I’m continuing with my theme of the past two blog posts, building on an argument that the automotive industry (as compared to other industries) is way behind in our use of data and analytics to help decision-making. To read the previous two blog posts, click here and here.

A couple of months back I was out in northern California, meeting with the used car director at a very progressive dealer group. These guys are as sophisticated as any group I’ve ever seen in terms of their adoption of tools and processes to really improve their game.

As we sat down and started to discuss how we might help these guys, this VP started to lament about his used car managers continuing to make mistakes during the trade-in process. More specifically, he recalled how pretty regularly his used car managers take in A4s without navigation, despite knowing that there is zero consumer demand for this car in their market. Yet despite knowing better, the problem continues to persist.

CompetitorPro VINFactor

Mistakes like this manifest in either wholesale losses, or an attempt to try to retail out of the problem. Either of these mean thousands of dollars in lost profit per car.

This got me scratching my head. This is the most progressive, data-driven dealership I know of, have built their own pricing software in-house and cross-train and share data very effectively, yet they still make mistakes like this every day.

In this era of real-time data and sophisticated software, why does this problem persist to effect dealers?

Why is this happening?

Well, a few things are going on.

First, dealers are still forced to run their business looking in the rear-view mirror. For example, think about the widespread use of price guides across the industry. Price guides base values on historical data, and don’t provide dealers with a real-time view into supply/demand in their market. None of the price guides are yet providing predictive analytics (what is going to happen?; what should I do?) for dealers.

Second, despite the promise of “artificial intelligence”, dealers still don’t have a real-time early warning system to alert them when they’re at risk of making a bad decision — or more specifically in this case, looking at a particularly unattractive vehicle for their lot (i.e. an Audi A4 without navigation).

Last, despite having access to an overwhelming sea of data, none of a dealer’s multiple software tools interoperate and work together to distill all their information into an immediate roadmap of actionable items for them to follow.

But there is hope!

The automotive space continues to be fascinating and have great problems to solve on behalf of dealers. The amount and pace of innovation in the industry is evident by the sheer number of new companies that pop up on the exhibitor floor at the NADA convention each year.

We’ve seen some incredible innovation occur across industries outside of automotive, and some of these innovations are starting to enter the automotive space. I can’t help but feel that we’re on the cusp of really helping dealers make better decisions, based the availability of both data and better intelligence tools.