Mike Stern loves his data but could not be classified as a “quant.” He’s programmed myriad radio stations, but also spent several years consulting Arbitron about how to best communicate ratings to PDs. In this guest post, he takes a look at the role that data plays in the building of content and brands – the good, the bad, and the scalable. – FJ
There are countless examples of how algorithms and the ability of computers to collect and crunch huge amounts of data affect the world in which we live. Industries as diverse as banking, baseball, and politics are just a few examples of businesses that have been turned upside down by the growth of statistical analysis. And radio has been impacted, too.
A recent Wired article by Felix Salmon details how power in an industry shifts from personal interaction and human intuition to a focus on data that’s led by people who don’t have as much industry experience but know how to effectively massage numbers. Radio is part of this story, and as I read Salmon’s piece, I couldn’t help but think about how radio has been impacted by the “quants” or data-driven managers. Here are what he calls the four stages:
1) Pre-Disruption: This is the period before the number crunchers and computer programs take over. Think about dating services before Match.com came along or retail stores before the advent of computer-driven floor space management. For radio, this was the period where spots were written on the log, and music was “managed” by the DJ from songs on cards in a file box.
2) Disruption: This is when the data-powered implementation hits. From political campaigns of the past that simply stumped around states everyone thought were important to the 2012 Obama campaign – the first to move from more generalized targeting of demographic groups to a very specific granular approach to finding potential voters. Or the airlines when “yield management” kicked in, creating a situation where just about everyone on the plane paid a different price based on myriad factors controlled by their computers.
In the world of radio, music scheduling systems quickly took over playlist management, voicetracking began to more efficiently use existing air talent (or imported jocks from out of town), and PPM came into being – providing granular data about radio listening and the people willing to carry meters.
3) Over-Reach: This is the phase, according to Salmon, where managers “often don’t know when to stop—and they create systems that encourage cheating.” He refers to school districts “teaching to the test” as a good example of data controlling the process.
And we know in radio, many over-analyze the ratings, drilling right down to households and even individuals – giving them more importance than the larger audience listening to the station. The result of this over-reach was “mic flight” (a term coined by Mark Ramsey) that led to on-air talent being all but cut out of many stations and an over-emphasis on perfecting song rotations and removing tune-outs. That, along with the economy, consolidation and other factors led to Radio Over Reach – an over-reliance on syndicated programming, national formats, voicetracking, and stations without any hosts at all.
4) Synthesis: Finally, in some industries and companies, cooler heads eventually prevailed. Salmon notes that Moneyball taught baseball that data could be used to predict talent potential, and the Oakland A’s took that to the bank. But in his book The Signal and the Noise, Nate Silver writes about how the Boston Red Sox effectively combined the new statistical model with traditional scouting methods to win a World Series.
That’s radio’s opportunity as well – wisely using the available data but effectively integrating the personal and local side, along with managerial talent to build better brands. Music scheduling systems are integral to controlling song inventories, but smart programmers know that to create surprise, a sense of place, and a real-time vibe, that human touch is an important part of the music presentation process.
Some programmers are starting to effectively apply pre-disruption tactics to their stations, bringing back a balance of art and science to their programming, while focusing on music discovery, storytelling, and content that distances their brands from algorithmic challengers like Pandora or even iPods.
So, with apologies to Otis Redding, I suggest trying a little synthesis.
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