Assessing your fleet: how real-time data is key to precise residual valuation
Leasing companies are currently struggling with falling residual values of higher-priced electric vehicles. This makes it all the more important to stand out from the crowd with a strong value proposition in the current market. Vehicle data can provide a solid basis for this, as Christoph Ludewig, vice president Europe at Geotab, explains.
Falling residual values of electric cars have been a recurring topic in the media for more than a year now. This affects providers and customers alike, as these reducing values are usually compensated for by higher rates.
If leasing companies want to achieve comparatively good sales despite the challenging market conditions and low demand, a strong pitch is needed. In addition to its appearance, the age and mileage of a vehicle are looked at first in order to assess its value. However, these parameters alone do not provide a particularly meaningful value.
Problems of traditional valuation
In order to determine the value of a used car as accurately as possible, other parameters must be examined in addition to age and mileage. These include, for example, the condition of the engine, transmission, or electrical components. In the case of electric cars, the condition of the battery is also crucial. In practice, however, this evaluation would be costly and time-consuming, so it is better to rely on estimates that are based on assumptions, such as that lower mileage is associated with less wear and tear.
The problem is that speedometer value alone does not indicate whether a car was mainly driven on the highway or in city traffic.
Driving behaviour is also not taken into account. The condition of the vehicle is significantly impacted by whether a driver drives cautiously or aggressively. With electric cars, additional external factors also play a role, such as whether the battery has been exposed to extreme temperatures and how it has been charged. Although it is comparatively easy to measure the condition of a battery, a visit to the workshop is still necessary.
Using telematics to replicate digital twinning
Inspections, measurements and workshop visits would not be needed if a digital image of the vehicle existed, into which various measurements were fed in real time – in other words, what is known in the industry as a digital twin. However, a complete digital twin for every vehicle in a fleet would also require an immense effort.
Modern data platforms offer a perfect alternative to this, as they can now record and analyse much more data than just a vehicle’s location and driving times. Integrations with OEM systems make it possible to send metrics like radiator temperature or idling times, or in the case of EVs, data on battery temperature and charging cycles.
Building on this database, companies can also set up bonus programs that reward careful use of vehicles. Those who drive with foresight, take care of the engine and carry out regular maintenance can look forward to attractive bonuses – for example in the form of cashback models or improved conditions for follow-up contracts. This creates a classic win-win situation for lessors and car rental companies: drivers have a clear incentive to treat the vehicle with care and ultimately benefit from this themselves. At the same time, companies benefit from a higher resale value and lower maintenance costs. In the long term, this not only optimises cost structures, but also strengthens brand loyalty.
In addition to its basic functions, the evaluation of near real-time driving data provides valuable input for determining residual value by bolstering assumptions and estimates with objective, tangible data. The integration of corresponding platforms should not only be considered from a technical perspective, but also from a business perspective. It helps to reduce risks, increase values and improve customer satisfaction. Against the backdrop of a tight used car market and the shift towards electrification, this data-based approach can prove to be a competitive advantage.