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Draw New Business for Your Insurance Quoting Through Agile Vehicle Data Tools

The auto insurance market in 2024 is not friendly to consumers. Providers, perhaps in an effort to mitigate rising loss ratio, have increased premiums by 26% in the past year, with plans for these costs to remain elevated through 2025. Consequently, consumers have flocked to auto insurance shopping sites and quoting portals to hunt for the provider that will impact their budget the least, with TransUnion noticing a 12% increase in shopping rates year-over-year in 2023.

The question for auto insurance lead generators and comparative raters is, beyond investing heavily in your market outreach (which is not guaranteed to succeed), how do you stand out as an exceptional product in a market that is both in demand and heavily saturated? How do you make your leads more appealing than your competitor’s leads?

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Topics: Insurance

Explaining the Power of Reverse VIN Lookup in Online Quoting Markets

We live in a world that is swiftly traveling towards a fully digital age, making data collection through form submission an integral part of the internet. One of the easiest ways to add significant value to the quoting portals and online lead generations for insurance and warranty is through reverse VIN lookup. Consumers are now able to easily enter their known vehicle details immediately without requiring a VIN, which can push leads through the necessary fields with a higher completion rate. The end users (insurance companies and warranty providers) can act on these leads more efficiently because they have the VIN pattern generated from this tool. Lastly, these companies encourage trust with the consumers by providing users with an experience first while still effectively collecting the necessary data.

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Topics: VIN, Insurance, Risk Management

Conversational AI’s Vehicle Integration Will Have Risk Management Consequences

DataOne Software’s blog this year has emphasized the impact of increasing vehicle complexity within a wide swath of the automotive industry, and we are not going to be short on topics any time soon. The latest innovation that has captured the investment dollars of the tech sector, “AI” (which in truth is a mislabeling of the technology – it should more appropriately be called a Large Language Model) is now making its way into OEMs’ development plans, and automotive risk assessment professionals will need to closely consider its ramifications to avoid unexpected losses.

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Topics: Insurance, Automotive Technology, AI

How Trim-Level VIN Decoding Addresses Common Risk Analysis Challenges

The economic conditions of the early 2020s had a unique impact on the automotive industry. Dubbed trimflation by car culture website Autopian, the output in entry-level models was greatly reduced, while high-margin trims became more commonplace and MSRPs were increased across the board. UMass Amherst Professor Isabella Weber noted, “Companies in the automobile sector [amplified] price pressures enabled by a form of temporary monopoly granted by the computer chip shortages. This allowed car producers to focus on expensive models with higher margins and generally raise prices without having to fear a loss in market share.”

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Topics: VIN, Insurance, Risk Management

Future-Proofing Your Auto Insurance Risk Analysis Modeling - Get the Whitepaper

The automotive industry is running parallel races toward fully electrified and fully autonomous vehicles, and both races have a deceptively close finish line. EVs will become the norm within 10 years due to a combination of government legislation and manufacturer mandates – Progressive estimates that EVs could comprise 40% of car sales by 2030. Vehicle autonomy, meanwhile, remains a top priority for OEMs as well, with the market projected to reach $200 billion by the end of the decade. Mercedes-Benz is already certified for Level 3 autonomy, a standard that encompasses conditional hands-free driving, suggesting that Level 5 (totally automated driving) is a feasible goal for automakers more quickly than initially anticipated.

The technology involved in developing electrified, autonomous vehicles will not be universal; each feature package will have its own unique benefits, drawbacks, and relationship to road conditions and driver capabilities. Consequently, auto insurers cannot treat “ADAS” or “EV” as monolithic or even loosely segmented variables in their risk analysis; all levels of the organization must have a comprehensive understanding of their car’s technology stack to maintain a balanced risk-to-rate ratio. 

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Topics: VIN, Insurance

Clear and Actionable Vehicle Data is Now Essential for Auto Insurers

Traditional risk models in the auto insurance space incorporate multiple data points, including vehicle details, the driver’s safety record, the vehicle’s geographic location, and that location’s associated weather patterns. While these factors may be weighed differently by each insurer’s model, the entire auto industry is now facing the reality that increased vehicle complexity will force a significant change to risk analysis. 

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Topics: Automotive Data, Insurance

6 Businesses That Benefit from Identifying Vehicle Specs by VIN

In a previous article, we discussed how businesses can leverage a commercial VIN decoding solution to identify vehicle specs by VIN, as well as which specs are often included. In this article, we are going to dive into which businesses benefit from this functionality. The short answer is just about any industry that has anything to do with vehicles, their maintenance, financing, insurance, or transportation. Here’s a deeper dive into some of the most popular use cases for vehicle specs identification by VIN:

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Topics: Logistics, VIN, Parts & Services, Insurance, Vehicle Equipment, Dealers, Fleet Management, Risk Management

The True Cost of Incomplete Vehicle Data for Underwriters

Beyond just misleading, limited data can harm your business. Market analyst, Gartner, found that 27% of the data in the Fortune 1000 companies is considered flawed, which they defined as inaccurate, incomplete, or duplicated. According to their research, poor quality data leads to high costs, a high customer turnover rate and excessive expenses. IBM estimates that bad data costs the US economy roughly $3.1 trillion dollars each year. They also found that 1 in 3 business leaders don’t trust the information they use to make decisions. Experian Data, in a separate study, determined that flawed data impacts the bottom line of nearly 90% of all American companies.

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Topics: Insurance, Risk Management, Finance

New Cars vs. Old Cars: A Diverse Auto Industry Requires Comprehensive Vehicle Data

Today’s automotive landscape is much different than 20 years ago, with the average life of a vehicle at a record high of 11.8 years. Automotive manufacturers across the board are building high-quality, longer-lasting vehicles, provided the owner keeps up with maintenance.

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Topics: Automotive Data, Parts & Services, Insurance, Dealers

What Does a “New Normal” Look Like for The Auto Industry?

We are living in a strange time right with a pandemic (COVID-19) that has greatly affected people worldwide and shuttered economies. Millions of people in the United States are out of work (hundreds of millions worldwide) and many businesses have had no choice but to furlough or lay off their employees, or even worse, close their doors permanently.

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Topics: Parts & Services, Insurance, Automotive, Dealers, Fleet Management