Design to Refine: Developing a tunable information architecture

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My UX London workshop; short version of the full-day workshop I'm teaching this year in San Francisco, Atlanta, and Chicago: http://bit.ly/gL7HaH

My UX London workshop; short version of the full-day workshop I'm teaching this year in San Francisco, Atlanta, and Chicago: http://bit.ly/gL7HaH

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  • Need to make strong point of context of large orgs\n
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  • Microsoft and the 90%\n
  • Microsoft and the 90%\n
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  • In this example, we analyzed AIGA’s top 500 unique queries for a specific month--these accounted for exactly 37% of all search activity. We used Microsoft’s “LEN” function to count the number of characters in each query, and then calculated the queries’ mean and median lengths (10.648 and 10, respectively). \n<big chart>\nSorting by query length, we see that the maximum length among these 500 queries was 62 characters, but that is something of an outlier; the next longest was 36, then 28 and flattening out (apparently, Zipf is everywhere):\n<small chart>\nBased on this data, we might be safe using a search entry box with a width in the 15-20 characters range. If horizontal real estate isn’t at a premium, a width of 30 characters would be even better.\n\n
  • Zipf is everywhere):\n<small chart>\nBased on this data, we might be safe using a search entry box with a width in the 15-20 characters range. If horizontal real estate isn’t at a premium, a width of 30 characters would be even better.\n\n
  • Zipf is everywhere):\n<small chart>\nBased on this data, we might be safe using a search entry box with a width in the 15-20 characters range. If horizontal real estate isn’t at a premium, a width of 30 characters would be even better.\n\n
  • Zipf is everywhere):\n<small chart>\nBased on this data, we might be safe using a search entry box with a width in the 15-20 characters range. If horizontal real estate isn’t at a premium, a width of 30 characters would be even better.\n\n
  • Zipf is everywhere):\n<small chart>\nBased on this data, we might be safe using a search entry box with a width in the 15-20 characters range. If horizontal real estate isn’t at a premium, a width of 30 characters would be even better.\n\n
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  • Might have this already in the SSA workshop slides\n\n
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  • Mention Sandia’s example\n
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  • Anchors will be liked by good leaders, and will outlast bad leaders\n
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Transcript

  • 1. Design to RefineDeveloping a tunable information architectureLou Rosenfeld •  Rosenfeld Media •  rosenfeldmedia.com London •  14 April 2011
  • 2. Hello, my name is Lou
  • 3. Agenda1. The quick intro2. Prioritizing and tuning top-down navigation3. Demo: content modeling4. Prioritizing and tuning contextual navigation5. Group exercise: site search analytics6. Prioritizing and tuning search7. Changing your work and your organization
  • 4. I’ve already dissed redesignSee the slides here:http://www.slideshare.net/lrosenfeld/
  • 5. The alternatives to redesign1. Prioritize: Identify the important problems regularly2. Tune: Address those problems regularly3. Be opportunistic: Look for low-hanging fruit
  • 6. Prioritize becausea little goes a long way
  • 7. A handful of your queries/ways to navigate/documents meet A little data goes a long waythe needs of the few audiences that use your site most
  • 8. A handful of your queries/ways to navigate/documents meet A little data goes a long waythe needs of the few audiences that use your site most LOVE IT
  • 9. A handful of your queries/ways to navigate/documents meet A little data goes a long waythe needs of the few audiences that use your site most LOVE IT LEAVE IT
  • 10. Zipf in text
  • 11. Report card for essential wants and needs
  • 12. Be an incrementalist:tune because things change
  • 13. From projects to processes:a regular regimen of design Example: the rolling content inventory
  • 14. Impact of change on design(queries)
  • 15. Be an opportunist:look for the low-hanging fruit1. Top-down navigation: Anticipates interests/questions at arrival2. Bottom-up (contextual) navigation: Enables answers to emerge3. Search: Handles specific information needs
  • 16. Life by a thousand cuts 50% of users are search dominantx 5% of all queries are typos, fixed by spell checking. 2.5% improvement to the UX 50% of all users are search dominantx 30% (best bet results for top 100 queries) 15% improvement to the UXDitto for improving content, search results design,navigation design…
  • 17. Summary You can refine 1. Prioritize the problems that are most important to your users 2. Regularly address these problems 3. Identify opportunities to make small improvements that go a long way
  • 18. Prioritizing and TuningTop-Down Navigation
  • 19. The data-driven main page:Who wants what and when?
  • 20. Who wants what?US English speakers
  • 21. Who wants what?German speakers
  • 22. When do they want it?
  • 23. Commerce sites get it
  • 24. The IRS gets it
  • 25. But really, who caresabout the main page?
  • 26. But really, who caresabout the main page?
  • 27. The risk of main page fixationFrom Tony Dunn’s Tales from Redesignland(http://redesignland.blogspot.com/)
  • 28. Focusing on main page =taking Zipf too far...plus lots of competition (Google, ads/landing pages)
  • 29. The tail that wags the dog:site map drivesimproved site hierarchy
  • 30. Site map by tool, unit, and format
  • 31. Site map by tool, unit, and format
  • 32. Site map by tool, unit, and format
  • 33. User-centered site mapUser-centered site map...
  • 34. Asking the possiblefrom your site index
  • 35. Specialized site indices
  • 36. Specialized site indices
  • 37. Specialized site indices Cisco’s site indices are specialized by content type (products, services)
  • 38. Best bet-based site indices MSU’s site index is built on popular information needs (based on best bet search results)
  • 39. Going broad and deep withguides (AKA microsites)
  • 40. Vanguard’s main page lovesguides
  • 41. Vanguard’s main page lovesguides
  • 42. The Tax Center is a guide
  • 43. One more example: IRS
  • 44. One more example: IRS
  • 45. ...e-filing is presented assequential steps
  • 46. Summary: Top-down navigation Prioritize main page content and layout 1. Confuse as necessary by diverting attention 2. Counter politics with data; e.g., use seasonality to drive design Tune and prioritize site-wide navigation 3. Use the site map as a skunkworks for site-wide hierarchy 4. Base site indices on specialized content or popular information needs (e.g., best bets) 5. Use guides (micro-sites) as narrow/deep complement to broad/shallow navigation schemes
  • 47. Agenda1. The quick intro2. Prioritizing and tuning top-down navigation3. Demo: content modeling4. Prioritizing and tuning contextual navigation5. Group exercise: site search analytics6. Prioritizing and tuning search7. Changing your work and your organization
  • 48. concert calendar album pages artist descriptions TV listings Demonstration: Content Modelingalbum reviews discography artist bios
  • 49. What are the common content objects in your site? album pages artist bios artist descriptions album reviews 53
  • 50. How do they fit together? concert calendar album pages artist descriptions TV listingsalbum reviews discography artist bios
  • 51. What content objects are missing? concert calendar And how do they fit? album pages artist descriptions TV listingsalbum reviews discography artist bios
  • 52. Where do you start? concert calendar album pages artist descriptions TV listingsalbum reviews discography artist bios
  • 53. How will you connect those objects?
  • 54. Use content modelsfor content that’s... Homogeneous High-volume High importance What’s the most important deep content in your site?
  • 55. Use content modelswhen you need to... Incorporate user research into your deep content Improve contextual navigation Identify missing content Prioritize metadata choices Really benefit from your CMS
  • 56. Steps for developingcontent models1. Determine key audiences (who’s using it?)2. Select important tasks to test (what are they using it for?)3. Determine important content areas (what do they want?)4. Determine content types (what are they using?)5. Determine metadata attributes (how will we connect the objects?)6. Determine contextual linking rules (where should the objects lead us to next?)
  • 57. Agenda1. The quick intro2. Prioritizing and tuning top-down navigation3. Demo: content modeling4. Prioritizing and tuning contextual navigation5. Group exercise: site search analytics6. Prioritizing and tuning search7. Changing your work and your organization
  • 58. Prioritizing and TuningContextual Navigation
  • 59. Establishing Desire LinesUse Content modeling • Site search analytics
  • 60. Where do searches begin?Not just the mainpage, according to aUser InterfaceEngineering study(http://is.gd/j1NHeS)
  • 61. Using site search analyticsto identify desire lines
  • 62. Choose acommon contenttype (e.g., events) !Where should !users go from here? !
  • 63. ! ! ! ! ! !Analyze frequent queries generated from each content sample
  • 64. ! ! !Can you type these queries to improve yourcontent model?Link events to:• the site’s articles on the event’s topic• info on locales for each event
  • 65. What content typesshould we be connecting?
  • 66. Important content types emerge from content modeling concert calendar album pages artist descriptions TV listingsalbum reviews discography artist bios
  • 67. Using SSA to prioritize contenttypes
  • 68. Getting content types out ofsite search analytics Take an hour to... • Analyze top 50 queries (20% of all search activity) • Ask and iterate: “what kind of content would users be looking for when they searched these terms?” • Add cumulative percentages Result: prioritized list of potential content types #1) application: 11.77% #2) reference: 10.5% #3) instructions: 8.6% #4) main/navigation pages: 5.91% #5) contact info: 5.79% #6) news/announcements: 4.27%
  • 69. What should we use toconnect content types?
  • 70. Which metadata attributes will yourcontent model depend upon?
  • 71. More on prioritizing metadata attributes
  • 72. Prioritizing semantic relationships
  • 73. How do weprioritize content?
  • 74. Some content value variables I
  • 75. Some content value variables I UsabilityPopularityCredibility
  • 76. Some content value variables Currency Freshness Authority Follows guidelines (e.g., titling, I metadata) UsabilityPopularityCredibility
  • 77. Some content value variables Currency Freshness Authority Follows guidelines (e.g., titling, I metadata) UsabilityPopularityCredibility Strategic value Addresses compliance issues (e.g., Sarbanes/Oxley) Content owners are good partners
  • 78. Subjectively “grade” your content’s value1.Choose appropriatevalue criteria for eachcontent area2.Weight criteria (total= 100%)3.Subjectively grade foreach criterion4.weight x grade =score5.Add scores foroverall score
  • 79. Subjectively “grade” your content’s value Subjective assessment1.Choose appropriatevalue criteria for eachcontent area2.Weight criteria (total= 100%)3.Subjectively grade foreach criterion4.weight x grade =score5.Add scores foroverall score
  • 80. Put the grades together for a moreobjective “report card” Helps prioritize content migrations, refreshes, ...
  • 81. Put the grades together for a moreobjective “report card” Objectifies subjective assessments Helps prioritize content migrations, refreshes, ...
  • 82. Summary:contextual navigation Use content modeling and site search analytics to 1. Identify and prioritize content types 2. Identify desire lines 3. Improve contextual navigation between content types 4. Identify and prioritize metadata attributes Prioritize content areas/subsites by establishing balanced value criteria
  • 83. Agenda1. The quick intro2. Prioritizing and tuning top-down navigation3. Demo: content modeling4. Prioritizing and tuning contextual navigation5. Group exercise: site search analytics6. Prioritizing and tuning search7. Changing your work and your organization
  • 84. Group exercise:Site search analytics
  • 85. Agenda1. The quick intro2. Prioritizing and tuning top-down navigation3. Demo: content modeling4. Prioritizing and tuning contextual navigation5. Group exercise: site search analytics6. Prioritizing and tuning search7. Changing your work and your organization
  • 86. Prioritizing and Tuning Search
  • 87. Make “the Box” accommodatemost searchers’ queries
  • 88. How long are our queries? Top 500 queries (37% of all traffic)
  • 89. Mean = 10.6 charactersMedian = 10 characters
  • 90. Mean = 10.6 charactersMedian = 10 charactersLong tail queries likely longer
  • 91. Mean = 10.6 charactersMedian = 10 charactersLong tail queries likely longerTop queries often in low 20s !
  • 92. Mean = 10.6 charactersMedian = 10 charactersLong tail queries likely longerTop queries often in low 20sDesired: @30 characters;Can you get that many? !
  • 93. Mean = 10.6 charactersMedian = 10 charactersLong tail queries likely longerTop queries often in low 20sDesired: @30 characters;Can you get that many? !Safe: @15-20 characters
  • 94. We’ve seen this before:auto-completing queries
  • 95. Auto-completing from aknown, common items (e.g.,
  • 96. Auto-completing from aknown, common items (e.g., Uses known terms: e.g., movie titles and actor/director names
  • 97. Auto-completing from queries
  • 98. Uses common queriesAuto-completing from queries
  • 99. Auto-completing from bestbets
  • 100. Auto-completing from bestbets Uses best bets
  • 101. Making change easy:supporting query refinement
  • 102. The absolutemeaninglessness ofadvanced search
  • 103. The absolute meaninglessness of advanced search !At University of Alaska-Fairbanks,advanced = expanded search
  • 104. The absolute meaninglessness of advanced search !At University of Alaska-Fairbanks,advanced = expanded search At the IRS, advanced = narrowed search !
  • 105. Contextualizing “advanced” features
  • 106. Look to session data forprogression and context
  • 107. Look to session data forprogression and context search session patterns 1. solar energy 2. how solar energy works
  • 108. Look to session data forprogression and context search session patterns 1. solar energy 2. how solar energy works search session patterns 1. solar energy 2. energy
  • 109. Look to session data forprogression and context 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
  • 110. Look to session data forprogression and context 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
  • 111. Look to session data forprogression and context 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
  • 112. Improving performance forspecialized queries
  • 113. Recognizing proper nouns,dates, and unique ID#s
  • 114. Surfacingspecialized content typesin search results
  • 115. Tuning Search Results:Handling specialized answers
  • 116. Tuning Search Results:Handling specialized answers
  • 117. Tuning Search Results:Handling specialized answers
  • 118. Tuning Search Results: Handling specialized answers“Product quick links” come directly from product content modelThese results are a strong counterbalance to raw results
  • 119. When raw isn’t good enough:best bet search results
  • 120. best bet #1
  • 121. best bet #1best bet #2
  • 122. best bet #1best bet #2even more best bets
  • 123. best bet #1best bet #2even more best betsraw results
  • 124. best bet #1best bet #2even more best betsraw results
  • 125. best bet #1 best bet #2 even more best betscompetition raw results
  • 126. best bet #1 best bet #2 even more best betscompetition danger? raw results
  • 127. best bet #1 best bet #2 even more best betscompetition danger? data raw results
  • 128. The 0 search results page:search’s equivalent of the 404
  • 129. Tuning Search Results: 0 results pagesNot helpful
  • 130. Tuning Search Results: 0 results pagesNot helpfulMuch better: “Did youmean?” and Popular Searches
  • 131. Summary: Search systems Tune query entry 1. Make “The Box” wide enough 2. Support query auto-completion to focus queries 3. Surface the right features to support query refinement 4. Recognize and take advantage of specialized queries Tune search results design 5. Surface specialized content types as results for specialized queries 6. Complement raw results with best bets 7. Enable recovery from finding 0 search results
  • 132. Agenda1. The quick intro2. Prioritizing and tuning top-down navigation3. Demo: content modeling4. Prioritizing and tuning contextual navigation5. Group exercise: site search analytics6. Prioritizing and tuning search7. Changing your work and your organization
  • 133. Changing your workand your organization
  • 134. Doing your work differently1. Processes, not projects2. Rebalancing your research and design
  • 135. From time-boxed projectsto ongoing processes Example: the rolling content inventory
  • 136. What else can roll?Most everything Each week, for example... • Content scouting and sampling (rather than inventory) • Analyze analytics to identify spikes, new trends Each month... • Identify new tasks, run new task analysis studies • Develop new best bet search results Each quarter... • Field study • Review and tune personas
  • 137. Build a practice that’sbalanced and data-driven
  • 138. User Research Landscapefrom Christian Rohrer: http://is.gd/95HSQ2
  • 139. User Research Landscape Ongoing coverage of each of these 4 quadrantsfrom Christian Rohrer: http://is.gd/95HSQ2
  • 140. 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)
  • 141. Getting your organizationto support your work1. Making friends and allies2. Changing your leaders’ minds
  • 142. Making friends and allies
  • 143. Showing content ownershow their content performs
  • 144. Showing content ownershow their content performs
  • 145. Helping marketingdevelop better messagingJargon vs. Plain Language at Washtenaw Community College • Online courses were marketed using terms “College on Demand” (“COD”) and “FlexEd”; signup rates were poor • Compare jargon with “online” (used in 213 other queries) • Content was retitled rather than re-marketed
  • 146. Helping IT say “no” with authorityReduce pressure to solve problems with technologies by making what we have workMinimize radical changes to platforms • Enterprise search • Content management systems • Analytics applications • ...
  • 147. Changing leaders’ minds
  • 148. Talking pointsfor refining, against redesigning 1. Solve the problem(s) 2. Save money 3. Reduce/end radical organizational changes
  • 149. Solving the problem(s)• Forcing the issue: ban the term “redesign” from discussions• Data-driven definition / prioritization / tuning / opportunism• Creating anchors to keep project from spinning out of control: elevator pitch / mission / vision / goals / KPI
  • 150. This can be very, very helpful Gamestorming by Dave Gray, Sunni Brown, and James Macanufo (O’Reilly, 2010)
  • 151. Saving money• Life by a thousand cuts: small changes have huge impacts (see: Zipf)• Reuse and retain technology investments• Retain institutional knowledge• Get more from your (empowered) team and make it pay for itself• Spend less on external support and fire your agency
  • 152. Reduce/end radicalorganizational changes• End the pendulum swing from centralized to decentralized approaches• Reorganize information, not people• Build self-sustaining, steady in-house capabilities to prioritize and tune
  • 153. Being prepared to fail
  • 154. Sometimes your leadersare in a hurry
  • 155. Sometimes your leadersare not very smart
  • 156. Sometimes your organizationis immature
  • 157. Nurit Peres’ Company UX Maturity Model(http://is.gd/x1dOuP)
  • 158. Renato Feijó’s UX Maturity Model(http://is.gd/dul2t2)
  • 159. Always be ready to gounder the radar
  • 160. Summary: changing your workand your organization Do your work differently 1. Move from time-based projects to ongoing processes 2. Build a balanced, data-driven practice Get your organization to support your work 3. Make friends and allies 4. Change leaders’ minds by • Solving problems • Saving money • Reducing radical change Be prepared to fail
  • 161. Agenda1. The quick intro2. Prioritizing and tuning top-down navigation3. Demo: content modeling4. Prioritizing and tuning contextual navigation5. Group exercise: site search analytics6. Prioritizing and tuning search7. Changing your work and your organization
  • 162. Say hello Lou Rosenfeld lou@louisrosenfeld.com Rosenfeld Media  www.louisrosenfeld.com | @louisrosenfeld www.rosenfeldmedia.com | @rosenfeldmedia