Online-to-offline marketing analyst Korrelate has been working with data management platform BlueKai to demonstrate the link between online ad data and offline purchase behavior in the auto category. (Read the release.)
We spoke with Daniel Jaye, who has taken the company’s reins as CEO, with founding CEO Curt Viebranz stepping into the chairman role. Jaye helped start one of the earliest behavioral targeters, Engage, and also worked at Tacoda.
AdExchanger: What did you set out to find when you and BlueKai examined people who were defined as prospective car shoppers?
Daniel Jaye: In this case, we’re talking about a correlation between online activity and auto-related shopping habits of consumers, a very bottom funnel sort of metric. We’re seeing something that we first set to prove at Tacoda, which is that behavioral targeting, in the right context, is an excellent predictor and locator of people who are apt to buy something. What was interesting about our results was that we saw tremendous affinity between brand research and brand purchases. It’s not just that auto intenders buy cars, of course, but rather that Volvo intenders are absolutely going to go out and buy Volvo, as opposed to a Chevy.
We’ve had a relationship with BlueKai for some time. But over the course of the last six months, we’ve been working on a detailed analysis of BlueKai’s auto intenders segment, which is data that has been aggregated from a variety of publishers. There have been a lot of questions out in the marketplace about where the data comes from, how good is it, what’s the competition for that data. Since we have this unique relationship with them, we thought it would be interesting to run it through our privacy-protecting, online-to-offline attribution system and see how it really stacks up.
We’ve seen people use this data in the past to good effect. But even we were surprised by how well the data correlates to actual, real world purchases. I was expecting 10- to 20x in terms of efficiencies. We typically saw 30x and the level of brand loyalty was phenomenal.
What accounted for that? What’s so great about BlueKai’s data? Was it the combination of Korrelate’s intelligence?
To be clear, Korrelate doesn’t do any actual targeting. We provide the insights; as I like to say, we’re the umpire not the batter. The difference between our measurement solution and others is that because of our scale, we can drill down more deeply than a panel-based solution – which can give you a thumbs up or thumbs down on whether a campaign performed better or worse – we can break it down to the exact segments and dayparts.
Do you mainly rely on BlueKai? Or do you work with any other data providers?
We work with a number of DMPs and DSPs, as well as ad networks and we have a dozen customers that we are producing results for. They fall into a variety of categories; some are agencies and advertisers looking to evaluate the different sellers of media. Others are ad networks and DSPs using it to determine how effective they are at optimizing placements. We’re also starting to work with endemic sites, such as automotive content shopping or related sites who want to be able to demonstrate how well their content correlates to purchases. And then we work with data syndicators/DMPs, companies that have their own platform for applying data and we help them figure out how that data correlates to actual buying activity by consumers.
What’s your sense of the value of DMPs and the value of cookie-based data in terms of being able to connect that to consumers’ online and offline shopping habits?
The DMPs provide so much value, whether it’s cookie data or just intelligence about how to allocate it. Interestingly, many of the DSPs have morphed into DMPs. Fundamentally, they’re still doing the same job. They’re trying to figure out the best way to execute marketing strategies for their customers. The difference between them and someone on the sell side is that they can look holistically at the entire campaign and media plan.
You could argue that Demdex, one of the first DMPs, had a more pristine separation of being a data manager side and not being so much on the actual buy side. The lines are blurring between providing data and working on data refinement and activation. It is fair to say that looking at cookie data alone is fairly myopic. At the same time, ignoring cookie data is very dangerous.
What’s your sense of the competitive landscape right now? And looking at the comScore patent lawsuit filed late last month against ad effectiveness companies , how crucial is it that data companies have patents as a point of differentiation? Does the threat of these kinds of suits give you pause when considering the ways you analyze data?
There are foundation technologies that are widely deployed and if you dig deep enough, generally speaking, it all starts to get a bit murky about where they originate. I haven’t looked too closely at the comScore suit, but I do know that there is some intellectual property around viewable impressions. But I also remember when comScore was founded and I know we were all collecting data using pixels in ads and on pages. It will be interesting to see how it all plays out. I started Engage, the first online ad targeting company, in 1995, so I know what’s foundational and what sort of data collection won’t be infringing.
We just filed our third patent application around technologies that allow us to develop useful measurement and the ability to collect online behaviors with offline activity down to the individual household, while at the same time, protecting privacy. That’s our big differentiator. And that’s an ability that a subpoena can’t trump.
