Mapping the Data Landscape (Without a CRM)

One of my personal and professional goals for the Visitor Experience and Engagement department is to make more data-driven decisions. We’ve written A LOT about data on this blog, so that will come as no surprise. However, like many museums and cultural institutions, we don’t always have the dedicated resources we’d like to gather data. In my dream scenario, we’d have a small team of people working just on gathering and analyzing data. In this same scenario, much of that data would be found in a unified CRM (customer relationship management system), which we don’t currently have. We operate no less than nine different database programs throughout different museum departments. Nine. And, of course, none of them talk to each other in any sort of automated way. To paint any kind of picture of who is engaging with us onsite and online, we have to do a lot of manual exporting and importing of data. Needless to say, it’s terribly inefficient and rather frustrating. For ticketing alone we use two different systems: Siriusware onsite and Showclix online. I (literally) dream of a unified system and we keep hunting for what I call the “ticketing unicorn” that would serve all our onsite and online needs including integrating with Raiser’s Edge (used by our Membership team) and a cart feature that is smart enough to up-sell and help customers through the purchasing process in a clean and understandable design.

Without a unified CRM, the data landscape can look a little bleak.
Stephen McMillan (American, born 1949). Zabriskie Point, 1976, 1976. Aquatint on paper, sheet: 22 1/8 x 29 3/4 in. (56.2 x 75.6 cm). Brooklyn Museum, Gift of ADI Gallery, 77.152.2. © artist or artist’s estate (Photo: Image courtesy of Stephen McMillan, CUR.77.152.2_StephenMcmillan_photograph.jpg)

Despite our lack of CRM, we’ve been working hard to gather data in ways we can. For example, we started a single question survey as part of our admissions process—What brought you to the Museum today?—that our front-of-house team is meant to ask as part of a casual and welcoming conversation. That, along with the zip codes we gather as part of every onsite transaction (a requirement of city funding is to report on zip codes), helps us begin to get some basic information about who is coming and why.

On top of regular focused data points like zip codes and single question surveys, we will be running an annual visitor study. We recently partnered with SightX for our research needs and have launched a baseline visitor study to get a better idea of visitor museum-going behavior and demographics. We haven’t run such a survey consistently for 3 years or more. I’m really looking forward to having that data ongoing. We’ve tried to be thoughtful in not only what questions we pose, but also how we phrase them.

In addition to our own visitor study efforts, one amazing resource we’ve been able to tap to build our data stores is our relationship with Pratt Institute School of Information and their Museums and Digital Culture graduate program in particular. In addition to our ongoing fellowship and Sydney’s recent work on ASK data, I’m able to work with the students I teach (and other professors’ classes) on projects that both give the students real-world experience and help the Museum—a true win-win. This past semester, I taught Audience Research & Evaluation. Through class work, students ran one evaluation related to the Frida Kahlo exhibition and another on our ASK app. I’ll report on the findings of these studies in a future post.

So data gathering is happening, albeit piecemeal sometimes, and we’re trying to smart about knitting it into processes and moments that make sense such as with the admissions process, or a short, three-question survey with our thank-you emails to online ticket holders. What does our data landscape really look like and what can we do with the data we do have?  I’m happy to say, that’s where a dedicated data analyst comes in. Thanks to Bloomberg Philanthropies’ ongoing support, we are able to contract a data analyst (job description forthcoming) to help us begin to make sense of all these data points, with ASK data as a pivotal element in the data landscape. I’m really looking forward to carving out time to get the big picture of all the ways we gather data and streamlining those moments. If you know of any good people, please send them our way. In the meantime, check back as we share the results of our recent visitor studies.