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

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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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  • Design to Refine: Developing a tunable information architecture

    1. 1. Design to RefineDeveloping a tunable information architectureLou Rosenfeld •  Rosenfeld Media •  rosenfeldmedia.com London •  14 April 2011
    2. 2. Hello, my name is Lou
    3. 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. 4. I’ve already dissed redesignSee the slides here:http://www.slideshare.net/lrosenfeld/
    5. 5. The alternatives to redesign1. Prioritize: Identify the important problems regularly2. Tune: Address those problems regularly3. Be opportunistic: Look for low-hanging fruit
    6. 6. Prioritize becausea little goes a long way
    7. 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. 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. 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. 10. Zipf in text
    11. 11. Report card for essential wants and needs
    12. 12. Be an incrementalist:tune because things change
    13. 13. From projects to processes:a regular regimen of design Example: the rolling content inventory
    14. 14. Impact of change on design(queries)
    15. 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. 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. 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. 18. Prioritizing and TuningTop-Down Navigation
    19. 19. The data-driven main page:Who wants what and when?
    20. 20. Who wants what?US English speakers
    21. 21. Who wants what?German speakers
    22. 22. When do they want it?
    23. 23. Commerce sites get it
    24. 24. The IRS gets it
    25. 25. But really, who caresabout the main page?
    26. 26. But really, who caresabout the main page?
    27. 27. The risk of main page fixationFrom Tony Dunn’s Tales from Redesignland(http://redesignland.blogspot.com/)
    28. 28. Focusing on main page =taking Zipf too far...plus lots of competition (Google, ads/landing pages)
    29. 29. The tail that wags the dog:site map drivesimproved site hierarchy
    30. 30. Site map by tool, unit, and format
    31. 31. Site map by tool, unit, and format
    32. 32. Site map by tool, unit, and format
    33. 33. User-centered site mapUser-centered site map...
    34. 34. Asking the possiblefrom your site index
    35. 35. Specialized site indices
    36. 36. Specialized site indices
    37. 37. Specialized site indices Cisco’s site indices are specialized by content type (products, services)
    38. 38. Best bet-based site indices MSU’s site index is built on popular information needs (based on best bet search results)
    39. 39. Going broad and deep withguides (AKA microsites)
    40. 40. Vanguard’s main page lovesguides
    41. 41. Vanguard’s main page lovesguides
    42. 42. The Tax Center is a guide
    43. 43. One more example: IRS
    44. 44. One more example: IRS
    45. 45. ...e-filing is presented assequential steps
    46. 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. 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. 48. concert calendar album pages artist descriptions TV listings Demonstration: Content Modelingalbum reviews discography artist bios
    49. 49. What are the common content objects in your site? album pages artist bios artist descriptions album reviews 53
    50. 50. How do they fit together? concert calendar album pages artist descriptions TV listingsalbum reviews discography artist bios
    51. 51. What content objects are missing? concert calendar And how do they fit? album pages artist descriptions TV listingsalbum reviews discography artist bios
    52. 52. Where do you start? concert calendar album pages artist descriptions TV listingsalbum reviews discography artist bios
    53. 53. How will you connect those objects?
    54. 54. Use content modelsfor content that’s... Homogeneous High-volume High importance What’s the most important deep content in your site?
    55. 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. 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. 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. 58. Prioritizing and TuningContextual Navigation
    59. 59. Establishing Desire LinesUse Content modeling • Site search analytics
    60. 60. Where do searches begin?Not just the mainpage, according to aUser InterfaceEngineering study(http://is.gd/j1NHeS)
    61. 61. Using site search analyticsto identify desire lines
    62. 62. Choose acommon contenttype (e.g., events) !Where should !users go from here? !
    63. 63. ! ! ! ! ! !Analyze frequent queries generated from each content sample
    64. 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. 65. What content typesshould we be connecting?
    66. 66. Important content types emerge from content modeling concert calendar album pages artist descriptions TV listingsalbum reviews discography artist bios
    67. 67. Using SSA to prioritize contenttypes
    68. 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. 69. What should we use toconnect content types?
    70. 70. Which metadata attributes will yourcontent model depend upon?
    71. 71. More on prioritizing metadata attributes
    72. 72. Prioritizing semantic relationships
    73. 73. How do weprioritize content?
    74. 74. Some content value variables I
    75. 75. Some content value variables I UsabilityPopularityCredibility
    76. 76. Some content value variables Currency Freshness Authority Follows guidelines (e.g., titling, I metadata) UsabilityPopularityCredibility
    77. 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. 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. 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. 80. Put the grades together for a moreobjective “report card” Helps prioritize content migrations, refreshes, ...
    81. 81. Put the grades together for a moreobjective “report card” Objectifies subjective assessments Helps prioritize content migrations, refreshes, ...
    82. 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. 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. 84. Group exercise:Site search analytics
    85. 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. 86. Prioritizing and Tuning Search
    87. 87. Make “the Box” accommodatemost searchers’ queries
    88. 88. How long are our queries? Top 500 queries (37% of all traffic)
    89. 89. Mean = 10.6 charactersMedian = 10 characters
    90. 90. Mean = 10.6 charactersMedian = 10 charactersLong tail queries likely longer
    91. 91. Mean = 10.6 charactersMedian = 10 charactersLong tail queries likely longerTop queries often in low 20s !
    92. 92. Mean = 10.6 charactersMedian = 10 charactersLong tail queries likely longerTop queries often in low 20sDesired: @30 characters;Can you get that many? !
    93. 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. 94. We’ve seen this before:auto-completing queries
    95. 95. Auto-completing from aknown, common items (e.g.,
    96. 96. Auto-completing from aknown, common items (e.g., Uses known terms: e.g., movie titles and actor/director names
    97. 97. Auto-completing from queries
    98. 98. Uses common queriesAuto-completing from queries
    99. 99. Auto-completing from bestbets
    100. 100. Auto-completing from bestbets Uses best bets
    101. 101. Making change easy:supporting query refinement
    102. 102. The absolutemeaninglessness ofadvanced search
    103. 103. The absolute meaninglessness of advanced search !At University of Alaska-Fairbanks,advanced = expanded search
    104. 104. The absolute meaninglessness of advanced search !At University of Alaska-Fairbanks,advanced = expanded search At the IRS, advanced = narrowed search !
    105. 105. Contextualizing “advanced” features
    106. 106. Look to session data forprogression and context
    107. 107. Look to session data forprogression and context search session patterns 1. solar energy 2. how solar energy works
    108. 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. 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. 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. 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. 112. Improving performance forspecialized queries
    113. 113. Recognizing proper nouns,dates, and unique ID#s
    114. 114. Surfacingspecialized content typesin search results
    115. 115. Tuning Search Results:Handling specialized answers
    116. 116. Tuning Search Results:Handling specialized answers
    117. 117. Tuning Search Results:Handling specialized answers
    118. 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. 119. When raw isn’t good enough:best bet search results
    120. 120. best bet #1
    121. 121. best bet #1best bet #2
    122. 122. best bet #1best bet #2even more best bets
    123. 123. best bet #1best bet #2even more best betsraw results
    124. 124. best bet #1best bet #2even more best betsraw results
    125. 125. best bet #1 best bet #2 even more best betscompetition raw results
    126. 126. best bet #1 best bet #2 even more best betscompetition danger? raw results
    127. 127. best bet #1 best bet #2 even more best betscompetition danger? data raw results
    128. 128. The 0 search results page:search’s equivalent of the 404
    129. 129. Tuning Search Results: 0 results pagesNot helpful
    130. 130. Tuning Search Results: 0 results pagesNot helpfulMuch better: “Did youmean?” and Popular Searches
    131. 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. 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. 133. Changing your workand your organization
    134. 134. Doing your work differently1. Processes, not projects2. Rebalancing your research and design
    135. 135. From time-boxed projectsto ongoing processes Example: the rolling content inventory
    136. 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. 137. Build a practice that’sbalanced and data-driven
    138. 138. User Research Landscapefrom Christian Rohrer: http://is.gd/95HSQ2
    139. 139. User Research Landscape Ongoing coverage of each of these 4 quadrantsfrom Christian Rohrer: http://is.gd/95HSQ2
    140. 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. 141. Getting your organizationto support your work1. Making friends and allies2. Changing your leaders’ minds
    142. 142. Making friends and allies
    143. 143. Showing content ownershow their content performs
    144. 144. Showing content ownershow their content performs
    145. 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. 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. 147. Changing leaders’ minds
    148. 148. Talking pointsfor refining, against redesigning 1. Solve the problem(s) 2. Save money 3. Reduce/end radical organizational changes
    149. 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. 150. This can be very, very helpful Gamestorming by Dave Gray, Sunni Brown, and James Macanufo (O’Reilly, 2010)
    151. 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. 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. 153. Being prepared to fail
    154. 154. Sometimes your leadersare in a hurry
    155. 155. Sometimes your leadersare not very smart
    156. 156. Sometimes your organizationis immature
    157. 157. Nurit Peres’ Company UX Maturity Model(http://is.gd/x1dOuP)
    158. 158. Renato Feijó’s UX Maturity Model(http://is.gd/dul2t2)
    159. 159. Always be ready to gounder the radar
    160. 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. 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. 162. Say hello Lou Rosenfeld lou@louisrosenfeld.com Rosenfeld Media  www.louisrosenfeld.com | @louisrosenfeld www.rosenfeldmedia.com | @rosenfeldmedia

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