Corporate Blog

How to Keep the Auto Buying Cycle from Spinning Its Wheels

This blog post originally appeared on Turn was acquired by Amobee in April 2017.

This is the third post in our Analytic Framework blog series. See first post on balancing targeting precision with broad reach and the second post on using analytics to find your audience

The origin of the phrase “kicking the tires” is murky. Some say it was a way of testing wooden wagon wheels to make sure they weren’t rotten. Data shows something more conclusive. Kicking virtual tires is a much weaker indication of purchase intent than kicking real ones. That’s what an auto brand discovered when it did a deep dive into its data.

Programmatic offers a cornucopia of consumer insights. Marketers’ preconceived notions often fail to withstand the scrutiny of hard data. This was the case with the auto brand as well.

Beware of the One-Model Shopper

Like every marketer, the auto brand was curious about how consumers interacted with its various sites and how those interactions translated into actual sales. These days, that’s a complex task. Cataloging the consumer’s journey across different media channels and touchpoints can be daunting. In the auto brand’s case, the analysis included data from dealerships as well.

When analyzing large amounts of data, the challenge is to pick out important trends and checkpoints. Turn’s checkpoint analysis is designed to isolate data that shows a consumer is taking a step through her journey. In this case, the auto brand was interested in seeing lower-funnel action on a car website, which is a good indication that the consumer is serious about buying a car.

One thing we found in this analysis was that consumers who looked for a lot of different models were much more likely to later do a lower-funnel action (like purchase or visit a dealership) than those who were searching just one model. Armed with this knowledge, we targeted consumers who had cross-shopping experiences on the belief that these consumers were much more likely to purchase.

Virtual Tire Kickers

Another interesting insight was that consumers who played with the online configurator often weren’t serious about buying a car. One reason is that those users were much younger than average. The auto brand used this information to tailor the experience more to try to get the consumers to visit a nearby dealership where they could test drive a real car.

Game over? Not so fast. If you take the long view, those virtual tire kickers aren’t bad customers to have around. They may not be in the market now, but they are clearly interested in some models and may buy a car sometime down the road.

The challenging aspect of this is that calculating the lifetime value of a customer is tricky. You would have to look at the behavior of such consumers over several years to reach an accurate number. Consumers are also keeping their cars longer than ever. The average age of a car on the road in 2015 was 11.5 years, a new record, according to IHS Automotive. Consumers also spend close to nine hours, on average, researching their auto purchases.

Adding to the complexity, in a multi-device world, there might be cookie loss after two weeks that indicates that a consumer is no longer in the market. However, if marketers can connect activity on mobile devices to the same consumer, they might find conflicting data that shows that consumer is still interested.

With the average new car costing $33,560, that is some important data. With so much at stake, every marketer ought to kick the tires on programmatic.

For more on analytic frameworks, see the intro to this series.

Dan Faber

Analytics Lead