Gary Marince
by Gary Marince
Yippee! Finally! PPM is here and we’re getting more information about programming than anyone ever could have imagined. So, what are the really big stories? Well, I’ll tell you . . . after this disclaimer: “WARNING: While learning about programming through PPM, resist the temptation to form hasty conclusions.” And what that means is simple . . . as you read this, look at these statements as observations which can help you . . . and not as facts or calls to action. Having said that . . .
Observation # 1. In PPM, employed male listening is higher than female listening . . . employed males appear to have the most listening to radio.
This helps explain why great Rock stations enjoy healthy ratings in PPM. This is the tricky part, in no way does this imply that employed-male listeners are more inclined to carry their PPM meters than they would be inclined to fill out their diaries. That’s just not true. Here’s what we’re finding, we believe that some of the listening by the unemployed may have been overstated or over reported in the diary methodology. In PPM, where we can measure what people actually hear, the Rock share is not “overwhelmed” or suppressed by overstated listening to other formats.
If you think about it . . . the majority of the workforce is male dominated. And someone who works is more likely to be out-and-about either working or coming to-and-from work. Often, the unemployed, which includes everyone not working, spends more time with TV than radio. More simply stated, there is a greater likelihood that an employed male is using radio – they have more listening occasions with radio.
About now I guess the question is: “Should I target only males in a PPM environment?” That would be a hasty and quite possibly wrong conclusion. I can think of a handful of female oriented stations in both Houston and Philly with monster numbers. This is just an observation and possible explanation – not a call to action.
Observation #2. Cume remains vital . . . but occasions seem to be gaining importance in bolstering TSL. Here’s an overly simplified statement, everyone listens to radio for about the same amount of time when they tune to a station. What seems to be driving the ratings needle is the number of occasions. Remember, we have big cume numbers in PPM. So, the next factor for ratings is Time Spent Listening (TSL). TSL is really a factor of two things: How long someone listens (duration), and / or how often they listen for that period time (number of occasions). Additional tune-ins translate to additional TSL. With boxcar size cumes in place, the additional occasions really help drive share.
So the next question is . . . “Should I then focus solely on TSL?” No, absolutely not. Cume . . . when left unattended, erodes! Track the cume. Make certain it’s viable, stable and / or growing. Then watch your TSL per occasion and your number of occasions. At some point this will become second nature and make perfect sense. Hang on.
Observation #3. I have been asked by a handful of programmers about the merits of marrying the minute-by-minute data in the Analysis tool with a music log to determine what songs are causing tuneout. And the question is . . . should you be doing that? No!
More emphatically stated, NO! Don’t go there. It sure is tempting isn’t it? “Let’s see, the song started here and shortly afterwards I lost 75% of my meters. I’ll never play <<insert your top testing record name here>> again.”
PPM was developed to create a quarter hour measurement. It was never, as configured today, intended for use as a music tester. When you think about the essentials for music testing . . . familiarity, fit / expectation, burn compatibility / cluster analysis etc., you quickly realize you have none of the components present in PPM. Ignoring this would set back music research and testing to pre-Marconi days.
But, even if you wanted to push this a little and say: “Yeah, forget the technicalities, people tuned away – doesn’t that matter?” Sure it matters, but can you confidently identify the reason they tune-out or tuned-away? Was it because of lifestyle – they got to work and were now hearing music from a peer’s radio? Were they listening to a station in a friend’s car – and got out?
Until you can substantiate why someone tuned away, there’s no basis for assuming they tuned away because they didn’t like the song.
Now there are technical considerations also. PPM has very heavily researched and well thought out business and edit roles. And these rules are used to determine who gets credit and how much listening credit they get. It works, kinda’ like this; if a PPM encoded signal is detected, at 30 seconds past the minute mark, edit rules kick in to determine which minute/minutes will get the credit. Bottom line, if you have a song which starts at 10:30:30 . . . and someone starts listening to your station at 10:30:30 . . . but PPM edit / business rules are such that they’ll round the occasion and report that listening started at 10:30:00 . . . you’d be basing your music testing off of phantom listening and make it look like the previous song had a larger audience than it really did.
Of course for an Average Quarter Hour Persons or currency type measurement, this is not an issue. But missteps here could result in a new and undesirable format “All the Non-Hits . . . All the Time!”
Realistically, you wouldn’t leave the house without first looking in the mirror – you gotta’ present yourself well – correct? Why try and use your on-air to determine whether or not you’re playing the right music.
So, in closing, a couple of things: Information, which is important to Programmers, is flowing from the PPM well daily. And this information will be helpful to programmers in markets which will remain Diary based for the foreseeable future. Hang with us. We’ve got the info which will help you make the most of your programming skills.
Looking forward to the next time . . .
Gary Marince is vice president of Programming Services for Arbitron Inc. He can be reached at gary.marince@arbitron.com.
