Key Insights

• DVLA data is the gold standard when it comes to data for automotive location planning.
• However, because of the way vehicle leasing causes distortion in the data, it can be difficult to get a fully accurate vehicle ownership picture.
• Data from partners in the payments sector could fill this gap by showing additional information about which leasing businesses are being paid and crucially, from where.
• This data could also take us beyond simply generating a more accurate picture of vehicle ownership, and provide a more comprehensive view of automotive market activity across different stages of the vehicle lifecycle.



The blind spot in automotive location planning

Opening a new business location serving the automotive sector isn’t cheap. Whether it’s a charge point for electric cars, a service station, a parts warehouse, or even a new car showroom, serious investment is required to get that site up and running. 

This means that careful investigation and planning is required to ensure that that investment will pay off, and that the site under consideration is viable not just in the immediate future, but also longer term. That investigation and planning, in turn, is reliant on accurate data, about what vehicles are driven by who, and where. 

The Driver and Vehicle Licensing Agency (DVLA), meets this need via its vehicle register (often referred to as “parc” data in the automotive industry). Whilst there is a publicly available version of this data set, the DVLA works with a group of partner businesses, like GMAP, to offer even more insight into trends within the UK vehicle market. 

This expanded DVLA dataset includes thirty fields in each record, and in addition to new car registrations, also covers areas of the automotive market such as used car purchases and disposals. The data can be broken down to a highly granular, partial postcode, level. 

This level of detail and granularity is the level required by businesses in the automotive sector when making location decisions, and has proved to be an invaluable resource.

When the data doesn’t quite add up

Whilst this data continues to be industry standard for automotive location planning, the future holds the possibility of even greater levels of insight for businesses in the sector, once additional data sources can be cross referenced with this dataset. 

A good example of this is the distortion in the DVLA’s data caused by leasing. Because vehicles are registered to the entity that purchases them, leased vehicles, as well as those driven under salary-sacrifice, personal contract hire schemes, and fleet vehicles, are registered to the office of the leasing business. This doesn’t give a clear picture of where they’re actually being driven or operated, creating a distortion in the data. This means that in the DVLA’s data, there are several post codes that have a disproportionate number of vehicles registered to them, even though these vehicles are, in reality, being driven and operated elsewhere.

This is particularly frustrating for automotive businesses trying to understand localised market potential, since it is often new and premium vehicles that are more likely to be owned through a leasing agreement, and the drivers of such vehicles are often a key target for businesses in the sector. This is especially true of electric vehicles, where a greater proportion of sales are made through leasing and contract hire than for any other type of car - meaning the vehicles businesses are most interested in are also the ones least likely to be registered where they're actually being driven.

Aside from enterprises selling cars, businesses that service vehicles and supply parts can also struggle with this gap in the data. Leased vehicles often have to be serviced at approved franchise dealerships or garages, so whilst an area might look like a great bet for an independent garage, with lots of vehicles, a significant chunk of these can be off the table for them. For car parts suppliers, because leased vehicles are typically younger, this suppresses demand. In addition, because leased vehicles often get sent back to a central location rather than coming onto the local used car market, it can be difficult to forecast future demand for parts in that area.

Could payment data fill the gap?

The solution to this problem is to add new data sources into the mix that can smooth out some of the lumps, but also add exciting new contexts to the data that is already in play. We recently wrote an article for our blog on what a typical automotive consulting project looks like at GMAP, and one of the elements of these projects that we discussed is how much information feeds into these projects, beyond the data from the DVLA. This includes information such as sales targets, dealership performance numbers, and franchise rules. This information from our clients provides important context when we then go on to consider vehicle registration information.

In the same way that this data directly from clients adds lots of useful context, there are other third party data sources that could, sitting alongside the DVLA data, provide rich additional insights. 

We were recently discussing the vehicle data picture with Aevi, who provide payment orchestration services to their clients, many of whom operate across sectors including retail, fuel, and mobility. 

Aevi’s payment orchestration platform connects payment devices, payment providers and value-added services through a single orchestration layer, helping businesses make better use of data and insights that would otherwise be hidden across the different systems they use to take payments.

Where appropriate and with the right permissions in place, that newly connected payment insight could add another layer of context for automotive planning - helping businesses build a clearer picture of demand and how markets are changing.

Bridging the gap between registration and reality

The data generated and gathered by Aevi’s platform could be incredibly powerful because of the potential it holds to smooth the bump in the DVLA data.

Payment data will likely never allow for an individual person to be identified and associated with a particular vehicle, but it may allow businesses with access to the data to see where recurring monthly payments to leasing businesses are being made.

Most leasing providers are known entities, and that's where payment data could, in theory, help. The data in question would be anonymised and aggregated, so it would never point at any one person or vehicle, but patterns of recurring payments to leasing firms could still provide a useful signal of where leased vehicles are likely to be based. 

With the right agreements and permissions in place, that signal could be combined with other datasets to estimate leasing density at postcode district or sector level. That, in turn, helps correct for the under-counting and over-counting that happens within the DVLA's data when leased vehicles are disproportionately registered in a small number of postcodes. 

Cross referencing between the data sets could then produce a fuller estimated picture of the vehicle market, with vehicle numbers distributed more accurately across the postcodes where they’re most likely to be based.

In this way, both datasets would work together to compensate for each other’s weaknesses. Of course the phrase “work together” is doing a lot of work here, since to get the two sets to work together and produce an accurate picture, requires an expert location planning agency like GMAP with the needed data knowledge, experience, and technical skills. Payments data is, quite rightly, heavily regulated, and the data available through partnerships will typically be anonymised and often aggregated. This means that for the location planning experts, they need to understand how to take that sampled data and use it to build a full picture.

Understanding the full vehicle lifecycle

The utility of payment data isn’t limited to just compensating for a distortion in the DVLA data set. Beyond this, it offers new ways to understand the whole automotive market, including spend on fuel or charging, insurance, servicing and repairs, parts, parking, and accessories. Once businesses have an idea what this total spending picture looks like, they can better identify cross-sell and bundling opportunities, that will really resonate with their customers, enabling them to generate more value, and extend relationships through increased customer loyalty. 

Focussing specifically on fuel and charging facility use, this data is useful for both vehicle dealerships, and businesses who retail fuel. The former can get a more accurate picture of where their vehicles are being driven and fuelled, helping them reach the right customers at the right time. The latter can use it to understand market demand for fuelling stations and charge points, helping them identify places that are underserved, and avoiding those areas where there is already sufficient coverage or saturation. 

This is particularly valuable when it comes to charging, where the question isn't only how much demand exists in an area, but what kind. Payment data can help distinguish between drivers topping up briefly, those charging at a destination whilst they shop or eat, and those relying on public charging as a replacement for charging at home. Each of these implies a very different type of site, with different dwell times, pricing, and facilities - so understanding which of them is driving demand in an area is as important as knowing that the demand is there at all.

Spends on servicing and parts will also help get a more accurate picture of the aftersales market, showing where parts are being bought, where services and repairs are being booked, and whether or not that is staying within franchise dealer networks, or going to independents. 

Payment data could also hint at broader changes in spending behaviour between different providers or channels, such as customers gradually moving away from franchise dealer servicing towards independents. Viewed in aggregate, those trends could give franchise networks a useful read on customer loyalty and churn across their market, without ever tracking any individual customer's data.

Where could payment data take us?

Payment data won’t magically give a complete picture of the entire vehicle market. However, payments data, combined with the existing DVLA data by agencies with deep expertise and technical skill can help to correct the anomaly caused by leasing in the DVLA data. But it could also go beyond simply enhancing the view of where cars are being leased, and give a wider picture of the UK automotive market so that the whole vehicle lifecycle becomes quantifiable. 

Let’s be clear, agreements around payment data are complicated, so this isn’t something that GMAP is going to be offering in the immediate future. But developments in the world of payment technology are moving at a pace, and increasingly becoming a powerful source of data, and therefore insight. The promise this holds is particularly exciting for those of us working in location planning, where large and constantly updated payment datasets can help us refine our understanding of what people are doing and where, and therefore help our clients plan and operate with more certainty.