A car dealer sent me a direct-mail “vehicle buy-back notification” for a car I haven’t owned for nearly two years. The dealer has wasted money sending me irrelevant marketing material and, frankly, doesn’t look too smart. But it’s not the dealers’ fault. It’s the data.
This dealer took all the right steps, working with a vendor who pitched a database of vehicle owners who supposedly were relevant to the dealership. These vehicle owners theoretically owned the targeted vehicles and were at the end of their leases or loans. Except the vendor used dated data, at least in my case.
Let’s assume the agency didn’t knowingly use data that was out of date. The problem is obsolescence. The lag time is too great between when traditional demographics, psychographics and garage data is gathered and when it is used.
The way most dealers today use data originated in the 1980s. It was the best idea available 40 years ago, but it doesn’t fit how marketers want to use data today, and how consumers want to be marketed to.
I recently sat in on a presentation where one of the statistics being quoted was from Nielsens’ Digital Ad Rating Benchmarks. Much to my disbelief, 40% of the $16 billion being spent on automotive marketing is going to the wrong audience at the wrong time. That’s $6 billion.
I wouldn’t believe it if I didn’t see examples of it all the time. I’ve been retargeted for items I was never interested in. I’ve received offers in another language (some data provider, somewhere, continues to convince a bunch of brands that I’m fluent in Spanish. I’m not).
The best way to think about data is to view it as a living organism. The more relevant the data, the more dynamic it is and the more it changes. Less relevant data tends to be static. It does not change often.
An example of this would be basic demographics. Age, income, ethnicity, etc. don’t tend to change (except age, but keep in mind it takes an entire year for age to change).
Same goes for psychographics. If you like baseball today, you’re probably going to like it tomorrow, too.
In today’s market, if you rely solely on demographics and psychographics, you’re missing the ridiculously obvious next-generation of targeting: Behavioral data. What if, rather than knitting together a bunch of disparate and frequently wrong data sources, you were able to know who was in market, simply by knowing exactly when they have exhibited behavior that indicates they are actively shopping?
What if you could then use that behavioral data to understand when people who haven’t yet entered the market will be doing so? Today’s modern data providers enable this.
The Truth About Behavior
Tell me the last time your behavior lied. You can’t, because behavior doesn’t lie. It’s also tough to misconstrue behavioral data. If Starbucks sees you buying a latte every day using its app, it knows for a fact you like lattes and it can predict when you’ll be in for your next one.
Once it understands your behavior, your demographics and psychographics becomes less important. It’s an understanding of behavior and ability to anticipate and react to it that makes the sale.
Dealers should focus on the people whose behavior indicates they are about to buy a vehicle. These are people who have been shopping for weeks, submitted leads and visited multiple comparison sites.
How about being able to know when current customers who bought from you a couple years ago start shopping again? Sure, CRM software may have flagged them and started re-engaging around the 30-month mark. But what if you had a way to know they were actively shopping at the 24-month mark?
Today’s behavioral data providers offer privacy friendly capabilities to allow the auto industry to better target imminent buyers and not waste money on people who aren’t in the market.
Brian Epro is vice president-automotive at data company marketer Jornaya.
