$1.3 Billion Is A Lot Of Money

With the approval of Nielsen's takeover of Arbitron, it's dubious that anyone thinks that broadcaster costs are going down anytime soon.

So, since it's fait accompli, let's at least inventory what I hope we all get for the money.

First, thanks to Wired's Tom Valderbilt, some history:

In the years after its founding in Chicago in 1923, the A.C. Nielsen Company thrived, thanks to a commitment to math and technology. While its competitors called random households and asked them what they happened to be listening to on the radio at that moment, Nielsen developed more sophisticated sampling methods. Rather than rely solely on self-reporting, Nielsen employed a device called the Audimeter that used photographic tape to automatically record listening activity. When television arrived, Nielsen used similar meters for viewing—although they were supplemented with paper diaries. But by the late 1950s, Nielsen sat comfortably atop the media-ratings industry. It had few competitors, and since television habits remained static, it had little reason to keep innovating.

Under the terms of the FTC’s approval, as Variety Digital Editor Todd Spangler reports Nielsen must continue to support Project Blueprint, the cross-platform project measuring TV, radio, PC, mobile and tablet engagement that ESPN has been working on with Arbitron and comScore, so perhaps Nielsen CEO David Calhoun's statement (“We are looking forward to providing all of the benefits of the combined company to our new clients in the radio industry and their advertisers, driving incremental value for them as well as our shareholders.”) means that we will all be getting more for the money, better, more reliable data to reflect a more mobile media world.

• Will Nielsen households increase from 25,000 to 75,000 (the national PPM radio panel) at no additional cost to current subscribers?
• Will the increased sample size permit cross-media usage measurement like BBM Canada has been doing since they implemented PPM in 2009, displacing Nielsen's TV measurement?
• Will sample weighting decrease, increasing reliability?
• Will PPM be extended to all TV markets now measured by Nielsen so that radio in those markets gets PPM data at no additional cost?

Skeptics abound, so all eyes will be on Nielsen, starting, perhaps this week, at AdWeek 2013.

After a late 2008 announcement that 50 Cumulus markets were to be measured by Nielsen and a revelation 18 months later that Cumulus CEO Lou Dickey was pleased with the results, the industry never actually saw that public release of the data which was promised, first in June of that year, then later in August and then ultimately Cumulus did not renew their deal with Neilsen, so you'll pardon me if I am fearful.

In February 2010 at Country Radio Seminar, Clear Channel Senior VP/Research Jess Hanson and I attempted to get representatives of the two ratings giants to "take the gloves off," but it turned into a gentle confrontation as then-CEO of Arbitron, Michael Skarzynski ended up a no-show, replaced by the able fencer Dr. Ed Cohen, but Nielsen Media Research's managing director for North America Lorraine Hadfield was ill-equipped to talk competing methodologies with the experienced researcher and stuck to her talking points, meaning that what was billed as a barn-burner actually turned into a good session to catch a nap, which is probably the way both of them had hoped it would be.

Ultimately, as Wired's Vanderbilt wrote, if I may adapt and paraphrase him a bit: "It all adds up to a potentially thrilling new era for radio, television and new media, one that values shows that spark conversations, not just those that hook us for 30 minutes (for TV and just 11 PPM minutes for radio). The stakes are high: Get it right and great programming will continue to thrive. Get it wrong and both the $70 billion television industry and the $16 billion radio business will be in jeopardy—along with your favorite radio station, personality and TV show."

Hopefully, Neilsen makes the most of their huge investment in the future of all U.S. media ratings by improving the value of what they deliver to clients and media buyers .. and wastes no time in doing so.

Arbitron's Dr. Ed Responds

My recent post "The Right Way To Sample" generated this reply, which I'm delighted to receive and share with you:

Thanks for the opportunity for “equal time” (no, we won’t go there) to the comments in your April Fool’s Day blog about Arbitron’s sampling.

First of all, thanks for the kudos on the cell phone only sampling that’s now underway in 151 markets with the rest of the markets (except Puerto Rico) on the way this Fall. We’ve worked very hard to bring this positive enhancement to fruition after much testing. Oh, and by the way, your readers should know that the market in question that had double the intab for African-American was actually only 50% over for one phase and is presently 25% over for two phases. As we always say, it’s a twelve week survey…let’s see how the full twelve weeks finish. We do like to set the record straight.

Now, let’s go on to the issue about single versus multiple person per household sampling and Ted Bolton’s piece from 1995. The concept is known as “probability of selection”.

Let’s start with the factual errors because these often become “urban myths” (or perhaps with your blog, they could be “country myths”). Arbitron does not select individuals from a list. Arbitron uses a random digit dial (RDD) telephone sample of residential landline phone numbers. Landline phones, with a few exceptions, are tied to households. While not perfect, you can generally assume one landline phone number per household.

Ted spent much of his piece writing about lists and quotas. Those of you who still have budget for a perceptual are probably using lists of names and setting quotas for different demos (or more accurately, your research company is doing it). While quota samples tend to be the norm for custom research, that doesn’t make it right, just expedient. You can’t use quota samples to tabulate radio ratings that are projectable. We can’t call a house and say “We’re looking only for a Male 18-24 year old”. To do valid radio ratings, you must use a probability-based sample.

But on to the main point. In simple terms, Ted was wrong. Multiple person per household (MPPH) sampling has long been an accepted methodology in media surveys, government surveys, and many other kinds of surveys. And in terms of “probability of selection”, an MPPH frame equalizes the probability of selection across demos while a single person per household (SPPH) sample distorts the probability selection.

Here’s an example of what I mean:

In an SPPH frame, the larger the household, the smaller the chance that any one individual in the household will be selected.

Single person per household sampling is used in many surveys and one reason is that it makes sense for studies conducted by phone. Consider the logistics of talking to one person in a household for an extended period (perhaps 15 to 20 minutes for a perceptual or callout) and then asking to speak to another person. Those of us who design phone surveys do our best to dissuade anyone who would ask us to survey more than one person in a household by phone; it just doesn’t work. As someone who worked at Birch (which used single person per household sampling for those of you who remember Birch ratings), it would have been an operational nightmare to measure more than one person per household.

In Arbitron’s case, nearly everyone is eligible to be in the survey (one exception is most of this blog’s readers who are in the media business). We sample at the household level. Even our new address-based method of finding cell phone only households is designed for the household level. And each household has a relatively equal chance of being selected assuming they have a landline phone. With the addition of the cell phone only frame, that opportunity extends to nearly all households.

Let’s take the discussion one step further. One could weight for this difference in probability of selection. Some people in our business hate the idea of weighting and others would have us weight for almost every variable (are you right handed or left handed?). In statistical terms, weighting reduces bias and increases variance. In simpler terms, weighting makes up for the potential that a group that has different radio listening habits is not represented at their level of the population (bias), but increases bounce in the estimates (variance). It’s a tradeoff and our view is that the large amount of variance (bounce) that would be created by weighting for household size would be a major negative for your ratings.

We’re quite comfortable with measuring everyone above a certain age (6+ for PPM, 12+ for diary) in the household and the need to defend the sampling method has long passed. And thanks again, Jaye, for the opportunity to be part of your blog.

-- Ed Cohen, Vice President-Research Policy and Communication, Arbitron, Columbia, MD (410-312-8592 - Ed.Cohen@arbitron.com

OK, who else wants to add a few cents' worth? Add a comment below or drop me an email.