In my previous post, I undertook the task of using the strategies Rohit Bhargava introduced in his book Likeonomics to establish a unique program that would help make my host institution, the Philadelphia Museum of Art, become a more trusted institution. My program of choice was the establishment of a “Behind-the-Scenes” tour which will take place every second Tuesday of the month. In particular, the audience that the museum will most heavily focus on in regards to these tours will be Baby-Boomers and Millennials, both members and non-members. The ultimate goal of this program is to increase museum membership, and with many Baby-Boomers already being familiar with the museum, the major focus then becomes the efforts to expand the existing Millennial audience. The question, was how could I prove the Millennial audience was the right demographic to focus on?
A few weeks ago, our Issue’s In Museums class had the awesome opportunity of sitting down and having a first hand tour with the creators of a GIS program called PolicyMap. In short, a geographic information system (GIS) lets us visualize, question, analyze, interpret, and understand data to reveal relationships, patterns, and trends. In our guided tour of PolicyMap and brief website navigation overview, we experienced the programs ability to show us current demographic data, income trends, and even public transportation routes through urban mapping. If all of these things weren’t cool enough, PolicyMap showed us how museum’s could utilize its systems to generate incredibly helpful content. For example, the ability to build your own reports for selected communities, build custom regions, and make your own radius around a specific address. With all of these resources at my finger tips, I was able to make a case as to why the Philadelphia Museum of Art should target Millennials using PolicyMap as evidence.
With a focus on demographics, I wanted to know what Philadelphia’s age range make-up consisted of directly around the museum. In order to see this information, my first step was to focus on two maps showing the demographics of ages 44-54, and 54-64 which covered a majority of the Baby-Boomer age range.