
- Weighs the reliability of different data sources
- Allows for more nuanced adjustments
- Better handles uncertainty
Multi-source model calibration:
- Incorporates multiple data sources simultaneously
- Weighs each source according to its reliability
- Reduces the risk of overcalibrating to a single test
- Creates more robust results
When evaluating model calibration approaches, be wary of providers who treat test data as infallible or who use calibration methods that can’t be explained clearly. The best model calibration approaches are transparent, consider multiple data sources, and recognize the limitations of all measurement methods.
The model calibration debate
The question of whether to calibrate MMMs with test data isn’t straightforward. Research shows calibration can either help or hurt model accuracy, depending on various factors.
Arguments for model calibration include:
- Model calibration can correct for confounding variables that the model missed
- It may bring model estimates closer to ground truth for specific channels
- It can make models more consistent with other measurement approaches
Arguments against model calibration include:
- Test data often fails to capture long-term and cross-channel effects
- Short-term tests may miss seasonal patterns that MMMs can detect
- Forcing models to match test results can distort other relationships
- Test data has its own limitations and biases
We’ve left causality out of each list because it’s a debate in its own right. Some believe that test data provides causal measurements that observational data can’t, but there are factors that cannot be controlled in many tests—and that means they cannot truly get to the root of causality. For more about this nuance, refer to our piece about why not all incrementality tests are rigorous RCTs.
Recent research found that models calibrated with test data performed worse on several key accuracy metrics, including mean absolute percentage error (MAPE) and normalized root mean square error (NRMSE). However, they performed better on other measures, suggesting the impact depends on what you’re optimizing for.
Why is this so important? Let’s go back to our piano analogy. We all know piano tuners as reliable tools that help us align our piano’s notes to a source of truth. But in the world of test data, you can get what is essentially a piano tuner that’s off. If you tune your piano to this tool that isn’t playing true B or C, it’ll throw off your entire piano and taint the music you play. That’s what we’re looking to prevent when we talk about calibrating an MMM with test data, or not. We need to figure out if we have an off piano tuner, and we think the only way to know is to test it out.
How to evaluate if calibration helped your MMM
Prescient aims to make this evaluation easy for our clients. We allow clients to run parallel models—one calibrated with test data, another calibrated without—and compare their accuracy scores side by side. That information is paired with our recommendation based on our data science and marketing expertise but, ultimately, clients can choose which model to use for themselves.
It’s a bit more complicated if you’re not working within the Prescient platform. Determining whether calibration improved your model requires looking at multiple accuracy metrics, not just one. Key metrics to consider include:
Prediction accuracy:
- How closely does the model predict actual revenue?
- Does it capture known sales patterns?
- Does it handle seasonal variations correctly?
Channel allocation stability:
- Do the channel contributions make logical sense?
- Are they consistent with other measurement methods?
- Do they align with your marketing expertise?
Forecast reliability:
- How well does the model predict future performance?
- Does it handle changing market conditions?
- Is it sensitive to actual changes in spend levels?
Signs that model calibration may have hurt your model include unexplained shifts in channel value, reduced ability to predict known outcomes, failure to capture seasonal patterns, or recommendations that contradict established marketing principles.
Making informed calibration decisions
You can see that there are implications to calibration methodology that affect your bottom line as a marketer. That means you need to know what questions to ask and what data to have ready if you’re a marketer working with or vetting MMMs.
Questions for your MMM provider:
- What calibration approach do you use?
- How do you handle conflicts between the model and test data?
- Can we see models with and without calibration?
- What metrics do you use to evaluate calibration success?
Data to have prepared:
- Complete documentation of your test methodologies
- Results from multiple tests across different time periods, if you have them
- Notes on external factors that might have influenced test results
- Your success criteria and key performance indicators
Remember that calibration isn’t a one-time decision. As your marketing mix evolves and you gather more data, you may want to recalibrate or even switch between a model calibrated with specific data and one calibrated without it, depending on your needs.
Key takeaways
Calibration can dramatically impact your MMM’s accuracy—for better or worse. Here are the essential points to remember:
- Calibration adjusts your model to better align with other data sources
- Test data can sometimes help calibrate, but has limitations and can sometimes erode the accuracy of your MMM
- Different calibration approaches yield different results
- Evaluating calibration requires looking at multiple accuracy metrics
At Prescient, we believe in giving marketers choices. That’s why we run parallel models, showing you accuracy metrics for each so you can make an informed decision about including or excluding data based on the results of that model calibration and what works best for your brand.
Because at the end of the day, the goal isn’t picking the perfect method—it’s getting the most accurate view of your marketing performance to drive better decisions.
