For music test samples, the old adage, “narrow focus yields broad results,” holds true when administered wisely. The sample sizes typically used for music testing mean there isn’t sufficient sample to populate reliable sub-samples for more than two or three demo breakouts. In most cases, an age range of 10 to 15 years is ideal.
Single-gender samples make sense when the targeted AQH composition of the station leans more than 60% toward one gender. Even at the 60% mark, there may be good arguments for having a single cell of the non-dominant gender, perhaps 25%-33% of the sample. The non-dominant gender shouldn’t be able to “vote” songs on to the station, but the additional information allows you to better manage songs not favored by that gender.
100% cume is usually a good specification for a sample since music testing is a TSL tool (and you can’t impact TSL of people who don’t listen to your station). If your station is new or doesn’t have enough cume yet, you may want to reach outside your cume for some of the respondents. In these cases, using montages to identify the people you want to include makes great sense.
If montage screening is employed with on-going music testing, like NuVoodoo OMR (Online Music Research) or its telephone counterpart, “callout,” those montages need to be updated regularly to ensure that they remain in-step with the desired programming archetype. Using at least two functionally-identical montages (but with no titles in common) randomized for respondents is a good practice.
If your station is healthy, you’re wise to specify 40%-60% of your sample as your station’s P1’s, but it’s important to have a reliable breakout of “outer cumers” (those P2’s and P3’s you’re hoping will become P1’s in the future). You can use cume duplication numbers to estimate the level of competing-station P1’s in your cume.
Cume duplication showing that 40% of your cume listens to one of your competitors does not mean you should expect to find that 40% of your cume is P1 to the competitor. As a rule of thumb, among shared cumes about one quarter is P1 to one station, another quarter is P1 to the other station and the remainder is splattered around as P1’s to many other stations. If WZZZ shares 40% of WAAA’s cume, for example, it’s reasonable to guess that one quarter of that 40% (which comes out to 10%) is P1 to WZZZ.
You can bend the levels of competitive P1’s in once or twice-a-year library tests and over-represent the P1’s of these key competitors. To reduce the problem of “over-fishing” these P1’s in on-going weekly music testing, you may need to reach outside your cume to allow in a few competitor P1’s who don’t cume your station.
Music testing isn’t about getting high test scores. It’s about using the available science to artfully construct a playlist and manage music scheduling to use one programming stream to keep the widest-possible portion of listeners satisfied.