INdustry Column: Census TV Data as "Match Fabric"

By Michael Collette, CEO, Dativa

We often see articles written about various types of data with categorization of first-party (my) data, second-party (your) data and third-party (vendor-aggregated audience) data. As we were developing the Inscape census TV data, I struggled with these definitions.   

On the one hand, you can think of census TV data (e.g. device-level data from set-top boxes or smart TVs) as an aggregation of the first party data sets for all the networks, local stations and advertisers that are found in the data set. That's kinda exciting (and valuable). Such data sets are aggregated and provided by a vendor. And they can be used as third-party data. For example, a census TV data vendor can supply a list of TVs that have displayed college sports or ESPN or Big Ten Network, or what have you. In this case, it really is third-party data.

But that really isn't the main use of such data. There are a great many uses, but the one that is really most interesting at least presently, is to use the data for what I decided might be called "Match Fabric."

Why? Because the really interesting value of census TV viewing data is unlocked when we enable advertisers, agencies, networks and stations to match specific audiences to the viewing data. This tends to occur in something of a cycle.   

On the buy side, advertisers, long frustrated with crude demographic targeting for TV advertising, can now match first-, second- or third-party data to viewing data. An example of a first-party data match might be generating a list of people that bought a Ford truck 5 years ago. Such people might provide a good target set for a new pickup truck ad. Ford, or its agency or data services vendor, can match their truck owners list to the TV viewing data set to produce a list of networks, dayparts, or shows that have high concentrations of the target list.

With initiatives like OpenAP, Ford can then ask TV ad sellers to sell them a media buy that is focused on shows with high concentrations of such viewers.  

Then the campaign runs. With the list matched to TV display devices, the ad seller, intermediary, or data services vendor can then measure the actual reach and frequency of the campaign against the target list to measure effective target GRP.

But we're not quite done. With another match--this time Ford truck buyers--we can produce a deterministic measurement of TV ad effectiveness. This can reveal which ad spots not only reached the most people in the target group, but also produce the greatest number of truck purchases. We can see the impact of frequency on conversion as well as recency and combinations of recency and frequency (e.g. the optimal result in terms of truck purchases might come from a frequency of 3 within 11 days).   

So, census TV viewing data turns out to be extremely valuable centrally because it can be used to match audience targets to audience delivery, to target media buying more effectively and to make it more efficient. I find the term "Match Fabric" useful because we really use the census TV data to connect audiences to behavior, and the term illuminates the virtue and value of the data.  

Over time, we expect that efficiency will quickly evolve from effective CPM, or the cost to reach my target audience, to conversions per dollar of media spend. Once we know how many game downloads we're getting per dollar, that quickly becomes much more important than audience measurement--at least for those advertisers that can easily capture and apply consumer behavioral data.    

Of course, such systems are only as good as the quality of the data that is employed and centrally, the quality of the matching systems that are used to glue it all together. There's great value in high-quality sourcing, matching, transforming, modeling and implementing census TV data as well as the audience and behavioral data that you match to it.

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A leading innovator in media technology, Michael Collette was recently named CEO of Dativa Inc., a data-centric boutique consulting company (www.dativa.com) that helps clients with data strategy, data science and data engineering.  Prior to joining Dativa, Collette was CEO of Cognitive Networks, where he pioneered the generation and implementation of Smart TV viewing data.  Following the sale of Cognitive to VIZIO in 2015, Collette served as GM of Inscape.  Collette was previously CEO of Ucentric Systems, where he drove adoption of whole home DVR in the PayTV market.

 

 

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