An Audience Building Roundtable Blog by Greg Burbidge

Kari Mesropov reminded us in the July blog post that TRG Arts’ data shows “50% of audiences are brand new. That’s right: half of your customer base is new.”  As organizations struggle to build audiences, this is no time to stay at home and wait for Balki Bartokomous’ to show up on our doorstep. We need to find innovative ways to find and invite interested non-attendees, Perfect Strangers, through our doors.

Whether or not it’s easy, organizations need to be attentive to how they attract the 50% of new attendees to maximize the likelihood that these attendees will turn into loyal patrons. Is finding interested non-attendees easy?

In Building Your Audience: Where Data Fits in the Process, Part I, Brad Pilcher laid the groundwork for our path when writing that all audience building “requires data, very specific data, and a great deal of it.” We’re going to take a look now at some super macro data resources that will point us in the direction of finding some familiar and unfamiliar audiences.
 
(If you need a refresher on the types of data check out Brad Pilcher’s great overview in the blog Building Your Audience: Where Data Fits in the Process, Part II.)
 
More of the Same
The first super macro data source we will tackle will be the ESRI Tapestry tool. This tool will help us define what our current audiences look like, and then find more of the same.
 
Even organizations with minimal data on their existing audiences likely have enough to get started on some cursory market segmentation. All we need are zip codes from those who are already taking in our programming. 
 
To help demystify the process we can take a look at my newest side hustle – my bake sale stand.

Step 1: Whatever ticket sales data you might be keeping, dump it out of that shoebox and on to your desk.
 
Step 2: Once you’ve sorted that shoebox out, or opened that excel sheet, count up how many people from your last production/event/concert/bake-sale came from each zip code. If you’re working in excel, you can get fancy and use a pivot table to count how many sales came from each zip code, or you could do a sort using the zip code column. Scan your list to quickly identify the top 3-4 reoccurring zip codes. 
 
For my last bake sale most of my sales came from zip codes 30307, 30033, and 30030.
 
Step 3: Let’s head over to ESRI, a market segmentation tool. Scroll down until you see the map. If I start punching in the zip codes into the free (FREE!) Explore Your Neighborhood tool you can see the most commonly occurring market segmentation types within each zip code. For my sales I can see that Urban Chic and Metro Renters profiles pop up in all of the zip codes that I am pulling from.

Step 4: I can then go over to the segmentation reports found here and scroll down the right hand side of the screen until I see the reports labeled Urban Chic and Metro Renters. My Urban Chic audiences like to “Embrace city life by visiting museums, art galleries, and movie theaters for a night out.” Good news for me!
 
But this is information on the people coming to my bake sale already. How do I find people that don’t even know I can make them delicious cookies and other baked goods? Now that I know a bit about my audience profile, it’s time to find new people that fit that same market profile. Let’s find more of the same audience.
 
Step 5: Now is where I head over to an ESRI segmentation map and zoom in to look at Metro Atlanta.

I see that I am currently drawing audiences from a lot of L3 shade of blue areas and I can look around the region at the geographies that have a similar market segmentation profile that are not coming to my bake sale.  Looks like Grove Park, South Smyrna and North Marietta might be interested in some baked goods!

This is just a free market segmentation tool, so now I have to go knocking on doors in some of those neighborhoods myself, find some community partners or buy some local advertising. The tool did help me look at my own audience and then think about where in the region I might find some similar audience that I’m not currently serving.

Homogenous.
Good for Milk, Not for Audiences.

My concern about finding more of the same is that this leads of a homogenous audience. Using data to find more of the same might build audiences and increase ticket sales, but how do we find audiences that aren’t like ours that might be interested non-attendees.
 
The work Magda Martinez wrote about in February digs deeper into the benefits of developing these strangers into audiences. Magda reminds us that Fleisher Art Memorial “chose to “diversify” and we found that, by diversifying our audience, we also learned more about broadening and deepening our audience relationships.”
 
This is where data sources come full circle almost exactly a year later. The Audience Building Roundtable group came together to first hear from the authors of the NEA report When the Going Gets Tough at the Art of Change: Building Your Organization for Audiences workshop in November 2015. Data from this report suggests that some of the richest opportunity to grow audiences comes in some of the places that are least like our existing audiences.
 
For this report surveys were done to explore who is, and is not, attending arts and culture events. One of the ways the NEA authors segmented those being surveyed was by educational attainment:

By far the largest audiences are coming from those with Bachelor’s Degrees and Graduate Degrees. Those are the people already in the room.
 
In building new audiences the yellow bar is what could be most promising for us. This represented those surveyed that were interested in attending the arts, but for some reason did not. By far the largest sections of interested non-attendees were those that did not complete college. Those with Some College or who only completed a High School Diploma were the ones with the highest rates of interest in attending the arts but for some reason did not.
 
The same study surveyed individuals and asked them to self identify by social class.

Those who self identify as middle class and upper class were the most likely to already be attending programming. The opportunity gap lies with those that identify in lower class and working class, where 18% and 17% of those surveyed were interested non-attendees. The upper class had only 2% respond that they were interested non-attendees.
 
These interested non-attendees are the ones who self report that they are interested in attending the programming we are providing, but face barriers they are not overcoming. The NEA study digs deeper into potential reasons that you can explore in the full report.
 
This report is another example of super macro data. This is cumulative data and does not represent one organization’s audience. It will take more work than what we did with our ESZI data to develop this new audience, but the super macro data suggests there is a rich trove of Perfect Strangers to be found here.

Finding Prefect Strangers won’t be easy, but they make up half of existing audiences. Finding ones that will stay with us and build a new community of patrons will be what leads us to the Dance of Joy.
 

Keywords: "interested non-attendees",  ESRI, ESRI Tapestry tool, NEA, NEA Reports, homogeneous audience, diversify