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MediaBrain Musings

For Those Who Love the Details of TV Measurement

4/6/2023

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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:
  1. Is the television switched on?
  2. What is showing on the television screen?
  3. What sounds are coming from the television?
  4. Who is nearby the television?
  5. Who has their eyes (including open) on the television? … my definition of watching.
  6. Who is hearing (not necessarily listening to) the television?
Many want to understand states of mind too. All fine, but that is another measurement, which has causal connections to prior measurements - for sure, but if you do not want to impute it, it is another measurement.
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.


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    Mark Green is co-founder of MediaBrain Inc.

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