Examples in Innovation: TVision
While a growing number of competitive TV ratings companies are combining native measurements of cable box and smart TV data, there is another group of companies innovating invasive measurements to enhance the informative value of native measurements.
TVision is a great example of such an innovator.
Sensing that button pushing is not the best way to measure who is watching the television, TVision decided to swap out the button for a camera: thereby converting the active requirement of self identifying button pushing with the passive camera-based recognition measurement of who is in the room and incrementally, who is looking at the television with their eyes open (what I call watching the television).
TVision started with specialized commercial technologies that require a team of installers to set up in cooperating households to deploy, collect, and process the feeds to report both in the room exposures and eyes on second-by-second TV viewing. The purpose of these operations were twofold: they offered proof of concept and data to start understanding television watching better. Upon achieving these milestones, TVision went on to innovate by rewriting all aspects of the systems, migrating from specialized, hard to install, gear to generic, easy to install, gear.
Today, the now inexpensive gear is so easy to self-install that TVision simply mails it to cooperating households. Since the camera imagery never leaves households, cooperators are comfortable deploying the gear: yes, even in bedrooms! Given the costs of setting up and maintaining panels is expensive, TVision sends only one kit to each cooperating household and asks them to install it on their primary television.
TVision’s panel proves that its technology is both useful, with its new ability to passively report second-by-second whose eyes are on the television, and usable, with its real life in the field deployment and operations.
Strategically, TVision has accomplished two things of note.
Well, well, well, my “Losing Relevance” post certainly caused a stir.
Yes, measuring who is paying attention to TV has many dimensions, many of which are independent measurements (orthogonal for the math crowd).
The key measurements are:
In a perfect world, we would measure everything everywhere. In a second best world, we would measure everything in a single random sample big enough to report everything. My viewpoints on these: Good Luck! … Measuring a random sample over time in the form of a panel involves extensive upkeep to maintain its randomness relative to the universe it purports to represent. And even that does not convey the whole of this complexity, as you need to not only keep your cooperation frame random relative to the universe but you have to keep the participation (for none passive measurements) random relative to the universe too. … Many tricks can be used to lower the immense cost of randomly selecting people, such as focus on households, enumerate them, stratify them, before randomly selecting within strata, then weight for individuals - this is much less expensive than randomly selecting enough people to report out basic demographic behaviors. OK, real random samples are unaffordable but this solution looks pretty random and representative: so, let’s do it! Now how do we get a big enough panel to measure advanced targets, low rated programs, etc., etc.
My viewpoint: Tackle these key measurements in parts and then stitch them together to report the whole. Yes, there are many challenges to understanding what is on the television as an example. Native measurements (cable boxes, Smart TVs, etc.) have their assorted shortcomings. Invasive measurements to determine “nearby” and “eyes on” do too, in addition to the sampling challenges. But if we pivot our thinking to leveraging invasive measurements to calibrate and impute behaviors on native measurements, then we solve for panel sizes and the disadvantages of the various native sources while creating an easier ecosystem to innovate in. Innovating specific measures is much less challenging than innovating the ecosystem as a whole. See “Attention is Everything”, “Into the Weeds”, and “Losing Relevance” for more on the arc of this viewpoint.
Originally formed by the US Congress to review and accredit the Nielsen Company’s ratings service, the Media Rating Council (MRC) needs to evolve on TV ratings or lose its relevance.
For the longest time, the MRC has stipulated that TV ratings have to be measured from either census or random samples. This stipulation is the root of Nielsen’s TV ratings problems.
Big picture: affordable random samples are not large enough to successfully report ratings of advanced targets and / or low viewing programs. This became apparent three decades ago, and the evolving use of first cable box feeds and later ACR feeds from smart TVs too has now grown into robust competitive TV ratings sources and services from not only Comscore, but now iSpot, VideoAmp, Samba, and Inscape among others. In all these new cases (originally innovated by Comscore), competitors are weighting collections of native measurements from cable box and smart TV feeds to project local and national TV ratings.
These very large samples of native measurements allow for easy reporting of advanced targets and / or low viewing programs.
During this period of evolution, Nielsen has stuck to its knitting of reporting from random samples. Even when Nielsen incorporates native measurements from cable boxes or smart TVs, they only include them as direct measurements for those households and do not leverage them to project those viewing patterns to local or national TV ratings. Consequently, Nielsen’s very large native measurements enhance but do not solve their reporting challenges yet.
Shortly after leaving Nielsen many years ago, I started to publicly advise investors that Nielsen’s coveted random samples for measuring TV Ratings would devolve into a calibrating panel to be applied to native measurements. If you are interested in more on this, please read “Into the Weeds of TV Currencies”.
I wonder if Nielsen will finally make this pivot away from the MRC stipulations (of reporting either census or random samples only) to the lower cost solution of using small invasive measurement panels like their random samples to calibrate much larger native measurements to compete with the new generation of competitors.
Mark Green is co-founder of MediaBrain Inc.