Media Insights Q&A with Jeff Boehme - Kantar - Charlene Weisler

Jeff Boehme, CRO of Kantar, started his career in broadcast at NBC. From there he immersed himself in local cable at the NCC which provided an excellent grounding in the potential of return path data and its applications. From there to Nielsen and now at Kantar, Jeff is setting policy on data metrics and analytics. In this interesting interview, Jeff talks about the data as currency, challenges and opportunities, the difference between MSO, Telco and Satco data and offers some insights into what the future of research looks like.

The six videos of the complete interview are as follows:
Subject Length (in minutes)
Background (3:43)
Kantar STB Data (5:41)
Addressable (5:04)
MSO, Satco, Telco (5:05)
Measurement Currency (6:16)
Challenges to STB Data (4:29)

The videos can be viewed at www.WeislerMedia.blogspot.com Click here to view the videos.

Below is a short excerpt of the interview:

CW: I am curious to know if you have seen difference between return path data, MSO versus Satellite versus a Telco. Are there differences?

JB: Yes. There are actually lots of differences. And if you think about the three types of platforms – at Kantar we define a platform as a video provider – from a telephone company from a MSO cable operator or a satellite company (and we are looking at over-the-top data now too) its not the difference between a telco and a cable company, it's about who is collecting the data, who is their third party, and more importantly what are the data output. Return path data – the tuning events, the start time and the end time and the duration in between – will have different results depending on who is collecting the data. Let's look at dwell time for example; one of our providers has a one second dwell time. Every tuning record that comes in is automatically assigned a value. And then we have a company on the other extreme that has a five minute dwell time. Nothing is counted that doesn't have at least a five minute continuous tune. That means that you can't really measure commercials using that particular portion of the data. Those types of differences require careful modeling.

CW: Many agencies are interested in addressable advertising. One of the challenges is that it is all custom. Is there a way to standardize addressable advertising metrics?

JB: There is a lot of discussion, not just with our clients but also with several industry organizations such as CIMM and the MRC. We are on an MRC committees to establish standards, not just for addressable advertising but also for return path data. We believe that return path data is really the right term because we are device agnostic. Kantar measures audiences to content and also to devices so if it is a set top box, if it's a smart TV, if its an iPad – our goal is to measure audience tuning levels.- usage levels for any device. Specifically to your question: How we loop them together is important but keep in mind that every platform may have a slightly different application. Direct TV, Charter and Cablevision New York actually use different applications. What we try to define are common hooks that define usage so that we can not only link advanced advertising from the television platform, but also across media. So if you've got an online application and a mobile application and you have a television smart app, we want to be able to link through some common denominator. We are working on those solutions with our sister companies.

CW: So are you data matching or data fusing?

JB: At this point we are data matching. And there are advantages to that. With the larger samples that we get from return path data and the larger consumer data sets that we have available, it is more reliable if you can actively match the tuning to the same households that are doing the purchasing. We have conducted some very successful projects matching data with our sister company Kantar Retail and Shop Comp data which has 80 million households of purchase records through club cards. What we are able to define for our customers are purchasing patterns matched to tuning records and combine the two to see which network is the better environment for purchases.

Interview conducted by Charlene Weisler, Weisler Media LLC. She can be reached through her research blogwww.WeislerMedia.blogspot.comor atWeislerMedia@yahoo.com. Twitter: www.twitter.com/weislermedia

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