Data lessons from a successful Taylor Swift exhibit

The Museum of Arts and Design makes data-driven decisions using visitor analytics from Dexibit.

Data lessons from a successful Taylor Swift exhibit | DeviceDaily.com

Originally set to close on September 4, “Taylor Swift: Storyteller,” an exhibit at Manhattan’s Museum of Arts and Design, was recently extended through March 24, 2024. A no-brainer, right? It’s Taylor Swift.

In fact, this decision, like many other strategic choices made by MAD (as the museum is known) was data-driven; driven, in fact, by data aggregated and interpreted by visitor analytics platform Dexibit.

“The show came to us in 2023,” said Wendi Parson, deputy director for communications and marketing at the museum. “Usually we plan our exhibitions well in advance but this one kind of dropped in our lap. We had two months from the time the project was a go to the time we opened. It was a gamble. There’s no question that Taylor Swift is one of the biggest pop artists in the world, but would her audience come to us?”

Luring the Swifties

Using Dexibit helped MAD understand the scale of visitor response to “Storyteller.” “Early on we recognized that about 75% to 80% of visitors to MAD were coming for that show,” said Parson. “We scaled up our operations quite a bit to accommodate the show; we staffed up to accommodate the visitors and move them through the space. In the early days we did see huge numbers (for us).”

She offered some reporting from Dexibit. “We opened in May 2023 and through September 24, we’ve seen a 218% increase in our audience, year over year, and a 600% increase in revenue. That told us our pricing was okay. We also saw a lot of new people, first-time visitors, getting an introduction to MAD. Now they know us and also got to see our core types of artists and art works that we present. We were thrilled to see people engaging with that content too.”

A ticket that includes admission to the Swift show as well as general admission is priced a little higher than general admission only. “We witnessed visitors who were primarily there to see Swift drift into the permanent collection gallery too,” Parson said. By collecting email addresses on pre-purchased tickets, MAD has been able to continue outreach to new visitors.

“The data really helped us to measure the impact of Swift on our operations and revenue streams,” said Parson. “The store got a big bump in revenue and sold merchandise they were unaccustomed to selling like wearables.”

Insights for visitor attractions

Based in Auckland, New Zealand, with an office in Washington D.C., Dexibit works with many cultural institutions in New York City and throughout the United States. But it’s not just restricted to the arts space. “Our customers are both cultural institutions like museums, galleries, zoos, aquariums, parks, libraries, etc.,” said CEO Angie Judge, “plus commercial attractions like theme parks, stadiums, ski resorts, location-based experiences and family entertainment centers.”

What these organizations have in common is the complexity of their business models, she explained. “They sell tickets to entry, potentially exhibitions, events, other experiences like tours. They have memberships or season passes. They run education programs, do venue rentals, hire out equipment. They’ve got shops, cafes, sometimes even hotels, often with third-party partners involved.”

Multiple locations, of course, increase the scale of these challenges, especially if the locations are international. “For those with multiple locations — they’ve then got this problem at scale, often in different geographies or even parts of the world. “From a data perspective,” said Judge, “it means lots of siloed systems where data can get trapped. Dexibit helps pull all these data points on the visitor experience together, to democratize data across their teams and to find hidden insights.”

 

MAD’s Dexibit journey

Parson had been familiar with Dexibit from her time at the Cooper-Hewitt Smithsonian Design Museum prior to coming to MAD in 2018. During her tenure there, they did not procure Dexibit’s services: “I immediately thought of them when I came to MAD. I brought it on right away.”

She listed the touchpoints that inform the visitor experience at MAD: “The touchpoints include how we reach them, how we get them in the door and how we keep them in our community. Dexibit is integral to that. We are a small, lean team; a nonprofit with limited resources. Having Dexibit’s data at your fingertips is invaluable. It helps us make informed decisions, track our progress against goals, report where we are at any given moment compared with last week, month or year. It makes it very easy to understand the data and leverage it to improve our outcomes and performance.”

There are a range of data streams to be tracked, not limited to social media channels, web traffic and admissions. “The data from our ticketing system rolls into Dexibit so we can tell a lot about our performance, who’s coming, which tickets are booked further out,” said Parson, “as well as other streams like our store revenue; that’s a different POS system and also rolls into Dexibit.”

MAD uses Dexibit data across departments, for fund-raising purposes, for example, as well as revenue forecasting. “We can look at all the revenue streams on one data visualization,” said Parson. “We can see if those streams correlate or not, which has been fascinating.”

Parson finds the forecasting capability invaluable especially as the museum, not surprisingly, has no data analysts on staff. “When we were in the midst of the pandemic and all the major revenue streams were decimated, it was fascinating to have tools from Dexibit that helped the sector, including ourselves, plan for the unknown.”

 

Arts, design and…data?

One assumption Dexibit challenges is that crowded attractions are successful attractions. “Visitor attractions love being busy — but often it can degrade the visitor experience. Visitors get frustrated by crowds. Time spent waiting in queues means less time eating in the cafe. Sentiment declines and complaints increase, especially if it’s a school holiday and there’s lots of kids and noise!” Detecting and addressing issues like these requires an attraction to connect the dots across a series of data streams.

That’s a new discipline for many cultural attractions. “One of the things that attracted me to Dexibit,” said Parson, “was that the visualizations are clear and easy to read. I don’t fancy myself a numbers person; I like words, I don’t do data. But they make the data approachable for a lot of us who can’t intuit what it means. They have an easy way of communicating very complex information.”

Insights are essential for an organization like MAD. Like most museums and galleries it now fulfils a role as an entertainment and not just an educational attraction. That means it’s competing for people’s time against restaurants, theaters and a whole range of leisure activities.

“Understanding a lot of things about our audiences helps us make little gains along the way in order to reach the goals we’re trying to reach.”


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About the author

Kim Davis is the Editorial Director of MarTech. Born in London, but a New Yorker for over two decades, Kim started covering enterprise software ten years ago. His experience encompasses SaaS for the enterprise, digital- ad data-driven urban planning, and applications of SaaS, digital technology, and data in the marketing space. He first wrote about marketing technology as editor of Haymarket’s The Hub, a dedicated marketing tech website, which subsequently became a channel on the established direct marketing brand DMN. Kim joined DMN proper in 2016, as a senior editor, becoming Executive Editor, then Editor-in-Chief a position he held until January 2020. Prior to working in tech journalism, Kim was Associate Editor at a New York Times hyper-local news site, The Local: East Village, and has previously worked as an editor of an academic publication, and as a music journalist. He has written hundreds of New York restaurant reviews for a personal blog, and has been an occasional guest contributor to Eater.

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