One of the toughest tasks when it comes to programming for a performing arts venue is trying to bring new experiences to the community that audiences will attend in large enough numbers to make the effort worthwhile. Sometimes you think something will be a hit and it doesn’t do well. Other times you discover you the artist you thought would only have niche appeal appeals to a pretty significant sized niche.
The artificial intelligence work of a company called Topos may help take some of the guesswork out of this process in the future.
According to a piece they wrote, they plugged in data about musical artist touring from 2008 to present, looked at the characteristics of the communities where the artists sold well and then created a list of places the artists hadn’t performed, but should consider.
For example, these suggestions for Florida Georgia Line.
They are careful to note that this is a work in progress and their algorithm is pretty narrowly focused, but they are optimistic about the potential.
In this article, we’ve constructed a narrow, highly specific view of place, ignoring myriad factors that shape neighborhoods.
Yet even this narrow view reveals much about neighborhoods, from their form (the connected downtown neighborhoods surrounding large arenas) to their milieu (the hipster neighborhoods connected to Bushwick).
We believe this approach starts to demonstrate the potential of understanding location as a set of relationships rather than solely as a set of isolated points or regions to which metrics are ascribed. Many applications of Location Intelligence — from opening a new store to planning a trip, launching a political campaign to arranging a tour — are ultimately about relationships: Brand and customer, traveller and a foreign culture, politician and constituent, touring musician and fan. Understanding the manifold ways one place is similar to another provides rich context for expanding these relationships into new territories.
Once the calculations have been further refined and test for larger tours, it may be awhile before the use of tools like these become viable for use by many arts organizations.
While I think most of us would be reluctant to leave all our decisions to a calculation, this work provides the opportunity to understand our communities better.
What I would be most eager to see is if these tools could help bring about the diversity in programming we all say we aspire to. A list of suggested artists backed by some proven data provides the opportunity to transcend what we and our boards think we know will sell in our community.
Of course, using a list in this manner would likely need to be accompanied by a sincere commitment to communication and trust building with a broader range of the community. It would be far too easy to discredit the list of suggestions by changing the programming but promoting and communicating about it in the same old way.
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