Alex Garkavenko’s mission is to understand the intricacies of admission data that a front of house team need in a new build museum, or how exhibition managers in an art gallery like to visualize zone activation. A Data Architect with Dexibit, Alex conducts endless observations, interviews and testing to design user experiences through an understanding of the personas that make up the dynamic of a museum team and their big data analytics requirements. It’s a job Alex found herself uniquely qualified for: originally trained in architecture (of the construction kind), Alex spent time in MoMA’s curatorial team, became obsessed with user experience, particularly ‘human-data interaction’, since retraining in computer science.
From Alex’s perspective, her combined experience shaped the view a museum’s analytics approach should be designed around responding to each member of staff, not as ‘one size fits all’. “We all look at data differently”, Alex says, “and data means different things to different people. Driving a team to making insightful decisions – that behavioral change is a very personal one. We perceive each user as an individual, make sure they are comfortable and fit with their needs and workflows.” Her team continuously feed knowledge back into dashboard and reporting templates personalized to specific roles and an endless stream of visualization, insight and interface designs. At Dexibit, Alex has an exclusive opportunity to test what works across a volume of museums. “Generally, we design visualizations to suit three data styles: some people like volumetrics – just the numbers, some people like statistical graphs – a more abstract representation, while others like illustrations – which lets you see the bigger picture, particularly for geospatial data. Being data obsessed, we instrument it all, looking for what works for whom.”
To understand the data requirements of personas within a museum team, Alex seeks groups which represent the bulk of visitor facing data relevant to the museum’s core business model. These key peronas see the biggest return from analytics and as a result, set the pace for the rest of the organization. Key stakeholders include the museum’s executive, finance and operations, technology and digital, marketing and membership, visitor services and front of house. “A Marketing Manager, for example, needs to understand the impact of every advertising dollar, optimize organic versus sponsored engagement and closely track achievement on target for visitors through the door”. Requirements also depend on the museum’s segment. With paid admission, such as in a typical science museum, understanding admission performance levers brings a significant impact. “When you realize even a 1% improvement means a $30,000 annual return in a small museum, the numbers add up quickly.” Lastly, Alex looks for the intensity of the data consumer’s needs. “The leadership team often just want the facts: what are the numbers, are they going up, how do they compare to goal? Operationally, this might be on a right here, right now basis – making on the fly decisions in near real time, especially at front of house. A line manager needs a bit more of the picture: what’s influencing this? What correlates? What do I have control over? Analysts or subject matter experts need the freedom to form their own hypothesis, to test and experiment – often involving an ecosystem of tools.”
“There are certainly some truths evident across all museums,” says Alex. “Looking across industry, there’s commonality. We’re all concerned with visitation, we all want to know the conversion of our outreach efforts, our retention in whichever form. We all forecast, we all set targets, we all want to compare back. And I’m certainly seeing a move towards prioritizing commercial sustainability, though that starts to get business model dependent.”
Even after interviewing many museum professionals, Alex still encounters the odd surprise. “One thing I have come across lately is busy museum executives who just want insight on the pulse of the museum delivered straight to their inbox. No dashboards, no reports, just natural language, addressing their institution’s specific needs. That’s where we see our future headed in museum analytics: it’s all about personalized recommendations.”
In the meantime, for museums commencing analytics projects, Alex recommends focusing on simplifying the conversation to two questions: “Firstly, decide on what movable metrics are going to make the biggest difference. Next, ask those in charge of delivering those numbers what they need to know to move them.”