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8 Information Architecture Better Practices

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Closing keynote at Webvisions Barcelona, 7 July 2012.

Published in: Education, Technology

8 Information Architecture Better Practices

  1. 8 better practicesfrom information architectureLou Rosenfeld •  Rosenfeld Media •  rosenfeldmedia.com
  2. Hello, my name is Lou www.louisrosenfeld.com | www.rosenfeldmedia.com
  3. The state ofcontemporary findability
  4. Why can’t we getfindability right?• Semantic illiteracy• Siloed organizations• Ill-equipped decision-makers prone to short-term thinking• We don’t know how to diagnose• We don’t know how to measure
  5. Information architecture:8 better practices for findability 1. Diagnosing the important problems 2. Balancing our evidence 3. Designing for the long term 4. Measuring engagement 5. Supporting contextual navigation 6. Improving search across silos 7. Combining design approaches effectively 8. Tuning our designs over time
  6. #1Diagnosing theimportant problems
  7. A handful of queries/tasks/ways to navigate/features/ A little goes a long waydocuments meet the needs of your most important audiences
  8. A handful of queries/tasks/ways to navigate/features/ A little goes a long waydocuments meet the needs of your most important audiences Not all queries are distributed equally
  9. A handful of queries/tasks/ways to navigate/features/ A little goes a long waydocuments meet the needs of your most important audiences
  10. A handful of queries/tasks/ways to navigate/features/ A little goes a long waydocuments meet the needs of your most important audiences Nor do they diminish gradually
  11. A handful of queries/tasks/ways to navigate/features/ A little goes a long waydocuments meet the needs of your most important audiences
  12. A handful of queries/tasks/ways to navigate/features/ A little goes a long waydocuments meet the needs of your most important audiences 80/20 rule isn’t quite accurate
  13. (and the tail is quite long)
  14. (and the tail is quite long)
  15. (and the tail is quite long)
  16. (and the tail is quite long)
  17. (and the tail is quite long)
  18. The Zipf Curve, textually
  19. It’s Zipf’s World;we just live in it A little... • queries • tasks • ways to navigate • features • documents ...goes a long way
  20. unverified rumor alert
  21. unverified rumor alert90% of Microsoft.com content
  22. unverified rumor alert90% of Microsoft.com content has never been accessed...
  23. unverified rumor alert90% of Microsoft.com content has never been accessed... not even once
  24. unverified rumor alert90% of Microsoft.com content has never been accessed... not even once
  25. #2Balancing our evidence
  26. from Christian Rohrer: http://is.gd/95HSQ2
  27. Balanced research leads to true insight, new opportunitiesfrom Christian Rohrer: http://is.gd/95HSQ2
  28. Lou’s TABLE OFOVERGENERALIZED Web Analytics User Experience DICHOTOMIES Users intentions and What they Users behaviors (whats motives (why those things analyze happening) happen) Qualitative methods for What methods Quantitative methods to explaining why things they employ determine whats happening happen Helps users achieve goals What theyre Helps the organization meet (expressed as tasks ortrying to achieve goals (expressed as KPI) topics of interest) Uncover patterns and How they use Measure performance (goal- surprises (emergent data driven analysis) analysis) Statistical data ("real" data Descriptive data (in smallWhat kind of data in large volumes, full of volumes, generated in lab they use errors) environment, full of errors)
  29. #3Designing for the long-term
  30. Stewart Brand’s Pace Layeringmodel Typical design focus Stuff that gets ignored: mission, vision, charter, goals, KPI, objectives
  31. #4Measuring engagement
  32. Measuringconversions?No problem...
  33. ..measuringanything else? Good luck!
  34. The missing metricsof engagement• Orientation (“What can I do here?”)• Authority (“I trust this”)• Social (“Who else likes this?”)• Connection/cross-promotion (“What goes with this?”)• and many more...
  35. Conversation architectureuncovers levels of engagementLevel 0: I visit site (unauthenticated)Level 1: I ask site a question (e.g., a search)Level 2: Site asks me a question (“can we save thosesettings?”)Level 3: Site suggests somethingto me (“you might also like this”)Level 4: Site acts on my behalf(“I’ve added this to your favorites list in case you’d like to reorder”) trust and value grow progressively
  36. #5Supportingcontextual navigation
  37. Contextual navigation:your site’s desire lines
  38. 1.Choose a contenttype (e.g., events) 
2.Ask: “Whereshould users gofrom here?”3.Analyze the 
frequent queriesfrom this contenttype from aiga.org 

  39. 
 
 
 
 
 
Analyze frequent queries generated from each content sample
  40. Content models emerge (example: BBC) concert calendar album pages artist descriptions TV listingsalbum reviews discography artist bios
  41. User studies are another greatway to get at content models
  42. #6Improving search across silos
  43. Reconsidering the search UI...
  44. ...convert “advanced” searchinto refinement
  45. ...convert “advanced” searchinto refinement search session patterns 1. solar energy 2. how solar energy works
  46. ...convert “advanced” searchinto refinement search session patterns 1. solar energy 2. how solar energy works search session patterns 1. solar energy 2. energy
  47. ...convert “advanced” searchinto refinement search session patterns search session patterns 1. solar energy 1. solar energy 2. solar energy charts 2. how solar energy works search session patterns 1. solar energy 2. energy
  48. ...convert “advanced” searchinto refinement search session patterns search session patterns 1. solar energy 1. solar energy 2. solar energy charts 2. how solar energy works search session patterns search session patterns 1. solar energy 1. solar energy 2. explain solar energy 2. energy
  49. ...convert “advanced” searchinto refinement search session patterns search session patterns 1. solar energy 1. solar energy 2. solar energy charts 2. how solar energy works search session patterns search session patterns 1. solar energy 1. solar energy 2. explain solar energy 2. energy search session patterns 1. solar energy 2. solar energy news
  50. Design forspecialized queries(e.g., proper nouns,dates, unique ID#s)
  51. ...and design specialized search results
  52. ...and design specialized search results
  53. ...and design specialized search results
  54. Content objects from productcontent model ...and design specialized search results
  55. Poor search results returned by search engineContent objects from productcontent model ...and design specialized search results
  56. #7Combining design approacheseffectively
  57. Yes, manual effort is still asimportant as tools
  58. Yes, manual effort is still asimportant as tools Narrow, deep content access
  59. Vanguard’s Tax Center is asimple, low-tech, editorial
  60. Vanguard’s Tax Center is asimple, low-tech, editorial ...to editorially rich content
  61. Manuallyselected results
  62. Manuallyselected results ...complement raw results
  63. Treat your content Each layer is cumulative; most important like an onion content is at the core informationlayer usability content strategy architecture indexed by search 0 engine leave it alone leave it alone squeaky wheel issues 1 tagged by users addressed refresh annually tagged by experts (non- test with a service 2 topical tags) (e.g., UserTesting.com) refresh monthly tagged by experts “traditional” lab-based titled according to 3 (topical tags) user testing guidelines content models for structured according 4 contextual navigation A/B testing to schema
  64. #8Tuning designs over time
  65. Your site is a moving targetbuilt on moving targets
  66. Impact of change on design(queries)
  67. Impact of change on design(queries) Interest in the football team: going...
  68. Impact of change on design(queries) Interest in the football team: going... ...going...
  69. Impact of change on design(queries) Interest in the football team: going... ...going... gone
  70. Impact of change on design(queries) Time to Interest in the study! football team: going... ...going... gone
  71. IRS before Tax Day
  72. Before Tax DayIRS before Tax Day
  73. IRS after Tax Day
  74. After Tax DayIRS after Tax Day
  75. Summary:8 IA better practices1. Diagnosing the important problems2. Balancing our evidence3. Designing for the long term4. Measuring engagement5. Supporting contextual navigation6. Improving search across silos7. Combining design approaches effectively8. Tuning our designs over time
  76. Say hello Lou Rosenfeld lou@louisrosenfeld.com Rosenfeld Media  www.louisrosenfeld.com | @louisrosenfeld www.rosenfeldmedia.com | @rosenfeldmedia

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