Our perspective on database consortiums comes from our management of data networks in 18 U.S. markets.
At TRG, we like to think of ourselves as “ticketing system agnostics”. We work with 18 community data networks across the U.S. and we deal in the gray area between where ticketing systems from different organizations bump up against each other. We’re like Switzerland—the trusted neutral party that helps everyone work together. Our community databases bring disparate groups of patron records together, efficiently handling NCOA and other data hygiene issues, as well as appending demographic data.
Beyond NCOA, pooling the data of the organizations in a community allows you (and everyone else in the community) to see the connections across the community—to discover all the other things your patrons do. Armed with this knowledge, you can more effectively target who you market to and develop patrons from one-time visitors into loyal advocates for your organization.
We run co-ops in a lot of places; in fact, we’re the largest provider of data network services in the U.S. Successful co-ops rest on four basic principles. The power of a community network is built from the ability of each participating organization to exchange and share data and lists using a PERMISSION BASED system. Effective programs are built to grant individual organization control on the use of the data, easy cost-effective access to the network of data, and sound training and professional development to optimize results from using the data network.
All of those requirements let us at TRG do what we do everyday, which is go to battle against conventional wisdom! Here are a few examples of how the collaborative data in our community databases has benefited communities:
In addition to the flagship Opera Company of Philadelphia, Philadelphia boasts six other companies, more than most U.S. cities. Opera America was curious about the behaviors and attitudes of audiences in such an opera-rich city, and hired TRG and Shugoll Research to provide insight.
Now, you’d assume that if a patron living Philadelphia likes opera, they’d attend performances at multiple companies. But you’d be wrong. Only 6% do, which means that 94% of operagoers in Philadelphia have patron records with one company. We thought that was startling.
For that 94%, the churn rate was 50%. In other words, half only go to the opera once and then they leave—and don’t come back. To attend ANY opera company.
However, for the 6% that attends performances at multiple opera companies—only a 15% churn. 15%--that’s equal to mortality and relocation numbers from USPS. This means that those FAR more loyal. If a patron attends multiple performances, odds are, you’ll have them for life.
Therefore, it is in the self-interest of an opera company to publicize other opera companies. Most U.S. opera companies have a short run of performances every few months, which means that it’s unlikely that the companies would be competing for the same patrons on the same date.
That’s what Philadelphia opera companies did, and reaped the benefits of new and loyal patronage.
With Opera America we investigated crossover between opera organizations. We do a similar study—in a “road atlas” type of chart—for our communities showing the cross-over from organization to organization.
Typically when arts marketers request a trade list, they have to guess as to which organizations in town have crossover with their own organization. In some cases they guess right, mostly they guess wrong. With the chart, they can use data rather than guessing about who their audience is or isn’t.
For example, in this community, the Chamber Music organization has the highest crossover rate. We found that across our communities, it is not uncommon to find high cross over surprises – surprised cross over is high, also surprised cross over is low. The data – not instinct – tells the story.
That’s just one example of how you can use data to inform your work, rather than guessing about who your audience is or isn’t. And to that end, our data is now helping you to create standards of excellence against which you could measure yourself. Our co-op members can see if they are doing as well as everyone else in town, in my region, against other organizations your size, etc. What should my conversion rate for single ticket buyers be? What should my renewal rate be?
Conventional arts marketing wisdom decrees that we are only marketing to a small percentage of the population in our vicinity. But in most communities, the data says otherwise—a third to one half of household communities have attended an arts event, donated, become a subscriber, engaged in some way sometime in the last five years.
Not only does that information give you a larger prospect pool, it is beginning to resonate in ways that influence public policy. Arts patron data has far-reaching implications for advocacy efforts. Our database resources have been used to help pass tax initiatives that are friendly to the arts and kill tax initiatives that are unfriendly to the arts. For example, in 2010, Pennsylvania had several key political races and ballot initiative that would negatively affect arts organizations. TRG mined the databases in Pittsburgh and Philadelphia and found highly active voter households who were also arts patrons. Arts advocates in the state were not only able to notify the arts voters of issues and races that could affect the arts, but they also demonstrated to elected officials the preponderance of voter/arts patrons in their districts through maps and reports.
What’s coming next? The cool stuff that’s coming next are predictive models that move from RFM scoring systems to statistical predictive models that allow you to model on the behaviors of a small group of people. For example, one of our clients used to have to talk to 125,000 to find the 600 subscriber households to buy 1200 subscriptions. Using the model, they now mail to 12,500 to find same 600 subscriber households. It’s a 10-fold improvement in lift. We’ve also got our eye on cultural tourism—which has been one of the holy grails in the 30 years I’ve been doing this work. The presumption is cultural tourists go to New York and Santa Fe, but what we are seeing is the proportion of people leaving market to go to arts events outside of your community (and vice versa) is huge. Part of tools we are creating will drop artificial geographic boundaries, creating a national database. What we know from taking periodic snapshots of the data set is that a staggering number of people are coming and going. What’s unknowable today is: which is more important to you as an organization? Who’s coming from the outside into your market or knowing patterns of people based in your community when they go outside of your community?
Consortiums: the Power of Technology & Data Collaboration
THE POWER OF TECHNOLOGY & DATA
INTIX 2012 – San Antonio
Successful Co-op Programs
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