TRG blog: Arts data collection in crisis?

Arts data collection in crisis?

Image by r2hox from data.path by Ryoji Ikeda
Image by r2hox via flickr
under CC BY-SA 2.0

A recent post by Createquity has raised great questions in response to the report published last year by the Cultural Data Project. Chief among them: “What would consistently effective use of data for decision-making at the organizational and system-wide level look like in practice?” Picked up by You’ve Cott Mail, the question became, “Are we ready to declare a crisis around data collection and use in the arts?”

As one of the largest collectors of arts patron data in the U.S., we’re seeing more clients wrestle with just these sorts of questions. The answers are complex, nuanced, and often unique to the organization or agency asking them.

No collection without planned action

The CDP’s President and CEO Beth Tuttle has begun exploring the idea of “decision-driven data collection.” This concept serves as a necessary counterpoint to a big data world where we’re encouraged to collect every data point and see what story emerges.

Research-responsive data collection

“Collect first, ask questions later” is an expensive proposition. Leading with strong research questions and designing the necessary corresponding data collection parameters is more affordable and actionable for all than maintaining an evergreen, research-ready dataset that is capable of answering any question one might possibly ask.

We know this at TRG first-hand.  Maintaining a centralized repository of patron transactional data requires a central system that can receive data from hundreds of donation and ticketing software platforms, standardize data fields into common definitions (e.g. “flex package” means different things to different organizations), and maintain data quality (e.g. tracking when patrons move to a new address, so that purchase behavior can be viewed over time.)

We’ve developed systems to do the work required above, and we use it successfully for specific research projects, like our recent analysis of patron loyalty in Philadelphia commissioned by the Greater Philadelphia Cultural Alliance, a project funded by the William Penn Foundation. We’ve also learned that compelling research questions must drive the data collection in a field that doesn’t see the immediate business return in a research-ready, regularly updated data set.

For organizations: revenue first

Organizations that are considering data use must also ask, “What are we trying to learn?  To what end?”  Our point of view at TRG is that the first data points that an organization must collect are those that will help it grow revenue. Revenue must be considered first, not because it is more important than the mission, but because it fuels the mission. Infused with a strong base of working capital built on a loyal base of patrons, an arts organization can afford to take artistic risk, raise its production values, expand its marketing reach, or spend staff time and resources to meaningfully diversify its audiences.

What does decision-driven data collection look like?


Image by r2hox via flickr
under CC BY-SA 2.0

With the idea of “decision-driven data collection” in mind, let’s return to Createquity’s question. What would it look like if organizations and communities consistently and effectively utilized data? And what can we do to get there?

Data collection at the organization level

Which data do organizations need to collect to build positive working capital and thriving business models? Let’s consider the following questions about effectively collecting and using data:

  • Which data should organizations collect?
  • How can we collect usable data?
  • What types of decisions can this data help with?

Which data should organizations collect?

The single most important kind of data for arts organizations to capture is patron contact information and transactional data. By this, I mean capturing a record not only of who bought or participated and their personal contact information (including telephone number, street address and email), but also a record of exactly what they purchased.  Why? Patrons vote with their dollars. A patron who has a positive artistic experience, is cultivated well, and has money to spend on entertainment will return again and again.

Is there room for survey data here? Absolutely. But, patron transactional data must be the foundation of audience development and loyalty programs. TRG’s consulting experience indicates that a patron’s past transactional history is a better indicator of their future possible relationship with your organization than what they say they want or say they’ll buy. When you’ve tracked a patron’s transactions and have their contact information, you’ll have the data you need for campaigns that make them feel known—and ready to take the next step with your organization.

How can we collect usable data?

Many organizations know that they need to collect patron transactional information, but the devil is in the details of actually recording those details accurately in CRM and other point-of-sale systems. If arts data collection is in crisis, as the title of You’ve Cott Mail’s summary suggests, this is one of the main reasons. Data collected here also enables good research for wider community studies.

The good news: Excellent data capture is possible at all levels—from multi-million dollar institutions to small community organizations.

The true, “bad” news: It takes hard work, and a commitment to investing in infrastructure.  How do we collect it?  One patron at a time, at each contact point.  Online systems make it easier—we can require the collection before a patron can move past a particular phase in the information or purchase process.  But as a field we must also be committed to closing operational gaps in collections.  Ask yourself: where does my organization have “invisible” patrons?  And then, work to create systems and incentives for staff to collect the data at these points to make those patrons visible through data.

Whether it’s a large museum making a commitment to capture contact information for those elusive walk-in visitors, or a small organization training its volunteers on creative ways to get the data for everyone who walks through the door (ideas here), arts organizations need to invest in systems and processes that capture info from every patron at every transaction.

What types of decisions can this data help with?

Data, even big data, is not magic. But, transactional data can transform your entire business—from how you scale your hall and price your tickets, to projecting revenue goals, to allocating budget and resources. The biggest and most important impact that data can have: helping you send the right message to the right patron at the right time. Do this and you’re no longer marketing arts events; you’re building relationships.

Building relationships is critical to every arts and cultural organization’s business model. Any marketer who’s ever built an acquisition campaign will tell you that new patrons are extremely expensive to acquire. The more loyal a patron becomes, the more they invest with your organization—and the less you have to spend to bring them back through the door.

Once the box office procedures are in place to capture patron data, marketing and development professionals can be trained to make use of the powerful repository that has been built. By segmenting the data with the end goals in mind, they’ll get the right message to the right patron. No more subscription offers to first-time single ticket buyers—just ask them back a second time. No more donation offers to first time subscribers—just get them to renew the first year. Decisions about whom to target and how to allocate resources become easier when retention and loyalty growth are priorities, informed by data.

Transactional data can also act as a canary in the coal mine, giving you advance warning of impending trouble. Subscribers in decline? Before declaring the subscription model to be dead (and avoiding a tough look inward), be sure that your multi-buyer pool—the very best prospect pool for new subscribers—has been growing over time and that you’ve analyzed and can replicate the behavior of those who have upgraded.

Data collection and research for improved results.  That should be our mantra as a field.

Data collection for multi-organization research and analysis

Data collected with a desire to study the behavior of multiple organizations or communities should depend on what questions need to be answered. It will vary over time and by market, as well as by the agency or organizations asking the questions.

Data collection for behavioral research at this level requires extraordinary care.  And the research should deliver actionable insights.

Productive scrutiny of arts research methods and findings has increased in recent years, but in the case of behavioral research, more emphasis must be placed on whether the findings are based on complete and high quality datasets. This needs a much more honest look, if we are to trust the findings.

We all have a responsibility to be accountable to ensure high quality of data being collected from arts organizations for research.  If data points were dollars, much money would be wasted each year, compiled with the intention of accountability but never turned into actionable insights?  It might just be that the arts data collection crisis could be impacted positively if results were more regularly returned in the form of actionable business intelligence that informs the daily work of arts and cultural organizations.

Conclusion

The data analysis capabilities available today have changed the definition of success and accountability for arts organizations. It’s no longer enough to know whether we’ve hit our attendance and revenue goals. We must know who is coming and whether they’re increasing their investment with us. Leveraging this data into working capital enables more than sustainability: it will enable thriving arts communities with devoted audiences for whom the artistic impact deepens in proportion to their investment.



Posted December 10, 2014
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