Image: Hyundai owners who shopped for either a Hyundai, Toyota or another brand in the LA market captured by AutoPulse data for November 2016.

A prospect visits a dealership yesterday. She walks the lot, talks to a sales rep and carefully reviews several new models. She manages to leave without the sales rep capturing her name, or any other contact information. While this is not the way it’s supposed to happen, it does, everyday.

Today, that opportunity is not completely lost. Many of these unknown prospects can be identified to the extent that the dealership, OEM or even the dealer’s website or CRM provider can email or text her a marketing message the very next day, addressing her by name and know additional information like her home address, year, make, model and VIN of vehicles owned and even her equity situation.

This same data set also enables marketers and OEMs to identify customers who own a particular make and who are shopping competing dealer makes as soon as one day after they were on the competing dealer’s lot and know the same information as above. It’s powerful and yet scary stuff.

How does it work? Geo-fencing or beacons you say? No, much more sophisticated than that. Most people are oblivious to the full terms of the EULA (end user license agreement) they sign when they download an app or play a game on social media. In many cases the user has agreed to allow the application developer to capture data to be used for marketing purposes. What happens when you aggregate data from over 90,000 of these apps, including leading sites like CNN and the Weather Channel, and you are sophisticated enough to begin to combine that information with other databases? You get “AutoPulse.”AutoPulse is the result of thousands of these data sets being carefully merged together and combined with location data that is sourced from a shopper’s cell phone.

Combining the mobile phone location data, the dealership location and actual consumer records like VIN and credit with other data produces a very rich and actionable set of extremely timely insight and PII data.

A key question for all marketing and analytical types would be “how are you able to work with the PII (Personally Identifiable Information)?” PII is any information that can potentially identify a specific individual when de-anonymizing anonymous data. The answer is that those protections are waived by the user when they agree to the EULA attached to the apps or games they downloaded – a good caution for us all.

What information is available? Their name, address, VIN number for any car they own, phone number for some and email address for about 50%. Yes, this is powerful stuff.

How accurate is the data? To vet the accuracy of this data we hired Experian to do a test of it. They reported that the VIN matched to the household was 98.5% and the VIN matched to the Signal record was 100%.

We wondered if just because someone showed up as a Signal shopper, does that mean they were buyers? We sent a large sample to the industry leader in state sourced registration data to do a match against their vehicle sales records. We were delighted that they reported a 99.95% customer match rate and that the purchase rate was right on par with their buy rates. With an average of over 800,000 AutoPulse records per month, this is an extremely good proxy for the entire industry and it yields a significant amount of actionable marketing and lead generation opportunity daily for every brand.

What kind of insight can come from this? By knowing who is on a dealer’s lot, when they are there and what kind of vehicle they own, we can see some interesting things. For example:

Comparing AutoPulse dealership visit data from October to November of 2016, we see that Lexus as a brand performed extremely well in terms of pulling in owners of non-Lexus vehicles and Lexus dealers did a great job of attracting owners of Mercedes vehicles. On the other end of the spectrum, we see that Jaguar owners were shopping not only at Lexus and Mercedes dealerships, but Chevy as well. And of course, we can pinpoint the rate of increase or decrease from any time range and zero into a DMA, region or zip code and home.

With this data, we can spot important shopping trends while there is time to do something about it. As an example, we can identify Hyundai owners that are shopping on a Toyota dealer’s lot and even Hyundai owners who are visiting dealers, but have not returned to a Hyundai dealer’s lot. Obviously that information can be used to send either loyalty or conquest marketing information as soon as one day after they were on the lot. It can also be rolled up into monthly reports to identify and compare the brands that are losing ground, the location and which brands or even stores are winning and losing.

Who is behind this data? We are pleased to announce that Root & Associates is partnered with Digital Data Solutions. Stay tuned. We will have much more on this in the following months.

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4 Comments

  1. Once again Kevin you are ahead of the learning curve in marketing research. Your approach shows promising capability for improving sales and marketing results.

    We look forward to future insight from “Signals” and Root and Associates.

    David Greene

  2. Kevin, wonderful explanation in many fronts. Are you using this information, in any way, for the service side or only sales?

    Thank you and perhaps we get to say hello in NOLA.

    Greg

    1. Hi Greg,
      Sorry I missed this note before NOLA. Would love to connect anytime.

      And yes, it does have service capabilities!

      Let me know when you would like to catch up.

      Kevin

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