We live in the age of data. Every day, people create at least 2.5 quintillion bytes of data and we’ve generated 90 percent of the world’s data since 2016, according to this Forbes piece from May.
While these statistics may seem startling, they underscore a new reality: An incredible amount of data exists with insights hiding in plain view that previous generations could never have imagined.
At CarLotz, our data team wades through the information onslaught and continues to refine the characteristics of the ideal vehicles to sell through our retail remarketing channel. As new retail and wholesale data comes available, our team continuously updates our model to ensure our clients sell the right vehicles in the right channel to achieve the highest possible returns across their remarketing portfolio.
Using insights from this data, progressive remarketers are re-evaluating their strategies to generate the greatest portfolio returns.
While we generally see a consistent lift when clients sell vehicles at retail as compared to their wholesale alternatives, our data team continues to use our transaction level data to determine what levels of reconditioning, what shipping distance radius, and what vehicle classes will maximize the retail remarketing results.
When buyers purchase a car at wholesale, they are inherently taking a greater risk than when purchasing at retail. Most importantly, if something is wrong with the vehicle or transaction, the wholesale channel provides buyers with fewer opportunities for recourse, and wholesale buyers factor in varying degrees of reconditioning requirements into their offer price, reducing proceeds to sellers.
Retail buyers, on the other hand, anticipate little to no reconditioning spend into the price assessments, have more time to spend with a vehicle prior to purchase, and have more recourse against the selling dealer should post-sale issues arise.
As such, retail buyers consistently pay a higher price at retail than they would at wholesale for the same vehicle – therein lies the general opportunity for progressive retail remarketers. The specifics, however — i.e., which vehicles to sell, when to sell them, and what reconditioning investments to make — can help remarketers maximize the returns their portfolios see from incorporating retail into their remarketing strategy.
Key findings from remarketing data review
To highlight the consistency of retail remarketing returns, our team looks at the difference between the retail price (the average price for a vehicle listed on major auto selling websites) and the wholesale price (average MMR).
In the charts here, you’ll see in the data that by holding the year, make, and model constant at different mileage levels (see the 2014 Ford Fusion SE chart on the left) and by holding the make, model, and miles constant at different model years (see the 60,000-mile Chevy Equinox chart on the right), the gap between wholesale and retail stays remarkably consistent.
For example, on the Ford Fusion chart on the left, the retail lift over wholesale is between $2,200 and $2,500 at every mileage band.
With the Chevrolet Equinox chart on the right, you’ll see the retail lift is also in that $2,000 range with some outliers at $3,000 for the 2016 and $4,000 for the 2013.
Obviously, in the case of the Chevrolet Equinox, we would recommend to our clients that their 2013 and 2016 Equinox product should absolutely be diverted to the retail remarketing channel, while their 2012 model years for this vehicle should go to the closest remarketing option, whether that’s retail or wholesale.
We also look at brand to determine which vehicles will perform best at retail. Here, you’ll see data for a variety of comparable trucks and sedans. For example, for 2015 model-year vehicles with 80,000 miles, the best performing vehicles were Silverados, RAMs, Titans and Tundras.
While F-150s performed well at retail, the spread versus wholesale was not as compelling as the other truck models. In this case, we would make different triage decisions for our client trucks to maximize their relative returns.
On the sedan side, the retail lift is fairly consistent across these 2018 low-mileage comparable sedans, so instead of prioritizing a specific make or model, we would suggest our clients prioritize send us vehicles closest to our retail locations.
Another variable we isolate is vehicle condition. In the charts here, you’ll see that the retail lift on lower-conditioned (i.e., higher-risk) vehicles is significantly greater than the lift for similar vehicles with a much higher condition report grade (i.e., lower).
Going too low on the CR scale can further reduce returns due to higher than ideal reconditioning expenses, so we typically aim for the center part of the bell curve of vehicle condition to minimize reconditioning spend, maximize proceeds, and ensure that the right vehicles are triaged to the right remarketing channels.
Our ability to collect, consolidate, and analyze data from various sources and subsequently present insightful, actionable conclusions to our remarketing clients help them make better remarketing decisions and enhance the returns on their portfolios. As the retail remarketing channel grows, effective data analysis will play an even more important role in enhancing remarketing efficiency and returns.
Michael Bor is co-founder and chief executive officer of CarLotz.