Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Upcoming SlideShare
What to Upload to SlideShare
What to Upload to SlideShare
Loading in …3
×
1 of 62

Big Data and the Visitor Journey: Using Data Science to Understand Visitor Experience... including when you are in the middle of a pandemic

0

Share

Download to read offline

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/

Big Data and the Visitor Journey: Using Data Science to Understand Visitor Experience... including when you are in the middle of a pandemic

  1. 1. t 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
  2. 2. t Jane Alexander Chief Information/Digital Officer The Cleveland Museum of Art Cal Al-Dhubaib Managing Partner / Data Scientist Pandata @janecalexander @caldhubaib
  3. 3. t This past fiscal year, more than 850,000 people visited The Cleveland Museum of Art ...and we collected a lot of data
  4. 4. t But what is this DATA telling us?
  5. 5. t Strategic Plan Launched in September 2017 • Achieve greater understanding of current and potential audiences • Refine analytical models to aid in future planning
  6. 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. 7. Gallery One  ARTLENS Gallery Through the multiple iterations of the space, our main goals have remained the same
  8. 8. t Is Gallery One Working?
  9. 9. t 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)
  10. 10. t 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
  11. 11. t 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
  12. 12. t Gallery One Lens: 1:45 Average Viewing Time Painting: 2-9 Seconds
  13. 13. t Learning How to Evaluate Digital
  14. 14. t 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
  15. 15. t • 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
  16. 16. t 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
  17. 17. t New WiFi endpoints allow for wayfinding in ArtLens App and improved WiFi coverage around the museum Meanwhile… 2017: Meraki Endpoints Implemented Across the Museum
  18. 18. t 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. 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
  20. 20. t 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
  21. 21. t 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?
  22. 22. t 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. 23. So how did Data Science help? • Noisy data • Translating devices to visitors • Building trust • Meaningful visualizations
  24. 24. Noisy data – passersby & staff
  25. 25. Noisy data – access points
  26. 26. Noisy data – access points Each router spots a device and estimates a distance and certainty
  27. 27. Noisy data – access points We triangulate most likely coordinates and certainty
  28. 28. Noisy data – data vs reality 1 2 3 4 5
  29. 29. Noisy data – data vs reality 1 2 3 4 5 This path is not realistic….
  30. 30. Noisy data – data vs reality 1 2 3 4 5 The visitor most likely traveled this way
  31. 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. 32. What is a visitor? • Some visitors have multiple wearables • Some phones dynamically generate MAC addresses • Some visitors do not have wi-fi enabled
  33. 33. t Getting the data into the hands of leadership
  34. 34. t
  35. 35. Data solutions fail without trust
  36. 36. We had to build trust by verifying known trends - Kusama Exhibit
  37. 37. So we could look at new data, like time…
  38. 38. ExperimentOperationalize Data Science is iterative
  39. 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
  40. 40. t
  41. 41. t • 177k visitors did not visit ARTLENS • Spent 2.2 hours on average in museum
  42. 42. t • 177k visitors did not visit ARTLENS • Spent 2.2 hours on average in museum These visitors go to few spaces overall
  43. 43. t • 82k visitors visited ARTLENS • They spent 12 minutes longer at the museum
  44. 44. t • 82k visitors visited ARTLENS • They spent 12 minutes longer at the museum These visitors interact with almost 4 times the number of spaces!
  45. 45. • 37k visitors spent more than 5 minutes in ARTLENS • They spent on average 36 minutes more at the museum!
  46. 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. 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. 48. And now we can ask questions like this ... Does the selection of artworks in ARTLENS impact gallery visits?
  49. 49. 49
  50. 50. t 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
  51. 51. t
  52. 52. Analytics – Wikimedia Artwork Views, 4 Months (May-Aug) en.wikipedia.org: www.clevelandart.org: 224,872 2,503,195 Analyzing pa The numbers
  53. 53. Artwork by Department
  54. 54. Artwork by Department Decorative Art and Design
  55. 55. Artwork by Department Egyptian and Ancient Near Eastern Art
  56. 56. We are prototyping live visuals now 57
  57. 57. We are prototyping live visuals now 58
  58. 58. We are prototyping live visuals now 59
  59. 59. 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
  60. 60. t Special thanks to Anna Faxon, Digital Project Manager, for helping us put this presentation together while we all are working remotely.
  61. 61. t Thank you! @janecalexander @caldhubaib @ohpandata

×