8 better practices from information architecture By: Lou Rosenfeld
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The document provides 8 better practices for information architecture (IA) to improve findability: 1. Diagnose the important problems by analyzing usage patterns and prioritizing what matters most. 2. Balance evidence from both quantitative and qualitative research methods. 3. Advocate for the long term by developing project visions and measurable goals. 4. Measure engagement beyond conversions by tracking tasks, trust, and connections between content. 5. Support contextual navigation through content modeling and understanding user desire lines. 6. Improve search across silos by recognizing query patterns and specialized search needs. 7. Combine manual and algorithmic design approaches effectively like an onion with layered information. 8
8 better practices from information architecture By: Lou Rosenfeld
- 1. 8 better practices from information architecture Lou Rosenfeld
- 2. Hello, my name is Lou www.louisrosenfeld.com | www.rosenfeldmedia.com 2
- 3. 3
- 4. The state of contemporary findability 3
- 5. Some questions that you probably can’t answer • Who are your content’s primary audiences? • What are the five major tasks and needs each has? • Are you satisfying those tasks and needs? • What data support your thinking? • How do you measure success? 4
- 6. Why can’t we get findability right?
- 7. Why can’t we get findability right? • We don’t know how to diagnose
- 8. Why can’t we get findability right? • We don’t know how to diagnose • We don’t know how to measure
- 9. Why can’t we get findability right? • We don’t know how to diagnose • We don’t know how to measure • Siloed organizations
- 10. Why can’t we get findability right? • We don’t know how to diagnose • We don’t know how to measure • Siloed organizations • Ill-equipped decision-makers
- 11. Why can’t we get findability right? • We don’t know how to diagnose • We don’t know how to measure • Siloed organizations • Ill-equipped decision-makers • Short-term thinking
- 12. Why can’t we get findability right? • We don’t know how to diagnose • We don’t know how to measure • Siloed organizations • Ill-equipped decision-makers • Short-term thinking • Semantic illiteracy
- 13. Data is binary Information isn’t
- 14. Information architecture: 8 better practices for findability 1. Diagnose the important problems 2. Balance your evidence 3. Advocate for the long term 4. Measure engagement 5. Support contextual navigation 6. Improve search across silos 7. Combine design approaches effectively 8. Tune your design over time 7
- 16. A 9
- 17. A Not all queries are distributed equally 9
- 18. A Nor do they diminish gradually 9
- 19. A 80/20 rule isn’t quite accurate 9
- 20. ( 10
- 21. ( 10
- 22. ( 10
- 23. ( 10
- 24. The Long Tail is ( much longer than you’d suspect 10
- 25. Zipf Distribution in text 11
- 26. It’s Zipf’s World; we just live in it A little... • queries • tasks • ways to navigate • features • documents ...goes a long way 12
- 27. UNVERIFIED RUMOR: 90% of Microsoft.com content has never been accessed... not even once TAKEAWAY: FOCUS ON THE STUFF THAT MATTERS!
- 28. Continually prioritize to do what’s important... 14
- 29. ...and continually fix (within to do an IA report card) 15
- 30. #2 Balance your evidence 16
- 31. from Christian Rohrer: http://is.gd/95HSQ2 17
- 32. Balanced research leads to true insight, new opportunities from Christian Rohrer: http://is.gd/95HSQ2 17
- 33. Lou’s TABLE OF OVERGENERALIZED Web Analytics User Experience DICHOTOMIES Users' intentions and What they Users' behaviors (what's motives (why those things analyze happening) happen) Qualitative methods for What methods Quantitative methods to explaining why things they employ determine what's happening happen Helps users achieve goals What they're Helps the organization meet (expressed as tasks or trying 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 small What kind of data in large volumes, full of volumes, generated in lab they use errors) environment, full of errors) 18
- 34. Balance over time: From projects to processes Example: the rolling content inventory 19
- 35. Develop a research regimen to do balanced by time, quadrant Each week, for example... • Analyze analytics for trends (Behavioral + Quantitative) • Task analysis of common needs (Behavioral + Qualitative) Each month... • User survey (Attitudinal + Quantitative) • Exploratory analysis of analytics data (Behavioral + Qualitative) Each quarter... • Field study (Behavioral/Attitudinal + Qualitative) • Card sorting (Attitudinal + Qualitative/Quantitative) 20
- 36. #3 Advocate for the long-term 21
- 37. S Typical design focus Stuff that gets ignored: mission, vision, charter, goals, KPI, objectives 22
- 38. For starters, develop your to do project’s elevator pitch Read Gamestorming (Gray, Brown, Macanufo); O’Reilly, 2010). http://amzn.to/nnpERG 23
- 39. #4 Measure engagement 24
- 42. The missing metrics of in-betweenness • Orientation (“What can I do here?”) • Engagement (“I like this; do you?”) • Connection/cross-promotion (“What goes with this?”) • Authority (“I trust this”) • and many more... 26
- 43. Use gradual engagement to do model to isolate, measure tasks Example: adoption of features; can you measure movement between layers? Layer 0: User visits the site (unauthenticated; no cookies, no nothing) Layer 1: User asks the site a question (for example, a search query) Layer 2: Site asks the user a question (would you like save this product to a wish list?) Layer 3: Site suggests something to the user (you might enjoy these products ordered by people like you) Layer 4: Site acts on the user's behalf (we've gone ahead and saved these products to your More on gradual engagement: account's list of frequently-ordered items) http://bit.ly/9hPqyx 27
- 45. Contextual navigation: your site’s desire lines Determine through content modeling, site search analytics Deep navigation requires content modeling : a better approach to deep IA and content structuring
- 46. Important content objects emerge concert calendar from content modeling (example: BBC) album pages artist descriptions TV listings Content that matters most album reviews discography artist bios 30
- 47. Important metadata attributes emerge from content modeling Metadata that matters most 31
- 48. Make content modeling a to do participatory design exercise
- 49. Make content modeling a to do participatory design exercise •Provide subjects with “de-oriented” samples of content types... and common tasks •Have them draw “desire lines” and starting points, and identify gaps in content types •Learn from “think out loud” and by identifying common patterns •More info: Atherton et al.’s “domain modeling” presentation: http://slidesha.re/fzChQB
- 50. #6 Improve search across silos 33
- 51. Reconsidering the search UI... 34
- 52. ...by contextualizing “advanced” features, focusing on revision
- 53. ...by contextualizing “advanced” features, focusing on revision search session patterns 1. solar energy 2. how solar energy works
- 54. ...by contextualizing “advanced” features, focusing on revision search session patterns 1. solar energy 2. how solar energy works search session patterns 1. solar energy 2. energy
- 55. ...by contextualizing “advanced” features, focusing on revision 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
- 56. ...by contextualizing “advanced” features, focusing on revision 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
- 57. ...by contextualizing “advanced” features, focusing on revision 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
- 58. Recognizing specialized queries (e.g., proper nouns, dates, unique ID#s) 36
- 59. Recognizing specialized queries (e.g., proper nouns, dates, unique ID#s) search pattern: TA292761 36
- 60. Recognizing specialized queries (e.g., proper nouns, dates, unique ID#s) search pattern: regulations March 2011 search pattern: TA292761 36
- 61. Recognizing specialized queries (e.g., proper nouns, dates, unique ID#s) search pattern: regulations March 2011 search pattern: regulations Owens search pattern: TA292761 36
- 62. Recognizing specialized queries (e.g., proper nouns, dates, unique ID#s) search pattern: regulations March 2011 search pattern: regulations Owens search pattern: search pattern: regulations TA292761 Caterpillar 36
- 63. ...and designing specialized search result 37
- 64. ...and designing specialized search result 37
- 65. ...and designing specialized search result 37
- 66. Poor search results returned by search engine Content objects from product content model ...and designing specialized search result 37
- 67. Read a book chapter on to do session analysis You’ll find one in my book Search Analytics for Your Site http://bit.ly/quFxdz 38
- 69. Y 40
- 70. Y Narrow, deep content access 40
- 71. V 41
- 72. V ...to editorially rich content 41
- 73. 42
- 74. Manually selected results 42
- 75. Manually selected results ...complement raw results 42
- 76. Treat your content to do like an onion information layer 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) refresh monthly (e.g., UserTesting.com) tagged by experts “traditional” lab-based titled according to 3 (topical tags) user testing guidelines content models for A/B testing structured according 4 contextual navigation to schema 43
- 77. Treat your content to do like an onion Each layer is cumulative; most important content is information layer usat thecore 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) refresh monthly (e.g., UserTesting.com) tagged by experts “traditional” lab-based titled according to 3 (topical tags) user testing guidelines content models for A/B testing structured according 4 contextual navigation to schema 43
- 78. #8 Tune your design over time 44
- 79. Your site is a moving target built on moving targets 45
- 80. I 46
- 81. I Time to Interest in the study! football team: going ...going gone 46
- 82. I 47
- 83. Before Tax Day I 47
- 84. I 48
- 85. After Tax Day I 48
- 86. Move from time-boxed to do projects to ongoing processes Example: the rolling content inventory 49
- 87. Summary: 8 IA better practices 1. Diagnose the important problems 2. Balance your evidence 3. Advocate for the long term 4. Measure engagement 5. Support contextual navigation 6. Improve search across silos 7. Combine design approaches effectively 8. Tune your design over time 50
- 88. S Let’s stop boiling the ocean 50
- 89. Say hello Lou Rosenfeld lou@louisrosenfeld.com Rosenfeld Media www.louisrosenfeld.com | @louisrosenfeld www.rosenfeldmedia.com | @rosenfeldmedia 51
Editor's Notes
- #5: http://xkcd.com/773/
- #8: http://www.semanticreview.com/images/semantic-data.jpg
- #11: Amazing drawing by Eva-Lotta Lamm: www.evalotta.net
- #12: Amazing drawing by Eva-Lotta Lamm: www.evalotta.net
- #22: Funnel: http://www.orionweb.net/wp-content/uploads/conversion-funnel.png Sitemap: http://www.peacockvaughninsurance.com/images/SiteMap.bmp
- #37: Onion courtesy Eva-Lotta Lamm