Apple’s latest iTunes update launched a streaming music service called iTunes Radio intended, most likely, to recapture the music-lovers now listening through similar services like Pandora, Spotify, or iHeartRadio, and to reconnect them to iTunes purchases. While the system is a late-comer to the genre, it brings with it some expected elegance from the Apple design cabal.
Among the more compelling features is the slider that lets you ‘tune’ a radio station based on how adventurous you want it to be. Do you want only ‘hits’ from the artist or artists you’ve identified? Do you want ‘variety’ around those selections (b-sides, deep tracks from the same artists, or very similar)? Or do you want ‘discovery’ of new artists and music that might be a few steps away from what you already know?
The slider is a proactive and user-driven version of something we often attempt in audience research — clustering arts consumers by purchasing behavior. For example, WolfBrown’s audience segmentation work with the Major University Presenters identified a 10-segment consumer ticket buying model, including types like Mavericks, Remixers, Networked Students, and Serenity-Seekers. Each wanted something unique from their arts experience, or the cluster of arts experiences they selected over a season.
What if we gave our arts audience a similarly simple and elegant opportunity to self-identify, or just voice some of their interests, preferences, and goals for their participation beyond the single event? Investment advisers spend a lot of time asking their client’s risk position and long-term financial goals. How could we ensure a similar conversation about preferred risk and reward with those we’re serving along their artistic journey?
Thanks for this, Andrew. I think we have a lot to learn about “preference discovery” from online retailers, including Apple, especially in how they allow consumers to “train” their content streams to conform to their aesthetic preferences. On a less automated and more simplistic level, several arts groups, including the Annenberg Center for the Performing Arts, Steppenwolf, and the Philadelphia Orchestra, have made significant attempts in recent years to collect self-reported preference data from audiences and use this data to improve marketing and customer service. The challenge has been two-fold: 1) automating the survey process and integrating the resulting data with ticketing systems; and 2) figuring out how to use the data in day to day marketing efforts. I think the technology will be in place within another year or so to automate and integrate survey data with ticketing data. And then someone will have to figure out how people can share their Pandora channels and iTunes library with arts organizations. But there are still major questions to address in terms of tactical use of self-reported/automatically discovered data on preferences, and whether such data really adds value to the behavioral data (sales history) that arts gruops already have. All but the largest arts groups don’t really have enough product to allow for aggressive filtering except in single ticket campaigns. And in any case, the curatorial instinct of arts groups runs counter to the notion that audiences should be allowed to “train” them in what content to deliver. Once audience members see the benefits of profiling themselves for an arts group, however, I think they will gladly cooperate, unless the NSA keeps spying on people and all trust is lost. What’s so ironic is that the folks over in the development department have been systematically profiling donors for a long time, with excellent results. All of that being said, I think there’s a great need for new and improved approaches to cultivating a sense of discovery and an appetite for risk, and this will undoubtedly require profiling/data collection on some level, if only to establish benchmarks.