This talk was presented at MW20 on April 4, 2020.  The Web page for this presentation can be found at:  https://mw20.museweb.net/proposal/big-data-and-the-visitor-journey-using-data-science-to-understand-visitor-experience-in-the-artlens-gallery-and-beyond/•  This presentation will discuss why we hired a data scientist to understand visitor experience at the Cleveland Museum of Art, in the ArtLens Gallery and beyond... Since the MW20 conference happened virtually, we decided to discuss how we continued to work together while the museum was closed and everyone was working remotely. Learn more about the Cleveland Museum of Art at https://www.clevelandart.org/

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Big Data and the Visitor Journey:
Using Data Science to Understand Visitor Experience
…including when you are in the middle of a pandemic
Museums and the Web Virtual Conference
April 4, 2020
Jane Alexander, Cleveland Museum of Art
& Cal Al-Dhubaib, Pandata

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Jane Alexander
Chief Information/Digital Officer
The Cleveland Museum of Art
Cal Al-Dhubaib
Managing Partner / Data Scientist
Pandata
@janecalexander @caldhubaib

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This past fiscal year,
more than 850,000 people visited
The Cleveland Museum of Art
...and we collected a lot of data

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But what is this DATA telling us?

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Strategic Plan
Launched in September 2017
• Achieve greater understanding
of current and potential
audiences
• Refine analytical models to aid
in future planning

6. Goal of Gallery One: 2012
To attract, create, engage, and give people the
toolsets to look closer, dive deeper, and
feel comfortable spending time in all our
museum galleries
Our Case Study

7. Gallery One  ARTLENS Gallery
Through the multiple iterations of the space, our main
goals have remained the same

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Is Gallery One Working?

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Collecting, Sharing, and Analyzing Data
from the Beginning:
• Which game(s)?
• Time spent
• Repeat(s)
• Object(s) matched
• Share(s)
• Favorite(s)
• App download(s)

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Individual attendance at the museum increased by 31%
The attendance of families increased by 29%.
Still asking....
Are the goals being met?
The Results of Gallery One 2013

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2015 Gallery One Evaluation Report
• 1930 Lens had the longest
average use time.
• However, this lens was
observed to be one of the
least visited lenses.
• The Sculpture lens had the
shortest average use time
• BUT...the Sculpture lens was
the most frequently used in
the space.
Figuring out what questions to ask with the data we collected
* Though data was taken in 2013, report was not published until 2015. Gallery One and ArtLens Evaluation Report by:
Elizabeth Bolander, Director of Research and Evaluation, and Meghan Stockdale, Research Analyst. January 2015

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Gallery One Lens: 1:45
Average Viewing Time
Painting: 2-9 Seconds

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Learning How to
Evaluate Digital

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ARTLENS Gallery 2017 Open
Are visitors who are going
into ArtLens gallery going into the
galleries and spending meaningful
time
Changed how to evaluate digital

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• 2-year project to examine new
ways of measuring the impact
of digital interactives in art
museums.
• ARTLENS Gallery as a case
study
Bolander, E.,Ridenour, H.,Quimby, C. 2019. Art Museums and Technology: Developing
New Metrics to Measure Visitor Engagement. Project funding was generously provided
by the National Endowment for the Arts ArtWorks program.
2018: New Methods

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Bolander, E.,Ridenour, H.,Quimby, C. 2019. Art Museums and Technology: Developing New Metrics to Measure Visitor Engagement. Project funding was
generously provided by the National Endowment for the Arts ArtWorks program.
36% of the
participants
spent time in
ARTLENS
Gallery
People who visited ARTLENS Gallery demonstrated greater gains
in self-reported level of art understanding and knowledge

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New WiFi endpoints allow for wayfinding in ArtLens App and
improved WiFi coverage around the museum
Meanwhile…
2017: Meraki Endpoints Implemented
Across the Museum

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Analyzing Visitor Experience with
Meraki Wireless Access Points
Meraki allows us to collect data from any wireless antenna hitting our Wi-
Fi access points, whether they’ve logged onto our wireless network or not.

19. Yes! We Have “Big Data”
• 105 wireless access points
• 1-2 million rows of data per day
• 0.4 GB per day just for search indexes
• A system of 8 separate cloud services
(plus Tableau) to process it all

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We hired a data scientist
to analyze our “Big Data”!
• Our attention was focused on
other digital projects
• Open Access
• Middleware
• Photogrammetry
But we knew there was an
opportunity to learn from our
Meraki data

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Visitors who spend at least 5 minutes in
ARTLENS Gallery are spending
approximately 30-60 minutes longer in the
museum and visiting more spaces
compared to those who do not visit
ARTLENS Gallery.
2019 Quantitative Findings:
…How do we know this?

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We Still Want To Know:
Where Are People Going?
How Long Are They Staying There?
Where Do They Go Next?
What Are They Learning?

23. So how did Data Science help?
• Noisy data
• Translating devices to visitors
• Building trust
• Meaningful visualizations

24. Noisy data – passersby & staff

25. Noisy data – access points

26. Noisy data – access points
Each router
spots a device
and estimates
a distance and
certainty

27. Noisy data – access points
We triangulate
most likely
coordinates
and certainty

28. Noisy data – data vs reality
1
2
3
4
5

29. Noisy data – data vs reality
1
2
3
4
5
This path is
not realistic….

30. Noisy data – data vs reality
1
2
3
4
5
The visitor
most likely
traveled this
way

31. Exhibit 1
Exhibit 2
Exhibit 5
Ex. 6 Exhibit 4
Exhibit 3
Exhibit 7
Exhibit 8 Exhibit 10
Exhibit 9 Exhibit 9
Exhibit 11
Ex. 13
Exhibit 12
Level One
Turning this into gallery insights
We assign
each point
along the
inferred path
to an exhibit

32. What is a visitor?
• Some visitors have multiple
wearables
• Some phones dynamically
generate MAC addresses
• Some visitors do not have wi-fi
enabled

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Getting the data into the
hands of leadership

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35. Data solutions fail without trust

36. We had to build trust by
verifying known trends -
Kusama Exhibit

37. So we could look at new data,
like time…

38. ExperimentOperationalize
Data Science is iterative

39. ExperimentOperationalize
1. Discover
2. Explore
3. Build
4. Test
5. Proof of concept
6. Proof of
value
7. Scale
Data Science is iterative
Business
decisions
8. Educate

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• 177k visitors did not
visit ARTLENS
• Spent 2.2 hours on
average in museum

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• 177k visitors did not
visit ARTLENS
• Spent 2.2 hours on
average in museum
These visitors go to few
spaces overall

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• 82k visitors visited
ARTLENS
• They spent 12 minutes
longer at the museum

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• 82k visitors visited
ARTLENS
• They spent 12 minutes
longer at the museum These visitors
interact with
almost 4 times
the number of
spaces!

45. • 37k visitors spent more than 5
minutes in ARTLENS
• They spent on average 36
minutes more at the museum!

46. • 37k visitors spent more than 5
minutes in ARTLENS
• They spent on average 36
minutes more at the museum!
These visitors
interact with
more spaces, for
even longer
periods of time

47. • 37k visitors spent more than 5
minutes in ARTLENS
• They spent on average 36
minutes more at the museum!
These visitors
interact with
more spaces, for
even longer
periods of time
They’re likely
spending more
money at the café!

48. And now we can ask questions like this
... Does the selection of artworks in
ARTLENS impact gallery visits?

49. 49

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On January 23, 2019, CMA
launched its Open Access initiative,
releasing high-resolution images of all its public-
domain artworks and collection information for
the entire collection, more than 61,000 art
objects
CMA's Open Access collection is hosted on repositories across the web, such as
Wikimedia, Creative Commons, and ArtStor

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53. Analytics – Wikimedia Artwork Views, 4 Months (May-Aug)
en.wikipedia.org:
www.clevelandart.org:
224,872
2,503,195
Analyzing pa
The numbers

54. Artwork by Department

55. Artwork by Department
Decorative Art and Design

56. Artwork by Department
Egyptian and Ancient Near Eastern Art

57. We are prototyping live visuals now
57

58. We are prototyping live visuals now
58

59. We are prototyping live visuals now
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60. Measuring digital engagement
• What does digital engagement tell us about physical engagement?
• What content is most relevant to digital visitors?
• How do content updates impact digital consumption?
• How can the CMA continue to be relevant?
60

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Special thanks to
Anna Faxon, Digital
Project Manager,
for helping us put
this presentation
together while we
all are working
remotely.

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Thank you!
@janecalexander
@caldhubaib
@ohpandata