The automotive data gap and the untapped value of payment data

• Vehicle registration data can distort the true picture of demand because leased and salary-sacrifice cars are often registered to the company that owns them, rather than the location where they are driven.

• Aggregated and anonymized payment insights can add another layer of context, helping automotive businesses compare what the data suggests with what is actually happening on the road.

• Valuable payment intelligence is often difficult to access because payment data is spread across disconnected terminals, gateways, providers, and locations.

• Payment orchestration data can create a clearer view across that fragmented infrastructure, making it easier for businesses to analyze payment activity alongside other data while retaining control over their own payment stack.

Don't have time to read more now? Sign up to our newsletter to get the latest insights directly in your inbox. 

The brief sounds simple enough: find the best location for a new automotive dealership for an ambitious and growing automotive manufacturer. The obvious place to start is the DVLA's vehicle registration data (the gold standard for this kind of decision), and it tells them exactly how many cars are registered in a given postcode. 

Except a chunk of those cars aren't actually there. 

They were bought by a leasing company two hundred miles away, and that's where they're registered - even though the person driving one to the office every morning has never been anywhere near that area.

We were recently discussing this exact problem with location intelligence specialists GMAP Analytics, who help organizations globally to make better decisions about where to build, stock, and staff up their businesses, in order to engage the optimum number of customers.

GMAP can overcome many of the limitations that they find within the DVLA data, because they have privileged access to an expanded version of the data set, a highly skilled team of data experts, decades of experience, and proprietary technology. But they aren’t the only people to encounter these types of problems. 

Multiply the scenario they describe across every business trying to plan using large and highly detailed data sets, and you start to see the full extent of the problem. Businesses often have no shortage of data. What they confront is the bigger, and perhaps subtler, challenge of ensuring that anomalies or distortions in the data don’t lead to poor conclusions

Payment data is a good example of this. Businesses have no shortage of payment data, but the information captured in each transaction doesn't always provide the context needed to understand what’s really happening.

Turning payments data into useful business intelligence means, combining this data with the other information a business already holds, filling in the gaps, and adding the context that raw transaction records can't provide on their own. Effective payment orchestration is the key here, helping to connect the dots.

Think about how much a business already learns every time a customer pays. A single transaction might only show a sale, but thousands of transactions together can reveal much more:

• Which locations are busiest, and when demand peaks
• Which products are typically bought together
• Where customers return, and where they drop off
• Where revenue opportunities are leaking away at the point of sale

That intelligence is already sitting within the payment flow, but in many businesses it goes to waste because the insight is split across systems that don't talk to each other. Each system does its job, but businesses are only able to see fragments of the wider picture - when the real value comes from seeing across all of it.

This is true across every sector, but automotive just happens to show it very clearly.

A fuel and mobility retailer can use its own payment data to understand how its network is performing day to day. 

Looking across sites, it can see which forecourts and charge points are busiest, when customers are using them, how customer behavior is changing as EV charging scales, and where different locations are heading over time. 

That’s a clear, customer activity-based view of its own network, and it answers questions that used to rely on surveys and guesswork. The difficulty comes when a business tries to answer those same questions across the wider automotive sector, where different datasets each show a different side of vehicle demand.

That brings us back to the specific issue that started us thinking about this in the first place: the automotive data gap. 

When someone leases a car, or takes it on through salary sacrifice or a personal contract hire scheme, the vehicle gets registered to the business that owns it - not the person driving it. 

Leasing companies tend to be concentrated in a small number of locations, meaning those postcodes end up with a disproportionately high number of vehicles on paper, while everywhere else looks quieter than it really is.

For dealerships, charge-point operators, and automotive suppliers, that difference can have a real impact on network planning decisions. Leased and salary-sacrifice vehicles are often newer, higher-value models - exactly the kind that signal where demand is likely to grow - so getting the location wrong can be costly. 

A badly sited charge point or dealership is a serious investment that takes years to pay off - if it pays off at all.

While GMAP already does a lot of work to account for the way leasing skews the picture, when planning for their clients, they’re exploring how they could generate more and even better insights in the future. One of the ideas they were talking to us about is how aggregated and anonymized payment insights could add further context - helping businesses understand whether the picture on paper matches the reality on the road.

Payment data has the potential to provide valuable insight, but putting it to work is more complicated than simply connecting one dataset to another.

Payment data isn't sitting around ready to be cross-referenced with something like the DVLA's vehicle register. In most businesses, it's fragmented across whatever mix of terminals, gateways, and payment providers they happen to use - all built up over the years to process transactions, not to analyze them. We've written before about how much that fragmentation holds businesses back.

Even when the data is available, using it effectively requires the right approach. Payment data is highly sensitive, and any analysis needs to respect the regulations and agreements that govern how it can be used. That means using aggregated and anonymized datasets, rather than exposing individual transaction records.

And this is all before the technical challenge of making genuinely fragmented data usable in the first place. Without the right infrastructure, valuable payment insight can remain hidden across disconnected systems and providers.

This is where orchestration comes in: helping businesses connect fragmented payment data and unlock the intelligence already sitting within their transactions.

Our automotive example makes the problem easy to see, but it’s not unique to one industry. Across businesses, transaction data has the potential to reveal useful intelligence - but that value is harder to access when payment systems are fragmented.

Aevi’s platform helps businesses connect payment devices, providers, and services through a single orchestration layer. It isn't another data source; it's the layer that brings scattered payment activity together, so insights can be analyzed and acted on alongside a business's other data.

Because the platform is vendor-agnostic, it works with the payment technology businesses already rely on. For a business with multiple payment providers, acquirers, or hardware across different sites or regions, that means they can build a clearer view across their own payment stack rather than being limited to a single source. 

Once those connections are in place, businesses can decide how best to use the information available to them - whether that means understanding their own operations better or combining payment insights with other sources of data.

Across sectors, the value of orchestration comes from helping businesses make better use of the data they already have, while keeping ownership and control where it belongs: within the business itself.

The charge point problem we opened with is a good reminder that a single dataset rarely tells the whole story, and that the value in data comes from context.

Specialists like GMAP already do the hard work of turning data like the DVLA's register into a real view of demand, but the wider opportunity is bigger than any one industry.

Every business is already generating a stream of transaction data with valuable business intelligence sitting inside it - from where demand is growing to where revenue opportunities are being missed. 

That intelligence can be difficult to access when data is spread across systems and providers that have built up over time, and locked behind the regulatory considerations that payments rightly require. 

That is where Aevi’s payment orchestration platform can help: connecting the payment technology businesses already rely on, so fragmented transaction data can be brought together in a way that makes it easier to generate business intelligence. 

Curious what your own payment data could tell you? Talk to our team about turning it into insight you can act on.