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Channel: Sara Devine – BKM TECH
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Building a little data capture into our admissions process

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As I mentioned in my previous post about mapping our digital landscape, we’re not letting the lack of CRM completely get us down. We have been trying to find creative ways to gather data with the systems we currently use. For years we have asked for visitor zip codes as part of the admissions transaction since we need to report those numbers to our city funders. We recently started to wonder if we could get just a bit more info at the front desk. In July we launched a simple test using our point-of-sale system (Siriusware) to gather the answer to a single-question survey: What is your reason for visiting? The answer to this basic question would be extremely helpful as we plan for future exhibitions, forecast revenue, and think about how to market ourselves. 

We began with a very short list of options in a drop down menu that included the special exhibitions, a few specific collection areas, and the collection more generally. We quickly found the need to add a few more options. For example, the admissions team asked for a “just in the neighborhood” option as it’s a common response to the question (though the data shows it’s not as common as they likely felt it was).

The survey appears in a pop-up window and has a drop-down menu of options. Unfortunately, the option to skip or cancel is bakedin; we can’t make this a required question to complete the transaction.

The survey appears in a pop-up window and has a drop-down menu of options. Unfortunately, the option to skip or cancel is baked-in; we can’t make this a required question to complete the transaction.

Results for the first two months are interesting. In July, the permanent collection ranked highest in response rate, while for August it was our Pierre Cardin special exhibition. The initial lack of options is one of the reasons for the high “other” response rate in July, which dipped the second month as more options were added. Currently, we have 16 options plus skip/decline. This feels like a lot, but maybe it’s ok. In particular, I wonder about including Korean art and African art in the list at the moment since both are temporarily off-view, but it would help us track an uptick once those collections are on view once more. We also have to remember to update the list regularly as special exhibitions open and close. For example, both Liz Johnson Artur and One: Egúngún exhibitions closed mid-August, which explains the dip in responses.

reason for visiting chartA quick comparison of the total number of survey responses (which should be every transaction) to total number of visitors who were required to visit the admissions desk shows the transaction count is about 60-65% of the visitor count. Multiple tickets can be purchased through a single transaction—and we know most of our visitors come in pairs or groups—and that feels close to the right percentage. I think we are still getting more cancellations than we should and we’ll keep working on it. The admissions team is meant to pose the question in a casual and conversational manner so it doesn’t feel like a survey (or an interrogation!) and select a response in order to proceed with the sale, although it is possible to cancel and move on. To avoid cancellation, we included a skip/decline option. Unfortunately, not everyone is consistently asking the survey question, which we know because we can run reports on who is logging which responses. For example, we found one person mostly just cancelled the survey in the first week and were able to speak with them. While we don’t want survey completion rate to become punitive, we do want to encourage completion because the information is important for us as an institution. Finding that balance can be tricky.

After two months, we are still working out the kinks, mostly in terms of making this process habit for the admissions team. A next step is to work with our Tech team to create a report that would knit together the survey answer, ticket info, and zip code from each transaction so we can compare the data set as a whole. That would be a pretty powerful triumvirate. 


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