Modern marketing rarely happens in one place. Most brands operate across multiple geographies—states, regions, countries—each with its own market dynamics, spend levels, seasonality, and competitive pressure.
So when marketers ask, “Can we understand incrementality across all our geos, not just one?”—hierarchical models are how we do it.

This article breaks down what hierarchical models are in plain language, when they’re the right tool, how we use them in Marketing Mix Modeling (MMM), and the questions customers most often ask.

What Is a hierarchical model (in marketing language)?

A hierarchical model is an MMM framework that allows us to model multiple geographies at once while still respecting the real-world differences across those markets.

At a high level, think of it as:

One modeling structure → many geo-specific results.
Each geo gets its own incrementality read, but the model learns patterns that are shared across geos when appropriate.

It’s not one giant model that treats every market identically.
It’s not multiple models that completely ignore each other.
It’s a structure that lets us borrow signal where it makes sense and keep differences where they matter.

When hierarchical models are the right fit

Hierarchical MMMs are especially helpful when a business operates in multiple geographies but faces one or more of these constraints:

1. Some geos don’t have enough volume

Small geos—new markets, smaller regions, or lower-population countries—often produce too few conversions for a stable MMM on their own.
Hierarchical modeling improves reliability by sharing statistical strength from larger geos.

2. You need geo-level actionability

Finance or regional marketing teams often need answers like:

• “What’s the incrementality of Meta in Canada versus the US?”

• “Does TV perform the same in the UK as in Australia?”

Hierarchical MMMs let each geography stand on its own without assuming they behave identically.

3. You want a shared strategy, but geo-specific insights

Many companies run centralized strategies, but performance often varies by:

• competitive pressure

• consumer habits

• seasonality

• media costs

• channel maturity

Hierarchical models let you answer both “what’s true for the brand globally?” and “what’s true in each market?”

4. You want to use experiment learnings across markets

If you run a geo-experiment in one country, hierarchical modeling lets us use that result as a prior when estimating another geo—while still accounting for uncertainty.

How hierarchical models work (without the math)

Here’s the intuition.

1. Each geo gets its own coefficients

The core MMM parameters—like how responsive a channel is to spend—are estimated separately for each geography.
This ensures each geo’s results ultimately reflect its own data and behavior.

2. Some parameters are shared across geos

A few parts of the model (for example, certain ad-stock or retention-rate parameters) reflect qualities of the channel itself, not the geo.
Those parameters are “global,” but they tend to have minimal impact on the geo-level incrementality results.

3. Geos influence each other a little, but not enough to override reality

It’s natural to ask:

“Do strong results in the US bleed into Canada? Or vice versa?”

Short answer: Only slightly, and only where appropriate.
The parameters that matter most for actionable results—like channel coefficients—are geo-specific.

So while geos technically sit within a shared structure, in practice you can treat the results as independent MMMs that learn faster and behave more stably.

4. You can transfer experiment results… with caveats

Experiment results from one geo can be used as priors in another—especially helpful for smaller markets.

But it works only if marketing behavior and consumers are similar.
Running a test in a low-spend, low-complexity geo and applying it to a high-spend, highly competitive geo won’t produce a meaningful result.

5. Low-volume geos will always have more uncertainty

Even with hierarchical modeling, a geo with very small conversion volume will show:

• wider ranges

• more volatility

• larger sensitivity to small changes

This is expected and normal.
It’s the statistical reality of small markets—one conversion can move the needle dramatically.

When this happens, you have two choices:

• Combine similar geos (if the business is willing)

• Use priors from larger geos to stabilize results

What Hierarchical Models Don’t Do

To set good expectations:

• They don’t force geos to behave the same

• They don’t pool spend or incrementality

• They don’t allow you to copy/paste a result from one geo to another

• They don’t reduce uncertainty for a tiny geo to the level of a large one

They simply give each geo its best shot at a stable, realistic incrementality read—even when data is limited.

FAQs about hierarchical modeling

1. Do the results from one geo change the results in another?

The actionable coefficients—like channel incrementality—are geo-specific, so each geo stands on its own.
Technically, some shared parameters exist, but they contribute minimally.

Result: The influence is extremely small.

2. Can we apply learnings from one geo to another?

Yes—especially experiment results.
But only if the geos are reasonably comparable in:

• marketing maturity

• consumer behavior

• channel mix

• competitive intensity

Otherwise, we risk introducing bias, not insight.

3. What if a geo doesn’t have enough volume for its own model?

This is where hierarchical modeling shines.
We can stabilize the geo by:

• sharing strength from larger geos

• using experiment priors

• or, if appropriate, combining geos

But low-volume markets will always have larger uncertainty ranges.

4. Does a hierarchical model mean we have a “global MMM”?

Not in the way many marketers imagine.
It’s still multiple MMMs, one per geo—just linked through a shared structure that improves learning.

5. Why not just combine all geos into one big MMM?

Because you’d end up with:

• misleading conclusions

• incorrect channel efficiencies

• loss of geo-level actionability

• higher risk of false correlations

Hierarchical modeling gives us the best of both worlds: shared intelligence + local accuracy.

In Closing

Hierarchical MMMs are one of the most powerful tools for modern marketers operating across multiple regions. They help you:

• trust your geo-level results,

• reduce noise in low-volume markets,

• leverage learnings from experiments more efficiently, and

• build a global measurement strategy that’s both scalable and precise.

If you operate in multiple markets, hierarchical modeling is often the difference between “interesting analytics” and actionable, region-specific investment decisions.

FAQ: Should every geo get its own standalone model?

Not always. A standalone geo is most useful when that market has enough volume, strategic importance, and meaningfully distinct behavior.

If a market is very small, has limited conversion signal, or is not a current growth priority, forcing it into its own standalone read can add noise without adding much decision value.

In those cases, the better options are often to:

• keep major priority geos separate when they drive planning and budget decisions,

• combine smaller or operationally similar geos to improve stability, or

• use a hierarchical structure so smaller markets can borrow signal from larger ones without being treated as identical.

The goal is not to maximize the number of geo cut lines. The goal is to create a structure that reflects how the business actually makes decisions while preserving enough signal for the model to be reliable.

A practical rule of thumb: if a geo is small enough that one or two conversions materially change the story, it may be better grouped with a similar region unless there is a strong business reason to keep it separate.

When should a geo stand alone versus be grouped with others?

One of the most practical questions in geo-based MMM is not just "should we use a hierarchical model?" but "which geos should stand alone, and which should be grouped?"

There is no universal rule that every state, region, or country deserves its own standalone model. The right structure depends on whether a geo is large enough, strategically important enough, and behaviorally distinct enough to justify being modeled separately.

A geo is more likely to deserve its own standalone treatment when:

• It represents a meaningful share of spend or conversions and has enough volume to support stable estimation

• It is a clear business priority for growth, efficiency, or strategic planning

• Its market dynamics are genuinely different from adjacent geos because of media costs, competition, regulation, consumer behavior, or channel mix

• The business expects to make decisions differently there than it would in a grouped regional view

A geo is more likely to be grouped when:

• It is too small to generate a reliable standalone signal

• It is not a major planning priority, even if the business operates there

• It behaves similarly to nearby markets and can borrow strength from them without losing much decision value

• A standalone read would create noise rather than actionability

A helpful rule of thumb is: separate a geo when doing so improves decision quality; group it when grouping improves stability without changing the decisions you would make.

For example, a very small state that a business only serves for regulatory or operational reasons may not deserve a standalone model if it is not a real growth market. In contrast, a state with similar size but clear strategic importance could absolutely merit separate treatment because the business needs a distinct read on incrementality there.

That means geo structure should reflect both statistical reality and business intent. Volume matters, but so does whether the company actually plans to invest, defend, or learn in that market.

A practical stakeholder framing

"Not every geo needs to stand on its own. The goal is not maximum granularity — it is the most decision-useful level of granularity. If a market is small and not strategically important, grouping it can produce a more stable and more useful model. If it is a priority market, standalone treatment is often worth it."

A practical question: which countries should stand alone, and which should roll into a rest-of-world bucket?

When a business expands internationally, one of the most useful applications of a hierarchical model is deciding which countries deserve their own read and which should be grouped into a broader rest-of-world bucket. In practice, this is rarely about drawing the maximum number of country lines. It is about choosing the most decision-useful level of geographic detail.

A country is more likely to stand on its own when it has enough outcome volume to support a stable read and when it carries enough spend or strategic importance to justify separate decisions. A country is more likely to be grouped when volume is too thin, spend is too light, or a standalone cut would add noise without changing the decisions the business would make.

In many businesses, the right answer is a hybrid structure: major markets stand alone, while smaller markets are combined into a regional or rest-of-world bucket. That gives the model enough signal to stay stable without forcing tiny markets to pretend they have more data than they really do.

A practical rule of thumb is to judge “small” mainly on outcome volume and spend, then pressure-test whether the business would actually act differently on a standalone country read. If the answer is no, grouping is often the more rigorous choice. If the answer is yes and the market is strategically important, a separate country cut may be worth the extra complexity.

Want to learn more about marketing mix modeling? Check out other Paramark marketing mix modeling articles here