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How Geo Experiments Can Calibrate Your MMM

December 10, 2025

10 min read

Written by:

Emily Sullivan Content Marketing Strategist

Emily Sullivan is an experienced marketing professional with over a decade of expertise in content creation, communications, and digital strategy. She thrives on translating complex, technical subject matter into content that is approachable, insightful, and genuinely useful to marketing professionals navigating a fast-evolving landscape.

Table of contents

1. What are geo experiments?
2. How geo-based experiments work
3. Using geo experiment results to calibrate your MMM
4. Benefits of combining geo experiments and MMM
5. Tips for implementing geo experimentation
6. Making MMMs smarter with real-world data

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Whether you sell in one country or all of them, geographics play an enormous role in optimizing your marketing efforts—or, at least, they should.

Most marketers are familiar with media mix modeling (MMM), an approach to advertising analysis that hinges on past data. But many marketing teams may not know how to properly differentiate historical correlation from real-world causation.

It’s not entirely your fault. MMM relies on observational data, making blind spots an inevitability. That’s where geo experimentation comes in.

With geo experiments, you can validate and calibrate your MMM by discovering the actual incremental lift from controlled regional testing. This guide to measuring ad effectiveness using geo experiments will help you do just that. We’re exploring how geo experiments work, why they’re essential for improving MMM accuracy, and how you can integrate them into your performance measurement framework.

What are geo experiments?

Geo experiments (AKA geographic lift tests) are randomized marketing tests conducted across geographic regions. Unlike many A/B tests that zoom in on individual consumers, geo experiments occur on a larger scale, studying the behavior of hundreds or thousands of users simultaneously.

The goal? To isolate the true causal impact of a brand’s marketing efforts.

In other words, a geo experiment is a sort of “real-world laboratory” where marketing teams can measure how much of the incremental lift, if any, is attributable to a particular campaign. By showing ads to consumers in some regions and withholding them in others, marketers can learn which advertising outcomes are legitimate—and which are just coincidental.

Why do they matter for MMM accuracy?

It’s worth running geo experiments because, without them, your MMM data may be incomplete or faulty.

On their own, MMMs require brands to make assumptions about their marketing impacts. Because all the data informing an MMM is historical, the framework excels at finding patterns from the past. Where it falls apart, however, is when predictions are part of the picture.

Essentially, there’s no way for an MMM alone to prove that its insights aren’t simply correlations. The best an MMM without geo experiments can do is guess. For modern marketers tasked with validating ad spend, that’s not good enough.

Fortunately, geographic lift tests can fill in the blanks. Geo experiments test MMM assumptions in actual market conditions, then provide guidance on how to remove any model bias from the equation.

How geo-based experiments work

By now, you should be on board with running geo experiments. That means it’s time to take the leap.

So, how do geo-based experiments work? These tests are designed and executed with five steps:

Region selection – First, you’ll select your regions (or “geos”). Geos should be carefully chosen to ensure you collect accurate data. When choosing markets, similarity is the name of the game; pick geos with similar historical performance and audience characteristics.
Random assignment – Once you’ve picked your locations, you’ll divide them into two groups: Test Geos and Control Geos. Your Test Geos will be exposed to ads, while the Control Geos will remain ad-free.
Campaign execution – From here, all you have to do is press “Publish.” Whether you’re running a TV spot, an OOH campaign, or a social media blitz, it’s time to sit back and let your marketing convert those consumers (hopefully).
Data collection – The good news is that, whether or not your campaign delivers the financial results you were expecting, you’ll still gain invaluable insights for your next push. Using the data gathered in your geo testing, you can measure outcomes like sales, site traffic, or brand lift.
Analysis – Finally, you’ve reached the “A/B” part of your A/B testing. Compare the exposed Test Geos against the unexposed Control Geos to determine your campaign’s impact on ROAS. If your marketing efforts are actually moving the needle, you’ll notice a marked difference between the two groups: your incremental lift.

That’s a quick summary of how to get the data. But what do you do with it once you have it?

You pit it against your MMM.

Using geo experiment results to calibrate your MMM

The main problem with MMMs is that they rely on historical regression analysis, leveraging past data to predict future performance. But businesses don’t grow in a straight line. The unpredictable happens all the time.

While historically rooted predictions can offer some accurate insights, more often than not, they drift from true performance if they’re not calibrated.

Fortunately, MMM calibration is easy enough when you have geo experiment results.

To start, you’ll use the incremental lift data from the tests as your truths. These are real-world measurements, not guesses. Compare the “true” data to the MMM’s predictions for the same period and region. Chances are, the figures won’t match up.

To fix the mismatch, you’ll have to correct the MMM coefficient. Doing so involves dividing the geo experiment’s ROAS by the MMM’s estimated ROAS.

For example, if the MMM predicted a 2% lift per dollar and the geo testing only uncovered a 1% lift, it means your media mix model is overestimating ROAS by half (0.5). That’s your new “calibration multiplier.”

From here, you can reweight your media channel effects to align with the actual causal outcomes you’ve observed. Multiply your MMM coefficient by the calibrator (in this case, 0.5), and you now have a model that reflects the truth.

Of course, you may need to run multiple geo experiments to validate and adjust the elasticities across all channels, including:

Digital
TV
Print
OOH
CTV

Benefits of combining geo experiments and MMM

As you can see, calibrating your MMM is no simple task. So, is the juice worth the squeeze?

In short, yes. MMM calibration through geo experiments offers several benefits, including:

Improved model reliability – Integrating real-world results into an otherwise theoretical model is the best way to increase predictive accuracy. Your team essentially goes from guessing to knowing.
Better confidence in media investment decisions – When you know truth from fiction, you can act fearlessly. No model offers 100% accuracy, but a calibrated MMM will act as a compass, pointing you toward the best possible choices. What’s more, verified data inspires buy-in from those who hold the purse strings, allowing marketers to invest more in successful strategies.
Continuous adjustments – Markets change. Geo experiments help you keep up with those consumer shifts. Once you know how to run and interpret geographic testing, you can use it regularly to keep your MMM current.

Tips for implementing geo experimentation

Because geo experiments and MMM calibration can be complicated, we’d recommend partnering with a marketing science company. However, if you plan to take on geo-based experimentation on your own, keep these challenges and best practices in mind.

Common challenges

Marketers and analysts face a range of challenges during geo testing, which can include:

Geographic variability – No matter how similar two regions appear, there are always inherent differences. From cost of living to climate, geos are never a 1:1 match.
Data latency – Marketing outcomes are rarely instant, especially for non-digital channels. Marketing efforts lag and decay, as seen in the concept of adstock. It can sometimes take up to two months to understand the full causal impact of a campaign.
Sample size limitations – Some brands don’t operate in enough regions—or lack the volume in those regions—to run valuable tests. When your sample size is small, it’s hard to say if an increase is a true causal lift or a blip.

Geo experimentation best practices

These geo experimentation challenges are troublesome, but not insurmountable. With the right strategies, you can overcome them:

Match baseline performance – Choose test geos with as close a baseline performance as possible. For example, if you’re testing in U.S. cities, pick locations with similar demographics and spending habits (i.e., don’t compare suburbs to bustling urban centers). The more similar your geos are, the cleaner your data will be.
Ensure campaign isolation – To avoid muddying your insights, make sure that the only thing you change in your testing is the region. Avoid modifying the creative, the ad spend, or any other variable.
Extend your experiments – Run your tests long enough to capture the full impact—a minimum of six weeks is recommended. Short tests run the risk of being skewed by seasonal shifts or one-off flukes.
Partner with experts – When in doubt, turn to the pros. By working with experienced analytics partners, you can design statistically sound experiments that won’t waste your time or your ad spend.

Making MMMs smarter with real-world data

If MMMs are based in history, geo experiments are grounded in science. When you bring them together, you gain access to a robust, invaluable tool—one that centers wholly on real-world evidence from the past and the present.

Through regular geo experimentation, MMMs become more than predictive tools; They morph into adaptive systems that can unlock never-before-seen insights, guide smarter media planning, and improve ROI.

If that’s the kind of solid foundation your marketing needs, get in touch with fusepoint. Our marketing performance measurement consulting services can help you develop an integrated analytics strategy rooted in accurate data experimentation and modeling.