TV Measurement Now A Marketplace of Options

Our Guide to Selecting the Best Options – Addendum

With all the recent news about shifting demographics and shifting voter perceptions, you may have missed the drip of news about Nielsen’s TV ratings disruption.

For context, this week the Myers Report’s annual survey forecasts that CTV, streaming and OTT video channels will see the most growth in 2025, with viewership continuing to surpass that of linear TV. Disney+ reported a profitable quarter. Paramount showed an 18% uptick in streaming ads, with a $49mm profit. Paramount’s premiere of the second half of Yellowstone was massive, as measured by VideoAmp.

That’s just this week.

All this opens a new marketplace for how “TV” – however you define TV – should be measured and valued. And what’s really interesting – is that TV’s measurement, especially its reach, has historically been a starting point in media planning.

This week is the second part of  a Q&A I had with one of the industry leaders in the media measurement space, Josh Chasin, winner of the Erwin Ephron award among other accolades. (Disclosure: Chasin is an advisor for Mediastruction’s analytics practice, FutureSight.online™

An Interview with an industry measurement guru

Below is our Q&A with one of the industry leaders in the media measurement space, Josh Chasin, winner of the Erwin Ephron award among other accolades. (Disclosure: Chasin is an advisor for Mediastruction’s analytics practice, FutureSight.online™)

Q:  Where does modeling and attribution fit into this new ecosystem of TV measurement?

A:  I’ve used the metaphor of coffee to describe ratings, or GRPs. You know those stores where there are barrels of fresh coffee beans in burlap sacks, all sorts of exotic beans from around the world, and the store smells great? And you might tell the shopkeeper, “I want a robust, bold, non-acidic, nutty morning blend.” And he recommends a particular beans, and asks, “How many scoops?” Ultimately, at the risk of straining my metaphor, impressions are like coffee beans; we need a unit cost to sell them, despite the rich tapestry of attributes each may have. Ratings are effectively “the scoop”.” But modeling and attribution are the tools that tell us what blends we need, and how effective we can expect those blends to be. We still have to buy those impressions at unit cost (CPP; or cost per scoop). Modeling tells us which ones to buy, how much to send and what outcomes to expect

Q: It seems like the ad industry typically calls for consolidation with every advancement in tech or data. And, yet, we’re seeing the opposite occur with ratings services. Historically, Nielsen has been the media buying currency. How do you see increasing fragmentation impact our currency – and how the buy and sell side determine inventory value?

A: Well, two things:

First, there will almost certainly be consolidation in the measurement space in the next year or two. Many of the companies in and around the space are actively for sale. I think we’ll see new alignments and configurations of these companies soon.

Second, there’s another factor that I think will affect how we use data to evaluate inventory. The migrations of viewing from linear to streaming means there will be fewer impressions; we have a lower tolerance for spot load on streaming services, and the services all basically let you buy your way out of advertising with pay tiers. Now assuming we don’t want to reach fewer prospects, so reach goals remain constant, frequency has to go down. It’s a tautology. So, I think we’re going to see effective frequency increasingly defined not by a number of exposures (in grad school we learned, between 3 and 10). Instead, effective frequency will be defined by the consumers’ cognitive experiences with impressions. One impressions that is delivered in an attentive environment, which resonates with the consumer, will drive better results than 12 unlanded impressions, to that same consumer. We need to stop relying on excessive frequency and use data on attention and resonance – input into intelligent models – to make media work better for both advertiser and consumer.