Leasing companies and insurers operate in an overwhelmingly cost-driven and competitive market where every single penny must be accounted for. Vehicles are also becoming more and more complex owing to the sheer amount of onboard technology they have – particularly electrified vehicles – which is leading to skyrocketing repair costs.
At the same time, they are having to contend with the need to ensure total customer satisfaction. There’s very little wiggle room for vehicle repair delays and prolonged amounts of time off of the road; customers will go elsewhere if they’re dissatisfied with their experience.
Over time, these factors – the desire to save on costs, minimise repair times, and satisfy customers – create something of a status quo for leasing companies and insurers. They’ll typically distribute their repairs across a network of trusted body shops based on the distance between them and their customers. In some cases, a single body shop may be used, or a company may provide customers with a long list of local body shops and expect them to select one.
The cost of staying the same
The problem with this approach is simple: it lacks data. When repairs are allocated to body shops based on a company’s status quo alongside the excuse of, “Well, we’ve always done it this way”, rather than to body shops based on reputation and expertise, it’s inevitable that money is being wasted. Indeed, if this sounds like how you do things, the message is simple: You’re spending more money on repairs than you need to. This is because there are many factors at play that must be accounted for when allocating repairs, all of which can be better informed by using your (historical) repair data.
The fastest body shop, or indeed the one that’s closest to the customer, might not be the one that delivers the most value or is the most efficient for a particular repair, and over time this can add up to significant amounts of money blindly, and unknowingly wasted on repairs.
Imagine for a moment that you’re unknowingly spending EUR 50,00 more per repair than is necessary because of the way your repairs are allocated. That might not sound like much, but over the course of 40.000 repairs in a year - a relatively conservative amount for a mid-to-large-sized operator—that amounts to EUR 2.0002000,00 in a year.
Dynamic distribution of repairs
To help leasing companies with mid-sized and large fleets and insurers overcome this challenge and save money on sunk costs, we have developed a solution: dynamic repair distribution.
Dynamic repair distribution can be set up with our Repair Manager solution, a data-driven repair allocation engine that can help leasing companies and insurers eliminate friction from and optimize their digital repair process through data-led repair allocation. This is powered by 1. the business rules engine and 2. our proprietary machine learning model.
1. The business rules engine
The Openclaims platform enables users to create their own business rules and apply them to repair variables such as brand, model, transmission, and specific requirements such as quality standards, maximum cost, and maximum distance between a body shop and the customer.
2. The machine learning model
Our proprietary machine learning model has been built using statistical learning techniques that leverage tens of thousands of data points to assess the impact of damages and calculate an estimated cost of repair based on given variables.
Using this information, the model can compare the performance of individual body shops to the estimated cost of repair and generate ideal repair distribution scenarios - for example, by identifying the most suitable body shop for a particular repair that also falls within your business rules.
Advantages of dynamic distribution
Dynamic repair distribution can be used to unlock a whole range of benefits beyond savings on sunken repair costs. These include: