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Mw2014 friends analytics

Mw2014 friends analytics

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A presentation by Bruce Wyman and Rob Stein at the Museums and the Web 2014 conference in Baltimore, MD. The presentation documents the first year of operations and strategy for the DMAFriends program at the Dallas Museum of Art

A presentation by Bruce Wyman and Rob Stein at the Museums and the Web 2014 conference in Baltimore, MD. The presentation documents the first year of operations and strategy for the DMAFriends program at the Dallas Museum of Art

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Mw2014 friends analytics

  1. 1. SEEING THE FOREST AND THE TREES How Engagement Analytics can Help Museums Connect to Audiences at Scale Robert Stein Dallas Museum of Art @rjstein Bruce Wyman USD Design | MACH Consulting @bwyman
  2. 2. HOW DO YOU MEASURE WHAT YOU CAN’T SEE? Flickr Credit ~liquoredonlife
  3. 3. IS IT POSSIBLE TO CREATE A PLATFORM FOR ENGAGEMENT?
  4. 4. CAN DATA HELP CREATE A MUSEUM THAT IS AGILE AND RESPONSIVE?
  5. 5. The Largest Art Museum in the Region 110 Years Old Supported by the City of Dallas NOT a Tourist Destination
  6. 6. The 2010 U.S. census reports that only 14.5% of US Adults visited museums in the prior 12 months (Census, 2012). 8%Dallas = 6.5M People - 500k Annual Attendance
  7. 7. METRICS WE CARE ABOUT 1.Repeat visits 2.Diverse participation 3.Increased affinity 4.Ability to motivate action
  8. 8. DMAfriends HOW IS IT BUILT TODAY?
  9. 9. Current Software Architecture
  10. 10. Launch Day January 21, 2013
  11. 11. DMAfriends deep dive Taking a
  12. 12. BIG DATA (almost) 1M Rows in the Activity Stream 1M System Log Events 20+ User Profile Fields … Big for Museums Anyways
  13. 13. What’s the data teaching us?
  14. 14. http://dma.org/friends/by-the-numbers
  15. 15. http://dma.org/friends/by-the-numbers
  16. 16. http://dma.org/friends/by-the-numbers
  17. 17. http://dma.org/friends/by-the-numbers
  18. 18. http://dma.org/friends/by-the-numbers
  19. 19. http://dma.org/friends/by-the-numbers
  20. 20. Bobby
  21. 21. SOFTWARE FOUNDATION / The Future
  22. 22. GOAL: Can we use data to make our museum more effective at producing engaged and invested members?
  23. 23. ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?? ? ? ? ? ? ? ? ? WHAT MIGHT A NATIONAL ENGAGEMENT NETWORK LOOK LIKE?
  24. 24. VISION PILOT PROJECTS • Denver Art Museum • Grace Museum, Abilene • LACMA • Minneapolis Institute of ArtsFOUR PILOT SITES
  25. 25. MOST IMPORTANT OUTCOME
  26. 26. GET THE CODE http://badgeos.org http://github.com/DallasMuseumArt/DMA-Friends

Editor's Notes

  • Is it possible to create a platform for engagement?Can such a platform support the diverse business models and unique expressions of engagement at a variety of museums?
  • Share about the design processWhy did we think a badging system was a good idea?What appeal does Wordpress hold as a basis for this approachWhy should / shouldn’t we think about systems integration with ticketing / membership / web siteWhy didn’t we allow online Friends from the beginning?Why didn’t we choose to “do an app”.How about privacy? Aren’t we concerned about tracking?What sorts of features fell off the list during launch? (visualization, mobile, projections, other cultural participation, etc…) Should any of them be included in the project now?
  • Relationship between activities and badges is awkward right nowSystem slowdowns under heavy loadData storage is both flexible, but perhaps not structured enoughCamera scanning of barcodesNo direct interface to Raiser’s EdgeCoupons are not currently unique to an individualMailChimp integration is desired, but not functioning efficientlyEngagement is one-level deep – need more complex paths of engagement more detailed engagement designNeed more kiosks
  • You’ll all have a chance to become a DMA Friend todayYou’ll notice iPad kiosksLabels on the WallsPay attention to the customer service aspect of the visitExplain some of the deficiencies of the program as it exists today.Share some about the hard parts and things we’re still trying to figure out.What are the limits of what we should expect from this project? THIS IS HARD! FAIL EARLY FAIL OFTEN! BUILD SMALL BUILD FASTWe will show you metrics after lunch
  • Summarize issues connected with data-sizeTalk about tools used and their relative advantagesChartioTableauPython + pandas, numpy, scipy, etc…
  • Front-of-house recruiting successesMotivate visits to the permanent collexGeographic distribution of participation (infer census)Call and response in the data (exhibitions)Repeat Visits
  • CRM + BadgeOS + ElasticSearch + VisMultiple locations – federated data reportingAnalysis framework examples
  • A staff that’s learning how to iterate and use data to increase impact of mission fundamentals

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