This article is cross-posted on AMTlab.
Read the first post in this two-part series here.
Last month, I wrote about the overwhelming amount of data produced by the sophisticated database systems now common in the arts industry. My commentary on the “analysis paralysis” that can result caught the attention of many of our readers. We’re glad, because 20 years of consulting work has taught us this: data-driven hard work works.
Data-driven hard work works
It is hard work to develop a loyal, sustainable audience base. There are few shortcuts. However, a focus on the right audience data can guide your efforts. That’s why I urged you in my last post to “stop studying everything.” Then you can minimize distractions and direct your time, energy, and hard work on efforts that will help you achieve your audience goals.
The first three metrics I talked about (revenue from patrons, active patrons, and subscriber-donors) can help you define an audience development strategy built around loyalty. Those are a few of the metrics we look at when we’re first hired as consultants. There are three more that can provide a more complete picture of audience behavior, loyalty, and revenue at your organization.
If you’re ready to dig deeper, let’s take a look at these three, too:
New audience churn rate
Turnover, churn, attrition—whatever you want to call it—it’s a measurement of the patrons you’ve lost. What does your churn rate look like?
How to calculate:
1. Determine the number of new patrons
you had from last season/year.
2. Within that group, find how many had
no transactions this season/year.
3. With those two numbers, calculate:
# of patrons who didn’t come back this year
# of new patrons last year
Look first at the number of patrons who came for the first time during your last season. Usually this can be found in an existing report in your ticketing system, or by querying the date patrons were entered in the system. Then see who in that group had a transaction this year. You can then calculate the churn rate using the formula on the right.
Based on our study of the national data we aggregate and analyze at TRG, as well as a decade of studying Advocate-Buyer-TryerTM analyses for individual organizations, we know churn to be the most important audience development issue faced by our field. In arts and cultural institutions, we over-prospect and under-retain. Period.
Our industry has a new audience problem. Contrary to popular belief, that problem lies not in attracting those new audience members, but getting them to return once they’ve attended once. Our national data shows that in the average arts organization, 4 out of 5 first-timers come once and then never return. Why are we working so hard to prospect new audiences and then just giving up on them?
Part of the reason we hear about: it can take time—sometimes years—to have an impact. In our field, we’re so desperate to make this year, this season, work that we can scarcely turn our attention to the future. Which is why Seattle Repertory Theatre’s case is so exceptional.
In the midst of a budget crisis, Seattle Repertory Theatre launched a “second date” strategy with the goal of retaining new audiences over four years. Their successful strategy, which you can read about here, is now part of the way the organization operates and serves as a model for the field. We’ve been recommending this strategy in nearly every conference presentation we’ve made this year—because data-driven hard work works.
Data capture rate
How to calculate:
Total # of patrons with contact info
Total # of patrons who attended
A data capture rate measures how many patrons for whom you have contact information--email address, street address, and phone number. Calculate this for every patron who attended or had a transaction in the last year or season. You’re shooting for a rate of 80% or more. Why? Your organization can’t invite on another date patrons whose contact information it doesn’t have.
This metric is a measure of quality for your database. You must keep good records of both what patrons bought and how to contact them, especially if you want to do meaningful research on your audience. For example, you can’t calculate an accurate churn rate if your data doesn’t link patrons to their purchases. It’s difficult for ticketing systems to do this without certain pieces of information on the patron.
But there are more practical implications, too. We can’t say it enough: Lost data equals lost revenue. My colleague Amelia Northrup-Simpson illustrated this in a recent blog post. Every time a new patron walks out your door without giving you their contact information, you’ve lost the ability to invite them back. Because of this, a low data capture rate can correlate with a high new audience churn rate.
Per capita ticket revenue
How to calculate
per capita ticket revenue:
Total ticket sales revenue
Total ticket unit sales
Regardless of whether you’re capturing the contact information of every single patron in your database, there is one metric you can study for every household in your audience. And that’s per-capita ticket revenue.
Per capita revenue is simply average price paid by patrons. You can measure “per-caps” for tickets, subscriptions, gifts... any buying behavior you want to track. For this example, let’s look at ticket per-caps.
You’ll find the data you need to calculate this in your ticket revenue reports. Do the math we suggest above for every performance/show/exhibit in your season. Put it on a graph with the overall sales—this represents demand. Sort the data based on demand, in ascending or descending order. (See example below.)
This metric will tell you a lot (in fact, we wrote an entire post about it), but the main takeaway is whether your pricing is in line with demand.
Some background: at TRG, we believe strongly that in our field of non-profit, mission-driven organizations, we have an opportunity to present experiences that challenge and that are new to our communities, however we define that. Those experiences don’t always garner high demand. It doesn’t matter; that’s less the point. But when we do we present experiences that are likely to be in demand, we believe we have an obligation to ensure our organizations benefit. In-demand experiences—blockbusters—can fund and fuel the mission. And if your organization has the capacity to manage this proactively, this is an evergreen strategy that helps ensure you have sustainable patron income.
In your organization, does this happen? Do in-demand experiences buoy the important mission-driven (but sometimes less in-demand) experiences?
At TRG, we typically see what’s described in this chart above. On a per-show/exhibit basis (indicated by the red bars), as demand grows the average price paid by patrons (indicated by the black line) declines. Why does this happen? Perhaps pricing plans in our performance venues or for our exhibits aren’t designed to ensure it. Or maybe we discount and distribute comps without taking demand into account.
To tackle this issue, look at the data and ask, “Does per-capita revenue grow with demand?” If not, in our view, you’ve got a problem.
Are you ready to do some data-driven hard work? If you want to impact audience loyalty and revenue, start here:
Consider the variables I’ve described in this series and their impact. Then, select a few metrics (remember, stop studying everything!) that you can work easily into your day-to-day operations for the most impact. Become obsessed about understanding what’s driving those numbers in your organization.
Next, recruit your peers across departments to understand these numbers with you. You’re all in this patron revenue game together, and together you can derive solutions that work.
Finally, make an action plan for each metric and start working. Know that the hardest part will be staying committed to your plan. Arts managers must constantly quell the fear of missing out (FOMO) on the latest shiny new technology or cool audience research metric. Resist. Instead, track the data as you go and celebrate your successes.
Following your plan, tracking the data, and doing the hard work—that’s what gets results. Remember: data-driven hard work works.
What metrics are you currently tracking and working hard to improve? Comment below.