Coleman Insights recently introduced the FACT360 Strategic Music Test. FACT360 is online music testing done right through the latest sampling techniques and data collection capabilities, and includes the same benefits that Coleman Insights has provided through its FACT Strategic Music Tests for more than 20 years. These benefits help radio stations build the most appealing and strategically on-target libraries possible.
In the spirit of our launch of FACT360, we present the fourth in a series of five blogs authored by Coleman Insights executives covering important considerations about music testing and music strategy. This blog is written by vice president Sam Milkman and covers the value of cluster analysis and Compatibility scores in a library test.
The previous entries in this blog series shared a theme of using music testing to help implement music strategies established in strategic research, such as the Plan DeveloperSM and FLIPSM studies used by many Coleman Insights clients. With this entry, I’ll get a little more technical and discuss how the addition of advanced statistical tools to the analysis of a music test can help you keep your station’s sound aligned with your strategy and deliver an appropriate level of cohesiveness. (By cohesiveness I mean that while your station delivers sonic variety, its sound has a suitable level of consistency for its format position.) One of the advanced statistical tools that can help you do this is cluster analysis.
When you have a large sample of listeners evaluating hundreds of songs in a music test you can observe patterns in their responses that reveal a lot about their appetites for specific genres of music. Cluster analysis helps identify these appetites by finding song combinations that objectively represent these appetites. In our FACT360 studies, we not only use cluster analysis to identify the most strategically important appetites that exist within your target audience, we also report Compatibility scores that reveal how correlated the demand for each song you test is with each cluster.
For example, if you are programming a station that—based on your strategic research—focuses on Contemporary Pop and Contemporary Rhythmic Pop as its core sounds, you may occasionally struggle with the selection of Contemporary Pop Rock titles to play. Beyond just looking at how popular these songs are with your target listeners, scores that tell you how compatible they are with the Contemporary Pop and Contemporary Rhythmic Pop clusters that exist in your research can help you make the best selections. Thus, you are not only picking Rock-textured songs your listeners like, you are picking Rock-textured songs that do not undermine the cohesiveness of your station’s Pop- and Rhythmic-centered sound.
This type of insight is especially valuable for stations that mix together music styles from different eras. A Country station, for example, may employ a contemporary approach but once or twice an hour features a Gold title from ten, 15 or 20 years ago. Sometimes such Gold titles “stick out like sore thumbs” and undermine the contemporary feel of such stations. Compatibility data can help stations avoid this problem; if two Gold titles are equally popular with your target audience but one is more compatible with the contemporary clusters in your music test, it is almost always going to be a better title to play.
Similarly, Compatibility scores help stations blend music from different eras appropriately. One 80s-centered Urban AC station may find that more 70s titles enjoy high Compatibility with its 80s core than do newer titles, while the opposite could be true for another station in the format. If you program a Classic Rock station trying to evolve and push the envelope into 90s and 00s music after your strategic research suggests you have some latitude to do so, Compatibility scores can help you determine which 90s and 00s songs blend best with your Classic Rock core.
How rigid should you be in applying Compatibility thresholds? There is no standard answer for that. A good general rule is that if your station enjoys strong well-defined images—which is something you can learn from Fit measurement (the subject of our next blog post) you can relax Compatibility standards and play more songs based on their appeal alone. However, if your station is in need of music image development, Compatibility scores can be helpful in avoiding or minimizing the rotation of songs that make it sound less cohesive and potentially undermine your music image development. Conversely, these scores can encourage you to emphasize the songs—through greater exposure and use in on-air imaging—that will help you build the music images that will benefit your station the most.
Not all library tests come with sophisticated tools like cluster analysis and Compatibility scores. If you have such tools and the guidance of a strategic researcher with the experience to help you take best advantage of these techniques, you may be a big step ahead of your competition.
