Using International Automotive Data To Plan Global Dealer Network Expansion
13th July 2026
Key Insights
- Dealer network planning is driven by demand volume and demand location, which together determine how many sites are needed and where they should be placed.
- International expansion becomes more complex because data varies by market, with no global equivalent of the Driver and Vehicle Licensing Agency (DVLA) to provide consistent coverage.
- Even where data is incomplete, demand can still be understood by combining available sources with demographic modelling and market experience.
- The analysis supports more than site selection, informing market entry strategy, network design, and how vehicles are distributed across the network.
Every international dealer network project starts with two questions...
How much demand is there for your vehicles in this market, and where is that demand located?
They’re two sides of the same problem, and the starting point for every dealer network decision.
Demand volume sets the scale - how many dealers a market can realistically support. Demand location sets the structure - where those dealers need to be to reflect how the market actually behaves.
Pull on either thread without the other, and you'll end up with a network that's either the wrong size, in the wrong places, or both - with direct impact on performance and investment returns.
We see that play out across both established and new markets, but applying it consistently isn’t always straightforward.
So that’s what we’ll be looking at today, exploring how international automotive data feeds into global dealer network planning, where it typically falls short, how those gaps are handled, and what the work actually enables - including a strategic layer that’s often overlooked, and how
GMAP’s automotive consultancy can help turn that into practical network decisions.
Why does the same approach become harder across international markets?
Here's the thing: the logic behind choosing a location for a business doesn't change when you move from a domestic project to an international one - you're still trying to understand demand and translate it into a network.
The objective is always the same, but the clarity of the data, and the work needed to turn it into a reliable view of demand, isn’t - and that changes the way the analysis needs to be done.
The gap between what you need and what's actually available can be significant. Different countries collect vehicle registration data differently - different formats, different levels of detail, different release schedules, and sometimes different definitions of what's being counted in the first place. There's no global standard for the collection of vehicle registration data and not every country has the equivalent of the UK’s DVLA to keep track of vehicle ownership.
That lack of consistency is the real issue. Not the approach itself, but the quality and comparability of the data feeding it.
Where international automotive data falls short
This typically shows up in two ways, and it’s useful to separate them because they create different types of constraints.
1. Inconsistent vehicle detail
In some markets, registration data is quite rich - make, model, fuel type, even specification. In others, it’s far more limited. You might only know how many vehicles are registered in a region, without much insight into what those vehicles actually are.
That's a problem when your whole analysis depends on understanding specific demand segments. If you can't isolate your target vehicles from the total parc, you're working from a much noisier signal from the start.
2. Geographic granularity is often too broad
This is the more common issue, and arguably the more limiting one for network planning.
Data is published at very different spatial levels depending on the market. Some countries provide local-area breakdowns, others stop at city level, and some only publish at state or province level - large administrative areas that might cover millions of people and hundreds of kilometres.
You know demand exists within that area, but pinpointing where it sits is close to impossible. And that’s exactly what you need to know when deciding where a dealer should be located
How GMAP builds a usable view of demand from imperfect data
There are two approaches here, and in practice we usually use them in combination.
Interpreting data based on experience
Sometimes the data you have, even if it’s not as granular as you’d like, is still enough - if you know how to read it. Early-stage market entry decisions don’t always require highly detailed local breakdowns, and knowing that demand is concentrated in certain provinces or major cities can be enough to form a solid first view of where to focus.
This is where GMAP’s 30+ years of experience in location intelligence applications comes in. A large part of this work is judgement - knowing when the data is sufficient for the decision at hand, and how to interpret it across different markets without overcomplicating the picture.
Modelling demand using demographics
When the data isn’t detailed enough, we bridge the gap by modelling it, using our custom Spatial Interaction Models alongside tools like IMPACT and MVPLUS to build a more accurate view of demand.
The principle is simple: population and purchasing power act as strong indicators of where vehicle demand is likely to sit.
In practice, that might mean using population distribution to break a province-level registration figure down into smaller areas, or adjusting that distribution by affluence or purchasing power data to reflect the fact that registrations aren't evenly spread - they follow wealth.
If you know a province holds a certain level of registrations in your segment, but you also know purchasing power within that province clusters around two or three cities, that’s where your distribution planning, and ultimately your dealer location decisions, should be directed.
The modelling makes that explicit rather than leaving it to assumption.
What this analysis enables for network planning
The output of this work supports three outcomes:
- How many dealers a market needs
- Where they should be located
- How stock and inventory should be distributed across the network.
That last point is often the most overlooked.
International automotive data analysis directly informs distribution decisions - which means the work feeds into operational planning as much as it feeds into strategy.
Domestic vs importer markets
One aspect of international expansion that often gets overlooked is whether you’re entering a market as a mainstream competitor or as an importer - and what that actually means for network strategy.
In many markets, particularly those with a strong domestic manufacturing base, the structure effectively splits into two segments: domestic manufacturers with a dominant share and strong loyalty, and importers competing for a smaller, shared pool of demand.
That creates an important strategic question for brands entering these markets: who are you actually benchmarking against? Some will go after the full market, others will make a deliberate choice to focus exclusively on the importer segment, measuring their performance against other foreign brands rather than against the domestic manufacturers. The Korean market is a good example of this, with some established global brands operating in what is functionally a separate competitive tier from Kia, Hyundai, and the other major domestic players.
We see this as a strategic choice, not a limitation. If your competitive set is other importers, your demand model looks different, your catchment assumptions are different, and your network sizing reflects a different market reality.
Clarifying this distinction early can significantly change the shape of the final recommendation.
The datasets used in international dealer network planning
These projects typically draw on a mix of data sources.
- Vehicle registration data - at whatever level of granularity the market provides
- Population and demographic data - to understand the underlying demand distribution
- Socioeconomic indicators - including affluence, purchasing power, and income patterns
- Market structure data - competitive landscape and domestic vs importer dynamics
- Client data - existing network footprint, sales performance, and strategic priorities
The balance between these changes depends on the data available. In markets with strong official data, registration figures do most of the heavy lifting. In markets where that's thin, demographic and commercial datasets fill more of the gap.
The skill is in combining these sources into something consistent and usable - a view of demand that’s strong enough to support real decisions, not just describe the market.
This is where location intelligence applications play a crucial role, turning fragmented datasets into a consistent view of demand.
How international projects typically begin
International projects follow the same structure as any GMAP project - that means starting with market scoping, reviewing data availability, modelling demand, and then developing network recommendations.
GMAP runs this process just as frequently in international markets as we do in the UK, so the methodology is well-established - even if the data conditions vary more widely.
Making confident location decisions without perfect data
International network planning rarely comes with complete data, but that’s not a reason to delay or to rely on instinct - it’s a reason to structure what you have and make the gaps visible.
At GMAP, the focus is on removing assumptions that don’t hold across markets. Structured modelling doesn’t eliminate uncertainty, but it keeps decisions grounded in evidence.
We don’t look for perfect data. We build consistency - turning imperfect inputs into comparable insight that supports real network decisions. That’s what allows global expansion decisions to be made with confidence, even when the data isn’t perfect.
If you’re planning or reviewing an international network,
get in touch with GMAP to understand what a more evidence-led approach could change.
