In 2017, The Economist declared data as “the world’s most valuable resource.” This observation is proven correct with each passing day. Access to data is now hailed as one of our most precious commodities, like oil and petroleum for generations before. Unlike the commodities of the past, the utilization and implementation of data is far trickier. It’s fairly easy to extract, usually more difficult to refine, and the hardest part is often figuring out how to utilize it internally and externally within a company.
Customers also want data — but they want it in a format they can understand. In the auto remarketing industry, commercial resellers want to understand pricing trends to help tailor their sales approaches, rather than stay stuck in the days of blanket, one-size-fits-all offerings. Custom vehicle remarketing solutions are not possible without access to accurate, reliable data.
Data is so valuable, in fact, that new companies are being created every day to contextualize data and draw new insights from it. For example, you may think a scooter is not the ideal vehicle of choice for most, but with access to mobile apps, data, and viral marketing, companies like Bird and Lime have become billion-dollar valuations in less than a year.
But how do you close the data technology gap, regardless of industry? In my line of work, many have noticed that the used car industry is behind when it comes to utilizing this prized resource. During my days at DRIVIN, I spoke to many dealers who felt that even with pricing tools, the customer had more insight into the market than their sales managers. So the question becomes, what’s needed to bridge this data gap?
Bridging the Big Data Gap
In the short-term, the industry must work together to standardize digital asset build. This means continued investment in data science to focus on machine learning, computer vision, and artificial intelligence (AI), which are essential tools to succeed in delivering a highly personalized used car offering.
Engaging commercial resellers is also critical to transforming the auto remarketing sector. Analysis of their portfolios is needed to develop data utilization platforms that improve the efficiency of the online and physical auctions.
But this is just the tip of the iceberg when it comes to applications at the intersection of data and new technology. We hear a lot about AI and machine learning, but what does all that mean and where does it fit with data?
KAR’s computer vision and machine learning specialties within data science, for example, are changing the dealer-to-dealer vehicle auction experience. The image recognition projects tied to KAR’s insurance side of the business, coupled with TradeRev’s H initiative, are making it possible for dealers to now get a complete set of images in just seconds with greater accuracy, clarity, and reliability than ever before.
The future of auto remarketing will be shaped by technologies like these that adapt to the evolving landscape and harness the changes taking place within the sector. Extracting the data and utilizing it within a modern user interface is a necessity. The usage of data and creating highly engaged and efficient tools, services, and products will be the difference in winning or losing.