‘I want it now’ culture will pose considerable challenges in the future
By Colin Tourick, University of Buckingham Business School
When I downloaded an app that allowed me to check nitrogen oxide levels close to my home, I was disappointed that the readings were two weeks old. Then I considered this disappointment in a little more depth.
Much of the research data I look at is months old, yet here I was feeling disappointment with readings that had lapsed by just a couple of weeks.
How expectations have changed.
We expect everything instantaneously and are disappointed if we can’t get it. Keep this in mind as we start considering the future of the fleet leasing industry.
In the long term, say 10-15 years, fully autonomous vehicles will be ubiquitous. People who prefer to drive may be viewed as “hobbyists”, much as we now regard those who take photographs on film then develop and print the results.
Many of us live in cities and won’t want to own cars because we will be able to summon up an autonomous vehicle at will, just as we book Uber today.
Whether the cars will actually be operated by Uber or a similar company is open to debate. The speed of Uber’s roll-out (and their losses) has been spectacular. It is working on autonomous cars, but once a fully autonomous car rolls off the production line it will be able to find passengers for itself. In this scenario, what value does an Uber-style company add?
Someone will need to buy the cars, charge for journeys, manage maintenance and so on, and fleet leasing companies would be well-placed to do so, though this role could also be performed by wholesale funders or indeed the manufacturers – they have most of the infrastructure already and just need cash on day one to replenish their working capital. That said, once vehicles are able to report their own faults, book themselves in for repair and drive themselves to auction for sale, there may not be much ‘management’ left to do.
Buyers in rural areas
Totally autonomous vehicles may not need to go to auction. They could drive themselves to potential buyers, who will probably be in rural areas where operators cannot viably offer ride-on-demand services.
The fleet leasing industry is in good health at present. Huge demand from its traditional customers – medium and large fleets – and its newer customers – consumers and smaller businesses – have driven recent growth.
In short-to-medium terms the growth opportunities will be immense, as more consumers and small businesses opt for the elegant simplicity of pay-by-the-month-and-hand-it-back leasing, rather than having to stump up cash to buy a car then deal with the used car market to sell it.
The industry has benefitted from the move from ownership to usership and, as the sharing economy grows and autonomous vehicles arrive, many businesses will decide they don’t need exclusive use of every vehicle, just guaranteed rapid access to transport that will get their employees from A to B.
This takes us into the world of ‘mobility solutions’. While some fleet leasing companies are “monitoring developments in this area” (a possible euphemism for “we aren’t sure anything is going to happen here so will just carry on doing what we do”) others are building and introducing solutions.
Many business vehicles cover 20,000 plus miles a year on mission-critical journeys. Here a dedicated company vehicle is essential and the cost per business mile is low. Some company cars travel relatively few miles each year and here the real cost per business mile is rather high.
There is scope here for leasing companies to sell personal lease schemes via employers, something that was trialled without much success 20 years ago but which may have more success now. Once vendors start knocking on the doors of fleet managers offering mobility services that slash the cost per business mile, those fleet managers will sit up and take note.
Every element of mobility services already exists – company cars, rental cars, car clubs, buses, trains, aircraft, corporate car sharing, corporate taxi services, even bike hire – but they are not yet joined up.
Employees need to be offered the optimal mix of transport modes for each journey, with expenses being managed automatically and with the flexibility to make changes mid-journey, if, for example, a train is cancelled.
While they are perfectly placed to build these services, most fleet leasing companies have yet to do, or even to meet academics or government agencies that work in this area or the young fintech companies that are trying to develop and roll-out solutions.
One definition of mobility management might be the intelligent merging of the functions currently performed by fleet industry companies, transport operators and travel management companies, to produce solutions where each journey choice is optimised for cost, timeliness and environmental impact. So a good starting point might be to explore collaboration with travel management companies.
Now would be an ideal time to introduce cost-optimised solutions based on traditional products. An employee goes onto a leasing company’s portal, enters their tax rate and annual business mileage, and the system automatically offers the optimum solution. This could be a company car (contract hire), personal car (ECO or PCH), salary increase (for agreeing not to take a company-funded vehicle), mileage allowance or mobility card – all optimised to minimise emissions and after-tax cost for both the employer and the employee.
Next let’s consider data emerging from connected vehicles. Soon all new vehicles will transmit vehicle data via factory-fitted modems.
A punch-up is brewing between manufacturers and their customers – most notably the leasing companies – about who owns the data generated by connected vehicles. The BVRLA is very active in this area on behalf of its members (see page 7). In due course no doubt the industry will have access to the vast majority of data that emanates from vehicles, allowing it to develop much better services.
These will include proactive maintenance management assisted by predictive insights gleaned by trawling through real time car data and the leasing companies’ own databases.
Few data scientists are employed in fleet leasing companies at present. No doubt this will change quite soon as the ‘Big Data’ revolution unfolds. Clients and drivers will expect to see real-time actionable information: last month’s raw data just won’t cut it.
Clients will also demand risk mitigation. A company car veers off the road and kills a pedestrian. Some months later, as part of a investigation, the employer is required to deliver the data the car reported before the accident. A data specialist trawls through this and identifies a fault code that was transmitted but not acted upon, which, in turn, led to the accident. Employers will want protection from this sort of nightmare and will expect their leasing company to deliver it.
Another area where leasing companies will need to invest is operational efficiency for themselves and their clients. Leasing companies will need to embed themselves into their clients’ systems, adding value and making themselves indispensable partners.
Doing the right thing at the right time, helping the client optimise their business travel, using connected car data in new ways to deliver new services, making sure that things are done just-in-time rather than just too late, striving for ever greater efficiency (ever lower levels of headcount per thousand vehicles managed) – these will mark the difference between the winners and the losers over the next few years.
And as we go into the era of fully-autonomous vehicles, the industry will carry on doing what it has always done; adapting to client needs. Many businesses will still need properly funded and managed cars and vans, fully dedicated to the company, while autonomy creates a lot of opportunity for the industry. But to remain relevant it will also have to deliver mobility services to meet the needs of employees who don’t need a dedicated vehicle.
And all of this will need to happen in real time, to avoid the disappointment that comes from looking at two-week-old data.