This post, which is cross-posted to the AMT Lab blog, is the second in a series of blog posts sharing success stories and best practices to highlight the benefits of effective data management. Find the first post here.
Data isn’t about numbers. It’s about people. When analyzed, data tells stories about people and their actions. Right now, in your database, a story exists about the decisions that people in your organization make. And, a story exists for every patron, which chronicles their relationship with your organization.
Having all those stories recorded in your database means that you don’t have to guess at what patrons are doing, or the impact that your decisions have made. TRG started as a consulting firm committed to building sustainable patron revenue for arts and cultural institutions. In order to get results for our clients, we found that we had to stop guessing at the right strategies and start using data to drive our counsel, which was a novel concept back in the ‘90’s.
In order to tell an accurate and truthful story, the data that you have must be complete and clean. At the organizational level, you may find it challenging to collect, manage, and effectively apply transactional data. Within the past twelve months we’ve found ourselves in conversations with the Cultural Data Project, the National Endowment for the Arts, the National Center for Arts Research, and a host of other research and CRM vendors who perform data analytics services. In our conversations all parties acknowledged that, while challenges exist, effective data management is both achievable and is rising in organizational value.
Let’s examine why data management is critical to arts organizations, and how to build a culture around it.
Where it’s at: decision-ready data
In 2008, Nate Silver proved he could predict Electoral College votes with near perfect accuracy using predictive models that were driven by large diverse data sets. Silver’s prediction signaled to the larger world what many tech insiders had known for years—that data’s role in guiding decisions has been vastly underestimated.
As words like “big data” and “predictive modelling” entered the public lexicon, for-profit and nonprofit organizations of all sizes started changing their approach and expectations for data. Data management is not necessarily any sexier for Hewlett-Packard than it is for the Chicago Symphony Orchestra, but organizations now recognize the need to invest in people, training, and systems in pursuit of dependable information. Increasingly, responsibility for data management has risen to C-level management because executives now recognize that cross-functional alignment around data is critical. Recently, the White House appointed its first-ever Chief Data Scientist.
At the same time, awareness of the possibilities that data management and analytics presents is a double-edged sword. Models of a scale and complexity we can’t imagine are in the near future, and it’s fun to dream about them. At the same time, it’s easy to forget the real purpose of data management: to enable the dependable information you need to run your organization at maximum efficiency.
What sort of information? Information on the people who support your organization—your patrons.
Data management enables patron cultivation.
Recently, Amelia Northrup-Simpson wrote a post on this blog about how relationships with patrons can parallel dating behavior. Upgrading patrons through increasingly loyal behaviors (as with development from a first date to marriage) results in increased lifetime value from that patron.
Data and how it’s managed can help you cultivate patrons in this way—or hinder you.
Let’s consider for a moment how you might identify new single ticket buyers, the first step in a performing arts patron’s evolution. Easy enough, right? Just make a list of people who have purchased a ticket for the first time. But how do you know that they are a first time buyer if you don’t have accurate historical purchasing data?
What about returning subscribers who have not become donors yet? These patrons need a specially targeted offer based on their previous experience with your organization. How will you make this list without complete historical data of both subscription purchase and donations?
In fact, to group patrons into profiles at each stage of their evolution, data identifies the recency, frequency, and the dollar amounts of your patron’s purchases. To be effective, you really need ALL of the transactional data so that you can target your marketing campaigns precisely with just the right message.
Complete and accurate data collection, a well implemented data segmentation strategy, smart list building tools, and reports to inform you of your campaign results are all part of a sound data management practice.
Data management is a culture.
As Steve Loyd wrote in the last post in this blog series, data management begins with a culture that values data-informed decisions. As Michael Kaiser says, “Someone must lead.” Someone must value data management and its pay-off, and lead and align the rest of the organization around those values.
An organization’s culture is defined by the collective behaviors and attitudes that employees of that organization share. It’s the sum of the small and large choices that the members of the team make every day. Those choices are defined and judged in the context of the organization’s values.
Your organization probably already values patrons. What if your organization decided that one of the ways you cared for your patrons would be by caring for the data you have about them? What could come from that? We see things like box office staff asking patrons for their complete contact information. Or, development and marketing combining their donors, subscribers, and single ticket patrons into a central shared database. Or, the organization having the ability to calibrate target marketing based on the patron’s past activity and most likely future path. Or, staff who know how decisions and campaigns will affect certain key performance indicators (KPIs) like single ticket buyer churn. These actions—and many others—add up to a strong culture of data management.
We’ll continue to write about data management on this blog in the coming months on topics like data collection, segmentation strategies, CRM systems, and data management staff roles. Have an idea? If you’d like to hear about something specific in this area, leave a comment below.