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8 Information Architecture Better Practices
 

8 Information Architecture Better Practices

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Closing keynote at Webvisions Barcelona, 7 July 2012.

Closing keynote at Webvisions Barcelona, 7 July 2012.

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8 Information Architecture Better Practices 8 Information Architecture Better Practices Presentation Transcript

  • 8 better practicesfrom information architectureLou Rosenfeld •  Rosenfeld Media •  rosenfeldmedia.com
  • Hello, my name is Lou www.louisrosenfeld.com | www.rosenfeldmedia.com
  • The state ofcontemporary findability
  • 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
  • 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
  • #1Diagnosing theimportant problems
  • A handful of queries/tasks/ways to navigate/features/ A little goes a long waydocuments meet the needs of your most important audiences
  • 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
  • A handful of queries/tasks/ways to navigate/features/ A little goes a long waydocuments meet the needs of your most important audiences
  • 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
  • A handful of queries/tasks/ways to navigate/features/ A little goes a long waydocuments meet the needs of your most important audiences
  • 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
  • (and the tail is quite long)
  • (and the tail is quite long)
  • (and the tail is quite long)
  • (and the tail is quite long)
  • (and the tail is quite long)
  • The Zipf Curve, textually
  • It’s Zipf’s World;we just live in it A little... • queries • tasks • ways to navigate • features • documents ...goes a long way
  • unverified rumor alert
  • unverified rumor alert90% of Microsoft.com content
  • unverified rumor alert90% of Microsoft.com content has never been accessed...
  • unverified rumor alert90% of Microsoft.com content has never been accessed... not even once
  • unverified rumor alert90% of Microsoft.com content has never been accessed... not even once
  • #2Balancing our evidence
  • from Christian Rohrer: http://is.gd/95HSQ2
  • Balanced research leads to true insight, new opportunitiesfrom Christian Rohrer: http://is.gd/95HSQ2
  • 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)
  • #3Designing for the long-term
  • Stewart Brand’s Pace Layeringmodel Typical design focus Stuff that gets ignored: mission, vision, charter, goals, KPI, objectives
  • #4Measuring engagement
  • Measuringconversions?No problem...
  • ..measuringanything else? Good luck!
  • 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...
  • 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
  • #5Supportingcontextual navigation
  • Contextual navigation:your site’s desire lines
  • 1.Choose a contenttype (e.g., events) 
2.Ask: “Whereshould users gofrom here?”3.Analyze the 
frequent queriesfrom this contenttype from aiga.org 

  • 
 
 
 
 
 
Analyze frequent queries generated from each content sample
  • Content models emerge (example: BBC) concert calendar album pages artist descriptions TV listingsalbum reviews discography artist bios
  • User studies are another greatway to get at content models
  • #6Improving search across silos
  • Reconsidering the search UI...
  • ...convert “advanced” searchinto refinement
  • ...convert “advanced” searchinto refinement search session patterns 1. solar energy 2. how solar energy works
  • ...convert “advanced” searchinto refinement search session patterns 1. solar energy 2. how solar energy works search session patterns 1. solar energy 2. energy
  • ...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
  • ...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
  • ...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
  • Design forspecialized queries(e.g., proper nouns,dates, unique ID#s)
  • ...and design specialized search results
  • ...and design specialized search results
  • ...and design specialized search results
  • Content objects from productcontent model ...and design specialized search results
  • Poor search results returned by search engineContent objects from productcontent model ...and design specialized search results
  • #7Combining design approacheseffectively
  • Yes, manual effort is still asimportant as tools
  • Yes, manual effort is still asimportant as tools Narrow, deep content access
  • Vanguard’s Tax Center is asimple, low-tech, editorial
  • Vanguard’s Tax Center is asimple, low-tech, editorial ...to editorially rich content
  • Manuallyselected results
  • Manuallyselected results ...complement raw results
  • 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
  • #8Tuning designs over time
  • Your site is a moving targetbuilt on moving targets
  • Impact of change on design(queries)
  • Impact of change on design(queries) Interest in the football team: going...
  • Impact of change on design(queries) Interest in the football team: going... ...going...
  • Impact of change on design(queries) Interest in the football team: going... ...going... gone
  • Impact of change on design(queries) Time to Interest in the study! football team: going... ...going... gone
  • IRS before Tax Day
  • Before Tax DayIRS before Tax Day
  • IRS after Tax Day
  • After Tax DayIRS after Tax Day
  • 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
  • Say hello Lou Rosenfeld lou@louisrosenfeld.com Rosenfeld Media  www.louisrosenfeld.com | @louisrosenfeld www.rosenfeldmedia.com | @rosenfeldmedia