Many organizations are trying to find ways to meaningfully combine their business and social data around operations, products, or services they offer, but encounter having a hard time “marshalling the data” (no pun intended). Certainly, unrealistic expectations around immature technologies (i.e.: Gartner Hype Cycle) have played a role in this “disconnect,” along with company/industry/stakeholder culture/DNA being out of sync with what various technologies and methods of data to be collected along with the meaning the data has for them.
But I don’t want to get hung up on why organizations fail.
Rather, I want to help institutions succeed by helping them find the best ways to enrich customer and visitor data collected via the social Web, to get insights from the data, and use it to inform enterprise projects. Most organizations (and individuals) aren’t actually “data-driven”; instead they are “concept”- and “model”-driven and struggling to identify the best “concept” or “model” to be driven by and get support for, is their real challenge. However, organizations begin this process of awakening to the data, by first becoming aware of the data they lack.
Take the Metropolitan Museum of Art, an institution that is really trying hard to be “data-driven” (and whose “culture” has been historically antagonistic to data measurement); the current director, Thomas P. Campbell, expressed the need for the art world in general to become more “metrics-driven” and business-friendly while in Davos at the World Economic Forum, earlier this year.
What Campbell is up against by trying to make a case for the relevance of the arts in the political and economic area is that the mental, cultural, and business “models” that cultural organizations (and many of their supports) operate with, don’t support it. Also, I bet there is no clear process to collect data that actually could be useful and perhaps there needs to be a “chief data officer,” etc.
I know for a fact that the museum doesn’t collect a lot of information they easily could and with a small shift in their “Model,” they easily could.
For example, I am a sustaining member and every time I visit, the museum knows I came (they generate a pass which records the event in a database); they also know every time I sip wine or coffee at the Balcony Bar (they lady at the desk writes down my membership number) or go to a special members preview of an upcoming exhibit or show that is about to open (they collect my membership number there, as well).
The Metropolitan knows a lot about its members.
But what about the rest of the 6.2 million or so who visited last year? I think the Metropolitan knows next to nothing about visitors who are not members; all they can do is count foot traffic and perhaps get some of them to follow their social media properties (mainly Twitter and Facebook) and hope for the best.
That’s not a good place to be if you want to be “data-driven.”
I set out to answer some of those questions using “public” data and some high-end platforms such as Geofeedia and StatSocial that we have at our disposal at the Zicklin School of Business within Baruch College; and, I presented the latest findings at an analytics conference in Miami earlier this month.
Figure 1: Visitation at the Metropolitan Museum of Art spanning 330 days (11/14/13-10/21/14)
With Geofeedia I was able to collect public data on accounts on social media (mostly Instagram and Twitter) that were posted while visitors were physically at the museum; and, Geofeedia was able to capture how many times they came and posted as well as what else they were looking at and when from public data. In other words, I was able to find answers to some of the questions the museum wants to know about 86 percent of its visitors, particularly about the attendance and engagement of non-members, based on 56,139 accounts gathered by Geofeedia over 330 days.
One limitation the museum surely has is that its social media platforms cannot isolate the activity of people in the museum from overall conversations in social media. While online activity is vitally important, the people on your doorstep are even more important, and the museum knows next to nothing about them.
I decided to sub-segment the data being collected up to a point by looking at activities visitors do at the Metropolitan. Sixteen themes or “stories” emerged from the data, which I then curated.
Figure 2: 16 Themes or Stories that happen at the Metropolitan Museum of Art
The focus on “storytelling” and stories is a way we, as humans make sense of the world. In analytics, that translates into a need to tell stories with data (which are meant to inform “business, mental, and cultural models”). I believe for institutions like the Metropolitan, the main challenge is the urgent need to find better operational and cultural models that will sustain visitation and membership growth along with understanding and acting on data they collect.
It turned out the data I collected paints a picture that their Rooftop Bar/Café – a place at the museum that is open six months of the year, generates 24 percent of all its “on-location” social media activities, suggesting the Met should find a way to keep the Rooftop Bar open all year (perhaps putting a bubble on the rooftop during the winter months). Certainly, going up to the Rooftop Bar in the middle of winter, insulated in a nice warm bubble while watching the snow fall (and sunning themselves), while sipping spirits, would be a wonderful new celebration (and story) to talk about and remember.
Among the more surprising findings was a strong culture meme taking place around the popular cable TV show Gossip Girl that took place on the steps of the Metropolitan Museum, a story the museum has yet to pivot off, which makes up 3 percent of its total on location social media postings by devoted fans of the show (which is now off the air). Another data story is how visitors profile themselves by selfies taken in front of the various pieces of art as well as in the bathrooms at the museum, which is 4 percent of their on-location activity in social media. Perhaps the museum ought to put some images of popular pictures in the bathrooms and invite visitors to take pictures against it.
Looking at the Temple of Dendur highlighted an issue that most media analytics tools have with social media; there were six times as many mentions and pictures of Temple of Dendur where the keyword “Dendur” wasn’t present, than when it was, showing a persistent “data gap” between what people post about vs. what they actually are doing.
Finally, I looked at the Twitter accounts of visitors to the museum that attended between two and 12 times over the 330 days I studied using a platform called StatSocial.com to look for brand affinities and membership behaviors that exhibit themselves in the behavior of mostly non-members.
Figure 3- What are twitter followers of frequent Metropolitan Museum of Art visitors interested in?
It turns out a desire to sip Veuve Clicquot champagne stands out along with a preference for Perrier water, certainly something to note for subsequent events that can help turn non-members into members of the museum. Certainly, substantive research has been done that proves getting the audience to adopt a low level of “compliance” (same thing as membership in this context) makes successively higher levels of membership easier to attain for organizations, and even a modest increase in the most inexpensive membership options can drive amazing returns on investment for the museum over time. Certainly that was the case for me – I started at a lower level of individual membership and jumped up become a sustaining member.
Perhaps the next time Campbell goes to Davos he’ll have a lot more to talk about. Certainly I, and my students, would love to help the museum with THAT conversation and talking points.
Homepage image via Shutterstock.