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andreajesus
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In the October-November-December book average, KRTH had 25,300 AQH listeners in 25-54. In September, it was 17,900 persons. That is roughly 30% lower now than at the end of 2019.
Maybe people are tiring of over-researched songs played in high rotations and have burned out on them? Nah, couldn't be that - that is simply crazy talk. Who's up for some more Bon Jovi?
Or maybe people have still not completely returned to normal.
That is not it. Based on actual people listening at the average "moment" during the day, listening to all stations together is considerably lower than it was before the virus.
LA average listeners all week Oct-Nov-Dec 2019 was 410,000. In September, 2020 it was 283,000. That is 31%, just a tiny bit more than what KRTH lost. So actually, compared to the total market, KRTH did a couple of percent better.
P.S. There is no such thing as "overly researched". All "research" does is ask people how much they would like (or dislike) hearing the song on the radio today. The only reason stations do not do that more often is because it is very expensive.
So why did you invent the oxymoronic term of "overly researched"?
A better idea. Forget the research (or tone it way down), save all that precious money and give it to your employees as a pay raise or bonus and just play the music.
Want some "Bad Medicine"?
People tire about everything, it human nature. No one is ever pleased, that's why you change things up, add and delete, not play the same dang song 57 times in a month!
And then, when you start playing songs that much of the audience dislikes, the ratings go down.
When the ratings go down, revenue decreases because rates are set on audience size.
Revenue goes down, and then you have to lay off staff, or change format.
You didn't take any marketing courses in college, did you?
Program managers meeting with the consultants at the station:
"Well let's see, "Brown-Eyed Girl" was the best testing song in our test last time and was in the top 5 songs on the last several tests so we will need to include it again in this next test coming up. And don't forget "My Girl". Who doesn't love "My Girl"? Apparently everyone loves it, because it always scores high too, but we gotta think about demos. Sixties are so out of demo now. So we will phase out the Temptations, and we will go hard into the seventies. Need to figure out which 70's songs everyone loves. Get "Hotel California" everyone loves that one, because it is all about California, and there is nothing more California than LA right? We would do "Stairway to Heaven", but the tests say it is not a hit with women. The women like ABBA. Get more ABBA songs into the next test...In the meantime I want everything in the last test's top 10 spun at least once every four hours until the next blue moon. "
This doesn't really answer your question, but it does make my point. I say let's live it up at the Hotel again! Warm smell of colitas rising in the air...
Have there been examples in major markets of successful stations (ratings-wise) that were somewhat free-form or at least had a very wide playlist? Thinking at the advent of FM/AOR radio in the '70s, some stations somewhere may have had success. But, maybe 'success' for those stations was sneaking into the Top 20.
They did after they tightened the playlist. That was the case for Tom Donohue's KSAN and Scott Muni's WNEW.
There was no pressure to succeed in the early 70s. But that changed as the decade continued, so they hired consultants, and playlists became more scientific.
If you want to see how well a "wide playlist" station does, look at KCSN. That's what you can expect.
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Despite the tight playlist, anything above 6.0 indicates the station is doing really well. Let's see how long KRTH can keep it up!
A 6.5 rating is pretty big compared to the Coffey days when the station was in the 3.0's, maybe a 3.3 or 3.2.
Perhaps Mr. Eduardo can help me out here.
OK, but first KRTH does not have a 6.5 rating. It has a 0.3 rating, down from the 0.4 it had in both October and November of 2019.
Agencies buy ratings, not share. That is because "rating" is a percent of everyone, not just those listening to radio on average. That's what share is... percent of those actually listening to the radio.
In 25-54, they have a 0.3 too. Down from a 0.4 in late 2019. Of course, that is better than the April and May 0.2 numbers.
When the PUR is significantly different (Persons Using Radio... a percentage of the population in a group that has the radio on) than in prior periods, you can't compare share.
If you divide two pizzas into 8 pieces, but one pizza is a 12" pie and the other is a 24" pie, each pizza gave 8 pieces. That is share. But the 24" pie is much bigger... the pieces are thus larger, and that is rating. But each pizza is a different universe, and this shows that you can't compare them.
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Hey, they used to be a good station. I never said profitable, I said good.
BTW, whatever happened to the rumor that they were ticketed to oblivion? They seem to be doing well and (I think) their most recent pledge drives have been pretty successful. Just think how successful they could be with a usable signal somewhere besides the CSUN campus and the hills of south OC?
That is so far from how a music test is implemented that it is not even amusing.
First, today, consultants don't manage tests. Group PDs or format managers do. And what they would do is help add any songs that might need testing based on experiences in many other markets. They also might have format conference calls where ideas are exchanged.
Nobody give their personal opinion on a song. It's all about what listeners tell them. If a song is older, they look at the scores from the younger group who was in the test and if it looks healthy, they leave it. If it is weaker, but not negative, they talk about protecting it with young or old codes so they don't play next to other songs that one group likes less than the rest.
They will look at multiple columns of younger, older, men and women, maybe ethnic groups too. They look for the very stong songs everywhere, and those get the most play. Others are coded to protect similar sets of lesser scores from playing together.
Then they look at factor analysis to see if there are songs that are out of bounds for the station, even if they seem to be in-format. Factor analysis also can show sub-groups of listeners that are separated by taste, not age or gender. Those factors also contribute to sound coding, so a music set will have balance and not drive one of the subsets away.
And the test results are filtered so any song well below cut-off in any of the multiple evaluation categories is not even displayed. Those are not even considered.
Picking the list can also be a group effort, with songs that are newer are looked at so that the core does not age. Songs are suggested so that plenty of "what if" tunes can be tested to see if they fit. Songs with "burn" may be tested again to see if they came back after a long rest.
Then the imaging questions are determined... ones that get a feel for listener attitudes about the station, radio, new media, jocks, and even "did you see a TV ad for a radio station recently" and stuff like that.
Of course, the key issue is the recruit. Percent by age and gender. Amount of time they listen to you each week. There are a bunch of other questions asked even before a person is accepted to participate in the test.
Once you find out how many songs passed the test, then you look at rotations to make the set work, with the biggest songs with no blemishes by gender, ethnicity, age or "cluster" getting the highest spins. And of course, artist separation, tempo control and all the rest go into implementation. I have spent all my time for a whole week implementing a test for a gold-based station in LA.
It does not matter if the PD likes the songs. What matters is whether a listener hears a good song every time they tune in.
P.S. Tell me how you'd use cluster analysis and under what circumstances you'd use two clusters, three or even four.
The owner, Cal State, was looking for another organization to manage the station at one time. Several operators were being considered. AFAIK, no decision was made.
https://www.allaccess.com/net-news/...t-csu-northridge-seeks-operator-to-run-kcsn-k
Perhaps COVID got in the way. We'll see what happens when the cloud is lifted.
It is the same 0.4 rating they had in October and November.
"Share" when PUR is not reasonably stable, can't be used for comparisons.
David, your inability to recognize facetious comedy (such as it most humbly is) never ceases to amaze. Obviously I was trying to make a larger point by painting a ridiculously absurd imaginary conversation. That said, it DID inspire you to write what I think is your most informative and enlightening post on the subject of tests and their implementation.
As I have said before, while I may not personally care for the results of the lowest common denominator tests as a listener, I absolutely appreciate the (1) business necessity of them and (2) even more so, the blend of art/science/statistics as they are applied to different markets with different attributes that are specific to them by experienced programmers who know what they are doing. I have said before, if I owned a station for profit, I would hire you or one of your peers to program it to its maximum profitability, and if that means playing the Macarena every hour, then so be it. Of course, at the end of the day, I would be listening to what I want, just like you said you have done.
BTW, the cluster question is one I am interested in - please tell. From my point of view, now you are sharing the good stuff, and I love to learn.
Cluster or Factor analysis is a process that looks for commonalities among non-standard groups. That means using math to see if all your listeners can be divided by factors other than just age and gender. What this is like is perhaps best compared with a museum of art; some will favor modern art, some the impressionists and other the classic styles. They all like museums (your station) but they have a preference for certain subsets. That does not mean that someone who like Picasso does not like Monet or Michelangelo, it just means that they favor one subset.
If you use a math process to find those subsets, then you can better balance your hours and sweeps. In fact, you can find subsets of subsets... liking impressionist landscapes but not portraits. Or liking most impressionists but not pointillists. Knowing this about your music allows for different methods of flow and protections from "two like songs" back to back when the difference is not style or gender but appeal to a specific "cluster" or group.
This is tedious, but just think about "songs" when you read the first paragraph or so. A music test has data on each participant and mathematically we can find similar patterns among other participants. That is a cluster. It can be big (worth programming to) or a little subset (to be ignored). And it mathematically explains that there are similar groups of people who have very different taste.
It can get very complicated... in alternative rock, the clusters are very polarized to the extent that you say, "can I get them all to listen to the same station?" and that is the challenge. Cluster analysis makes sure you appeal to the strongest subsets and allows balancing so you don't piss off one of them in every sweep!
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