The Ultimate Guide to Vehicle Data

Smarter, safer, better vehicles should be less risky to operate and, when built with repair and resilience in mind, may also be cheaper and easier to fix when accidents do happen. The technologies car makers introduce to make vehicles “smarter, safer, better” may be optional equipment and can cause variations in initial value of the new vehicle and retained value as that vehicle hits used car years which in turn has lingering effects on insurance. Auto insurance companies are increasingly using vehicle data to improve their risk assessment and pricing models for vehicles, drivers, driving conditions, operator behavior, and surrounding traffic. The vehicle data we will discuss in this guide includes connected car data, driver data, and data as it relates to features on a vehicle. This data can be collected from a variety of sources, including in-car sensors, GPS data, and even social media. By analyzing this data, insurers can better understand how drivers behave and how likely they are to get into accidents. This information can then be used to offer more personalized insurance rates and to provide drivers with more accurate information about their risk.

There are several ways that vehicle data can be used to make auto insurance companies more competitive. For example, insurers can use this data to:

Continuous learning in a constantly changing and dynamic mobility ecosystem is needed now more than ever. Auto insurers who are "car literate" keep up with the latest trends in the auto insurance industry. They have a deep understanding of how specific aspects of a vehicle influence the auto insurance industry and the needs of customers. They are able to provide their customers with the information and resources needed to make informed decisions about their specific auto insurance coverage needs, while maintaining an edge over their competition. Auto insurers who are car literate don’t “guess” using a broad knowledge of vehicle information, they pre-fill knowable information from VIN-detailed data for all links in the insurance value chain over the life of the vehicle. Using the right vehicle data is key to this identity.

What is the right data? Where is the right data? What kinds of data should I be looking at? How do I use this data? These are all common questions facing auto insurers.

Perhaps the most important step in gaining a leg up on your competitors is learning how to leverage vehicle data once you have it. This begins by identifying the right data. The right vehicle data starts with VIN-specific data which includes both the old school “squish-VIN” decoder data items like make, model, trim, and drive train, and more advanced intelligence like the full 17-digit machine fingerprint that illustrates and documents precisely every important feature factory-installed on a vehicle. This latter opportunity often needs to be combined with an ‘auto feature key’ to detangle the complexity and combined marketing dialogues used across OEMs. On top of that, we layer in Vehicle Data in the Cloud which brings in insights into how well a given driver is driving, where they are driving, when they are driving, how much they are driving, etc.

Now that you know what data you need, how do you find it? The proper vehicle data can be found through the following methods:

• Gain access to VIN-specific data gathered from the OEM and made available for analysis with a trusted third-party normalization effort (making internal teams become data librarians is uncommon).
• Partner with other companies. There are several companies that collect and analyze vehicle data. By partnering with these companies, you gain access to valuable insights that you can use to improve your business.
• Explore Vehicle Cloud Data sources.

Once ascertained, the vehicle data can be used in a myriad of ways. These are a few key steps that auto insurers can take to leverage vehicle data:

When it comes to pricing, ultimately there are two options: pricing with data and pricing without. We’re sure you can guess which option works better.

Auto insurance pricing is based on several factors, including the driver's age, gender, driving record, and the vehicle being insured. Vehicle data can be used to improve the accuracy of the latter two factors, which can lead to lower premiums for drivers who are at a lower risk. For example, vehicle data can be used to calculate a driver's risk score. This score considers multiple elements, such as how often the driver uses their car, how far they drive each day, and their driving habits. Drivers with a lower risk score will be charged lower premiums.

You can lose more money than ever by being wrong about the vehicle insurance to value gap – both by missing the opportunity to personalize a risk assessment and/or by not knowing the value of the vehicle you would need to replace.

It's worth noting that vehicle data can also be used to detect fraud. For example, if a driver claims to have been involved in an accident that never happened, vehicle data can be used to prove that the accident did not happen. This can help to prevent insurance fraud and keep premiums low for all drivers.

The results of auto insurance claims without using vehicle data can be inaccurate and costly. Without access to vehicle data, insurers are unable to accurately assess the risk of a claim and may be more likely to deny or underpay claims. This can lead to frustration for drivers and can also damage the reputation of the insurance company.

In addition, the lack of vehicle data can make it difficult to detect fraud. Without being able to track driving habits and patterns, insurers may be more likely to be taken advantage of by fraudulent claims. This can also lead to higher premiums for all drivers.