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Adaptable Information Architecture
How to say no to your next redesign



   Lou Rosenfeld •  lou@rosenfeldmedia.com

   Rosenfeld Media UX Workshops •  Fall 2012
Hello, my name is Lou




 www.louisrosenfeld.com | www.rosenfeldmedia.com
Agenda
1.    Hello / What is information architecture?
2.    Why redesign should die / The alternatives
3.    Prioritizing and tuning top-down navigation
4.    Break
5.    Exercise: content modeling
6.    Lunch
7.    Prioritizing and tuning contextual navigation
8.    Exercise: site search analytics
9.    Break
10.   Prioritizing and tuning search
11.   Changing your work and your organization / Discussion
What is
information architecture?
Definition



 The art and science of structuring,
 organizing and labeling information
 to help people find and manage it.
Three circles
Three tracks

1. Top-down navigation:
   Anticipates interests/questions at arrival
2. Bottom-up (contextual) navigation:
   Enables answers to emerge
3. Search:
   Handles specific information needs
What is redesign
and why should it die?
Why am I so down on
redesign?
Why am I so down on
redesign?
Redesign is
hollow, meaningless,
and a vanity.


It is the true definition
of insanity.
A story in the Ann Arbor
News
UM was going to redesign its
Gateway
UM was going to redesign its
Gateway
UM was going to redesign its
Gateway
UM was going to redesign its
Gateway
Adaptable Information Workshop slides
$250,0
  00
$250,0
work study students!




  00
$250,0
work study students!




  00     WebObjects!
They even had a ribbon-cutting
This became...
...this
...this
...this
...this
Then they did it
all over again
Then they did it
all over again
and again
Then they did it
all over again
and again
and again
Then they did it
all over again
and again
and again
and again
Where we are today
Where we are today
Where we are today
Where we are today
Where problems are undefined
lies insanity and vanity
Where problems are undefined
lies insanity and vanity
 We attempt the impossible: “boil the ocean”
 in no time at great cost
Where problems are undefined
lies insanity and vanity
 We attempt the impossible: “boil the ocean”
 in no time at great cost
 We believe the unbelievable: unwarranted
 claims from agencies and software vendors
Where problems are undefined
lies insanity and vanity
 We attempt the impossible: “boil the ocean”
 in no time at great cost
 We believe the unbelievable: unwarranted
 claims from agencies and software vendors
 We become irresponsible: unwarranted
 declarations of victory at the expense of
 our teams and users
See the problem differently
Your site is a
complex adaptive system
John Holland:
“A Complex Adaptive System
is a dynamic network of
many agents acting in parallel,
constantly acting and reacting
to what the other agents are
doing.”
Examples of CAS
Examples of CAS
Examples of CAS
Your site is a moving target
built on moving targets
Your site is many sites, products,
things out of your control


 more John Holland:
 “The control of a complex adaptive system
 tends to be highly dispersed and
 decentralized... “The overall behavior of the
 system is the result of a huge number of
 decisions made every moment by many
 individual agents.”
“The perfect is the
enemy of the good.”

Voltaire might
have added:
“Constant change
means never having
to say you’re sorry.”
You can’t redesign
But you must refine

1. Prioritize: Identify the important problems
   regularly
2. Tune: Address those problems regularly
3. Be opportunistic: Look for low-hanging
   fruit
Prioritize because
a little goes a long way
A handful of queries/tasks/ways to navigate/features/
 A little goes a long way
documents meet the needs of your most important audiences
A handful of queries/tasks/ways to navigate/features/
 A little goes a long way
documents meet the needs of your most important audiences
A handful of queries/tasks/ways to navigate/features/
 A little goes a long way
documents meet the needs of your most important audiences
A handful of queries/tasks/ways to navigate/features/
 A little goes a long way
documents meet the needs of your most important audiences
A handful of queries/tasks/ways to navigate/features/
 A little goes a long way
documents meet the needs of your most important audiences
(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)
Zipf in text
A little really does go
a long way
 A handful of...
  •   queries

  •   tasks

  •   ways to navigate

  •   features

  •   documents

 ...meet the needs of your
 most important audiences
Unverified rumor:
90% of Microsoft.com’s content
   has never been accessed
From prioritization...

...to a report card
 (repeat regularly)
Treat your site
        like an onion                                      Each layer is cumulative



            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)     (e.g., UserTesting.com)
                                                              refresh monthly

          tagged by experts      “traditional” lab-based    titled according to
 3           (topical tags)           user testing               guidelines
         deep links to support                             structured according
 4       contextual navigation
                                      A/B testing
                                                                to schema
Be an incrementalist:
tune because things change
From projects to processes:
a regular regimen of design




  Example: the rolling content inventory
Impact of change on design
(queries)
IRS before 4/15
Before
                  April 15
IRS before 4/15
IRS after 4/15
After
                 April 15
IRS after 4/15
Be an opportunist:
look for the low-hanging fruit

1. Top-down navigation:
   Anticipates interests/questions at arrival
2. Bottom-up (contextual) navigation:
   Enables answers to emerge
3. Search:
   Handles specific information needs
Life by a thousand cuts

  50% of users are search dominant
x 5% of all queries are typos, fixed by spell checking.
 2.5% improvement to the UX

  50% of all users are search dominant
x 30% (best bet results for top 100 queries)
  15% improvement to the UX


Ditto for improving content, search results design,
navigation design…
Summary

 Site redesign is wasteful, expensive, and ineffective
  1. You don’t have a single, perfectible site
  2. You do have a collection of living, changing pockets of content
     and functionality
 You can refine
  3. Prioritize the problems that are most important to your users
  4. Regularly address these problems
  5. Identify opportunities to make small improvements that go a
     long way
Prioritizing and Tuning
Top-Down Navigation
The data-driven main page:
Who wants what and when?
Who wants what?
US English speakers
Who wants what?
German speakers
When do they want it?
Commerce sites get it
The IRS gets it
But really, who cares
about the main page?
But really, who cares
about the main page?
The risk of main page fixation




From Tony Dunn’s Tales from Redesignland
(http://redesignland.blogspot.com/)
Focusing on main page =
taking Zipf too far




...plus lots of competition (Google, ads/landing pages)
The tail that wags the dog:
site map drives
improved site hierarchy
Site map by tool, unit, and format
Site map by tool, unit, and format
Site map by tool, unit, and format
User-centered site map
User-centered site map...
Adaptable Information Workshop slides
Asking the possible
from your site index
Specialized site indices
Specialized site indices
Specialized site indices




                   Cisco’s site indices are
                   specialized by content
                   type (products, services)
Best bet-based site indices



              MSU’s site index is built on
              popular information needs
              (based on best bet search results)
Going broad and deep with
guides (AKA microsites)
Kansas main pages loves guides
Kansas main pages loves guides
But the guides need a little
work
Vanguard’s main page loves
guides
Vanguard’s main page loves
guides
The Tax Center is a guide
One more example: IRS
One more example: IRS
...e-filing is presented as
sequential steps
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
Break
Agenda
1.    Hello / What is information architecture?
2.    Why redesign should die / The alternatives
3.    Prioritizing and tuning top-down navigation
4.    Break
5.    Exercise: content modeling
6.    Lunch
7.    Prioritizing and tuning contextual navigation
8.    Exercise: site search analytics
9.    Break
10.   Prioritizing and tuning search
11.   Changing your work and your organization / Discussion
concert calendar




  album pages   artist descriptions
                                            TV listings


    Exercise: Content Modeling

album reviews   discography           artist bios
Lunch
Agenda
1.    Hello / What is information architecture?
2.    Why redesign should die / The alternatives
3.    Prioritizing and tuning top-down navigation
4.    Break
5.    Exercise: content modeling
6.    Lunch
7.    Prioritizing and tuning contextual navigation
8.    Exercise: site search analytics
9.    Break
10.   Prioritizing and tuning search
11.   Changing your work and your organization / Discussion
Prioritizing and Tuning
Contextual Navigation
Establishing Desire Lines




Use
     Content modeling
 •   Site search analytics
Where do searches begin?
Not just the main
page, according to a
User Interface
Engineering study

(http://is.gd/j1NHeS)
Using site search analytics
to identify desire lines
Choose a
common content
type (e.g., events)
                      





Where should              



users go from here?



                              






                             





                                                         

                                 





                                     
                   





Analyze frequent queries generated from each content sample


        








                                                      




               





Develop logic that automatically links an event to:
1. articles that share the event’s topic
2. events that share the topic but have different
geographic locales
What content types
should we be connecting?
Important content types emerge
 from content modeling                    concert calendar




  album pages     artist descriptions
                                              TV listings




album reviews     discography           artist bios
Using SSA to prioritize content
types
Getting content types out of
site 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%
What should we use to
connect content types?
Which metadata attributes will your
content model depend upon?
More on prioritizing metadata attributes
Prioritizing semantic relationships
How do we
prioritize content?
Some content value variables



               I
Some content value variables



               I
 Usability
Popularity
Credibility
Some content value variables
                             Currency
                             Freshness
                             Authority
                        Follows guidelines
                            (e.g., titling,
               I             metadata)
 Usability
Popularity
Credibility
Some content value variables
                                  Currency
                                  Freshness
                                  Authority
                             Follows guidelines
                                 (e.g., titling,
               I                  metadata)
 Usability
Popularity
Credibility


                           Strategic value
                       Addresses compliance
                    issues (e.g., Sarbanes/Oxley)
                     Content owners are good
                               partners
Subjectively “grade” your content’s value
1.Choose
appropriate value
criteria for each
content area
2.Weight criteria
(total = 100%)
3.Subjectively grade
for each criterion
4.weight x grade
= score
5.Add scores for
overall score
Subjectively “grade” your content’s value
1.Choose                            Subjective
appropriate value                  assessment
criteria for each
content area
2.Weight criteria
(total = 100%)
3.Subjectively grade
for each criterion
4.weight x grade
= score
5.Add scores for
overall score
Put the grades together for a more
objective “report card”




  Helps prioritize content migrations, refreshes, ...
Put the grades together for a more
objective “report card”
                               Objectifies subjective
                                   assessments




  Helps prioritize content migrations, refreshes, ...
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
Agenda
1.    Hello / What is information architecture?
2.    Why redesign should die / The alternatives
3.    Prioritizing and tuning top-down navigation
4.    Break
5.    Exercise: content modeling
6.    Lunch
7.    Prioritizing and tuning contextual navigation
8.    Exercise: site search analytics
9.    Break
10.   Prioritizing and tuning search
11.   Changing your work and your organization / Discussion
Exercise: site search analytics
Break
Agenda
1.    Hello / What is information architecture?
2.    Why redesign should die / The alternatives
3.    Prioritizing and tuning top-down navigation
4.    Break
5.    Exercise: content modeling
6.    Lunch
7.    Prioritizing and tuning contextual navigation
8.    Exercise: site search analytics
9.    Break
10.   Prioritizing and tuning search
11.   Changing your work and your organization / Discussion
Prioritizing and Tuning Search
Make “the Box” accommodate
most searchers’ queries
How long are our queries?


  Top 500 queries
 (37% of all traffic)
Mean = 10.6 characters
Median = 10 characters
Mean = 10.6 characters
Median = 10 characters

Long tail queries likely longer
Mean = 10.6 characters
Median = 10 characters

Long tail queries likely longer

Top queries often in low 20s



                                  

Mean = 10.6 characters
Median = 10 characters

Long tail queries likely longer

Top queries often in low 20s

Desired: @30 characters;
Can you get that many?
                                  

Mean = 10.6 characters
Median = 10 characters

Long tail queries likely longer

Top queries often in low 20s

Desired: @30 characters;
Can you get that many?
                                  

Safe: @15-20 characters
We’ve seen this before:
auto-completing queries
Auto-completing from a
known, common items (e.g.,
Auto-completing from a
known, common items (e.g.,
         Uses known terms:
         e.g., movie titles and
         actor/director names
Auto-completing from queries
Uses common queries
Auto-completing from queries
Auto-completing from best
bets
Auto-completing from best
bets
          Uses best bets
Making change easy:
supporting query refinement
The absolute
meaninglessness
      of
advanced search
The absolute
                                     meaninglessness
                                           of
                                     advanced search




                                              



At University of Alaska-Fairbanks,
advanced = expanded search
The absolute
                                     meaninglessness
                                           of
                                     advanced search




                                                      



At University of Alaska-Fairbanks,
advanced = expanded search



                                            At the IRS,
                                           advanced =
                                      narrowed search



                                                          

Contextualizing “advanced” features
Look to session data for
progression and context
Look to session data for
progression and context
  search session patterns
  1. solar energy
  2. how solar energy works
Look to session data for
progression and context
  search session patterns
  1. solar energy
  2. how solar energy works




 search session patterns
 1. solar energy
 2. energy
Look to session data for
progression 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
Look to session data for
progression 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
Look to session data for
progression 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
Improving performance for
specialized queries
Recognizing proper nouns,
dates, and unique ID#s
Surfacing specialized content
types in search results
Tuning Search Results:
Handling specialized answers
Tuning Search Results:
Handling specialized answers
Tuning Search Results:
Handling specialized answers
Tuning Search Results:
    Handling specialized answers




“Product quick links” come directly from product content model
These results are a strong counterbalance to raw results
When raw isn’t good enough:
best bet search results
Adaptable Information Workshop slides
best bet #1
best bet #1


best bet #2
best bet #1


best bet #2


even more
 best bets
best bet #1


best bet #2


even more
 best bets




raw results
best bet #1


best bet #2


even more
 best bets




raw results
best bet #1


 best bet #2


  even more
   best bets


competition


 raw results
best bet #1


 best bet #2


  even more
   best bets


competition
  danger?

 raw results
best bet #1


 best bet #2


  even more
   best bets


competition
  danger?
   data
 raw results
The 0 search results page:
search’s equivalent of the 404
Tuning Search Results:
    0 results pages



Not helpful
Tuning Search Results:
    0 results pages



Not helpful

Much better:
 “Did you
mean?” and
  Popular
 Searches
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
Changing your work
and your organization
Doing your work differently



1. Processes, not projects
2. Rebalancing your research and design
From time-boxed projects
to ongoing processes




  Example: the rolling content inventory
What else can roll?
 Each week, for example...
  •   Analyze analytics for trends

  •   Task analysis of common needs
 Each month...
  •   User survey

  •   Exploratory analysis of analytics data
 Each quarter...
  •   Field study

  •   Card sorting
Build a practice that’s
balanced and data-driven
User Research Landscape




from Christian Rohrer: http://is.gd/95HSQ2
User Research Landscape

                                   Ongoing coverage
                                    of each of these
                                      4 quadrants




from Christian Rohrer: http://is.gd/95HSQ2
A balanced research regimen
 Each week...
  •   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)
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)
Getting your organization
to support your work


1. Making friends and allies
2. Changing your leaders’ minds
Making friends and allies
Showing content owners
how their content performs
Showing content owners
how their content performs
Helping marketing
develop better messaging

Jargon 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
Helping IT say “no” with authority


Reduce pressure to solve problems with technologies by
 making what we have work
Minimize radical changes to platforms
 • Enterprise search
 • Content management systems
 • Analytics applications
 • ...
Changing leaders’ minds
Talking points
for refining, against redesigning


 1. Solve the problem(s)
 2. Save money
 3. Reduce/end radical organizational changes
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
Steward Brand’s Pace Layering
model              Typical design
                       focus




                     Stuff that gets ignored:
                     mission, vision, charter,
                      goals, KPI, objectives
Example of an anchor:
your elevator pitch




  Read Gamestorming (Gray, Brown,
  Macanufo); O’Reilly, 2010).
  http://amzn.to/nnpERG
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
Reduce/end radical
organizational 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
Being prepared to fail
Sometimes your leaders
are in a hurry
Sometimes your leaders
are not very smart
Sometimes your organization
is immature
Nurit Peres’ Company UX Maturity Model
(http://is.gd/x1dOuP)
Renato Feijó’s UX Maturity Model
(http://is.gd/dul2t2)
Always be ready to go
under the radar
Summary: changing your work
and 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
Discussion
Agenda
1.    Hello / What is information architecture?
2.    Why redesign should die / The alternatives
3.    Prioritizing and tuning top-down navigation
4.    Break
5.    Exercise: content modeling
6.    Lunch
7.    Prioritizing and tuning contextual navigation
8.    Exercise: site search analytics
9.    Break
10.   Prioritizing and tuning search
11.   Changing your work and your organization / Discussion
Say hello



 Lou Rosenfeld
 lou@louisrosenfeld.com
 Rosenfeld Media 
 www.louisrosenfeld.com | @louisrosenfeld
 www.rosenfeldmedia.com | @rosenfeldmedia

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Adaptable Information Workshop slides

  • 1. Adaptable Information Architecture How to say no to your next redesign Lou Rosenfeld •  lou@rosenfeldmedia.com Rosenfeld Media UX Workshops •  Fall 2012
  • 2. Hello, my name is Lou www.louisrosenfeld.com | www.rosenfeldmedia.com
  • 3. Agenda 1. Hello / What is information architecture? 2. Why redesign should die / The alternatives 3. Prioritizing and tuning top-down navigation 4. Break 5. Exercise: content modeling 6. Lunch 7. Prioritizing and tuning contextual navigation 8. Exercise: site search analytics 9. Break 10. Prioritizing and tuning search 11. Changing your work and your organization / Discussion
  • 5. Definition The art and science of structuring, organizing and labeling information to help people find and manage it.
  • 7. Three tracks 1. Top-down navigation: Anticipates interests/questions at arrival 2. Bottom-up (contextual) navigation: Enables answers to emerge 3. Search: Handles specific information needs
  • 8. What is redesign and why should it die?
  • 9. Why am I so down on redesign?
  • 10. Why am I so down on redesign?
  • 11. Redesign is hollow, meaningless, and a vanity. It is the true definition of insanity.
  • 12. A story in the Ann Arbor News
  • 13. UM was going to redesign its Gateway
  • 14. UM was going to redesign its Gateway
  • 15. UM was going to redesign its Gateway
  • 16. UM was going to redesign its Gateway
  • 20. $250,0 work study students! 00 WebObjects!
  • 21. They even had a ribbon-cutting
  • 27. Then they did it all over again
  • 28. Then they did it all over again and again
  • 29. Then they did it all over again and again and again
  • 30. Then they did it all over again and again and again and again
  • 31. Where we are today
  • 32. Where we are today
  • 33. Where we are today
  • 34. Where we are today
  • 35. Where problems are undefined lies insanity and vanity
  • 36. Where problems are undefined lies insanity and vanity We attempt the impossible: “boil the ocean” in no time at great cost
  • 37. Where problems are undefined lies insanity and vanity We attempt the impossible: “boil the ocean” in no time at great cost We believe the unbelievable: unwarranted claims from agencies and software vendors
  • 38. Where problems are undefined lies insanity and vanity We attempt the impossible: “boil the ocean” in no time at great cost We believe the unbelievable: unwarranted claims from agencies and software vendors We become irresponsible: unwarranted declarations of victory at the expense of our teams and users
  • 39. See the problem differently
  • 40. Your site is a complex adaptive system John Holland: “A Complex Adaptive System is a dynamic network of many agents acting in parallel, constantly acting and reacting to what the other agents are doing.”
  • 44. Your site is a moving target built on moving targets
  • 45. Your site is many sites, products, things out of your control more John Holland: “The control of a complex adaptive system tends to be highly dispersed and decentralized... “The overall behavior of the system is the result of a huge number of decisions made every moment by many individual agents.”
  • 46. “The perfect is the enemy of the good.” Voltaire might have added: “Constant change means never having to say you’re sorry.”
  • 47. You can’t redesign But you must refine 1. Prioritize: Identify the important problems regularly 2. Tune: Address those problems regularly 3. Be opportunistic: Look for low-hanging fruit
  • 48. Prioritize because a little goes a long way
  • 49. A handful of queries/tasks/ways to navigate/features/ A little goes a long way documents meet the needs of your most important audiences
  • 50. A handful of queries/tasks/ways to navigate/features/ A little goes a long way documents meet the needs of your most important audiences
  • 51. A handful of queries/tasks/ways to navigate/features/ A little goes a long way documents meet the needs of your most important audiences
  • 52. A handful of queries/tasks/ways to navigate/features/ A little goes a long way documents meet the needs of your most important audiences
  • 53. A handful of queries/tasks/ways to navigate/features/ A little goes a long way documents meet the needs of your most important audiences
  • 54. (and the tail is quite long)
  • 55. (and the tail is quite long)
  • 56. (and the tail is quite long)
  • 57. (and the tail is quite long)
  • 58. (and the tail is quite long)
  • 60. A little really does go a long way A handful of... • queries • tasks • ways to navigate • features • documents ...meet the needs of your most important audiences
  • 61. Unverified rumor: 90% of Microsoft.com’s content has never been accessed
  • 62. From prioritization... ...to a report card (repeat regularly)
  • 63. Treat your site like an onion Each layer is cumulative 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) (e.g., UserTesting.com) refresh monthly tagged by experts “traditional” lab-based titled according to 3 (topical tags) user testing guidelines deep links to support structured according 4 contextual navigation A/B testing to schema
  • 64. Be an incrementalist: tune because things change
  • 65. From projects to processes: a regular regimen of design Example: the rolling content inventory
  • 66. Impact of change on design (queries)
  • 68. Before April 15 IRS before 4/15
  • 70. After April 15 IRS after 4/15
  • 71. Be an opportunist: look for the low-hanging fruit 1. Top-down navigation: Anticipates interests/questions at arrival 2. Bottom-up (contextual) navigation: Enables answers to emerge 3. Search: Handles specific information needs
  • 72. Life by a thousand cuts 50% of users are search dominant x 5% of all queries are typos, fixed by spell checking. 2.5% improvement to the UX 50% of all users are search dominant x 30% (best bet results for top 100 queries) 15% improvement to the UX Ditto for improving content, search results design, navigation design…
  • 73. Summary Site redesign is wasteful, expensive, and ineffective 1. You don’t have a single, perfectible site 2. You do have a collection of living, changing pockets of content and functionality You can refine 3. Prioritize the problems that are most important to your users 4. Regularly address these problems 5. Identify opportunities to make small improvements that go a long way
  • 75. The data-driven main page: Who wants what and when?
  • 76. Who wants what? US English speakers
  • 78. When do they want it?
  • 81. But really, who cares about the main page?
  • 82. But really, who cares about the main page?
  • 83. The risk of main page fixation From Tony Dunn’s Tales from Redesignland (http://redesignland.blogspot.com/)
  • 84. Focusing on main page = taking Zipf too far ...plus lots of competition (Google, ads/landing pages)
  • 85. The tail that wags the dog: site map drives improved site hierarchy
  • 86. Site map by tool, unit, and format
  • 87. Site map by tool, unit, and format
  • 88. Site map by tool, unit, and format
  • 91. Asking the possible from your site index
  • 94. Specialized site indices Cisco’s site indices are specialized by content type (products, services)
  • 95. Best bet-based site indices MSU’s site index is built on popular information needs (based on best bet search results)
  • 96. Going broad and deep with guides (AKA microsites)
  • 97. Kansas main pages loves guides
  • 98. Kansas main pages loves guides
  • 99. But the guides need a little work
  • 100. Vanguard’s main page loves guides
  • 101. Vanguard’s main page loves guides
  • 102. The Tax Center is a guide
  • 105. ...e-filing is presented as sequential steps
  • 106. 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
  • 107. Break
  • 108. Agenda 1. Hello / What is information architecture? 2. Why redesign should die / The alternatives 3. Prioritizing and tuning top-down navigation 4. Break 5. Exercise: content modeling 6. Lunch 7. Prioritizing and tuning contextual navigation 8. Exercise: site search analytics 9. Break 10. Prioritizing and tuning search 11. Changing your work and your organization / Discussion
  • 109. concert calendar album pages artist descriptions TV listings Exercise: Content Modeling album reviews discography artist bios
  • 110. Lunch
  • 111. Agenda 1. Hello / What is information architecture? 2. Why redesign should die / The alternatives 3. Prioritizing and tuning top-down navigation 4. Break 5. Exercise: content modeling 6. Lunch 7. Prioritizing and tuning contextual navigation 8. Exercise: site search analytics 9. Break 10. Prioritizing and tuning search 11. Changing your work and your organization / Discussion
  • 113. Establishing Desire Lines Use Content modeling • Site search analytics
  • 114. Where do searches begin? Not just the main page, according to a User Interface Engineering study (http://is.gd/j1NHeS)
  • 115. Using site search analytics to identify desire lines
  • 116. Choose a common content type (e.g., events) 
 Where should 
 users go from here? 

  • 117. 
 
 
 
 
 Analyze frequent queries generated from each content sample
  • 118. 
 

  • 119. 
 
 Develop logic that automatically links an event to: 1. articles that share the event’s topic 2. events that share the topic but have different geographic locales
  • 120. What content types should we be connecting?
  • 121. Important content types emerge from content modeling concert calendar album pages artist descriptions TV listings album reviews discography artist bios
  • 122. Using SSA to prioritize content types
  • 123. Getting content types out of site 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%
  • 124. What should we use to connect content types?
  • 125. Which metadata attributes will your content model depend upon?
  • 126. More on prioritizing metadata attributes
  • 128. How do we prioritize content?
  • 129. Some content value variables I
  • 130. Some content value variables I Usability Popularity Credibility
  • 131. Some content value variables Currency Freshness Authority Follows guidelines (e.g., titling, I metadata) Usability Popularity Credibility
  • 132. Some content value variables Currency Freshness Authority Follows guidelines (e.g., titling, I metadata) Usability Popularity Credibility Strategic value Addresses compliance issues (e.g., Sarbanes/Oxley) Content owners are good partners
  • 133. Subjectively “grade” your content’s value 1.Choose appropriate value criteria for each content area 2.Weight criteria (total = 100%) 3.Subjectively grade for each criterion 4.weight x grade = score 5.Add scores for overall score
  • 134. Subjectively “grade” your content’s value 1.Choose Subjective appropriate value assessment criteria for each content area 2.Weight criteria (total = 100%) 3.Subjectively grade for each criterion 4.weight x grade = score 5.Add scores for overall score
  • 135. Put the grades together for a more objective “report card” Helps prioritize content migrations, refreshes, ...
  • 136. Put the grades together for a more objective “report card” Objectifies subjective assessments Helps prioritize content migrations, refreshes, ...
  • 137. 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
  • 138. Agenda 1. Hello / What is information architecture? 2. Why redesign should die / The alternatives 3. Prioritizing and tuning top-down navigation 4. Break 5. Exercise: content modeling 6. Lunch 7. Prioritizing and tuning contextual navigation 8. Exercise: site search analytics 9. Break 10. Prioritizing and tuning search 11. Changing your work and your organization / Discussion
  • 139. Exercise: site search analytics
  • 140. Break
  • 141. Agenda 1. Hello / What is information architecture? 2. Why redesign should die / The alternatives 3. Prioritizing and tuning top-down navigation 4. Break 5. Exercise: content modeling 6. Lunch 7. Prioritizing and tuning contextual navigation 8. Exercise: site search analytics 9. Break 10. Prioritizing and tuning search 11. Changing your work and your organization / Discussion
  • 143. Make “the Box” accommodate most searchers’ queries
  • 144. How long are our queries? Top 500 queries (37% of all traffic)
  • 145. Mean = 10.6 characters Median = 10 characters
  • 146. Mean = 10.6 characters Median = 10 characters Long tail queries likely longer
  • 147. Mean = 10.6 characters Median = 10 characters Long tail queries likely longer Top queries often in low 20s 

  • 148. Mean = 10.6 characters Median = 10 characters Long tail queries likely longer Top queries often in low 20s Desired: @30 characters; Can you get that many? 

  • 149. Mean = 10.6 characters Median = 10 characters Long tail queries likely longer Top queries often in low 20s Desired: @30 characters; Can you get that many? 
 Safe: @15-20 characters
  • 150. We’ve seen this before: auto-completing queries
  • 151. Auto-completing from a known, common items (e.g.,
  • 152. Auto-completing from a known, common items (e.g., Uses known terms: e.g., movie titles and actor/director names
  • 157. Making change easy: supporting query refinement
  • 158. The absolute meaninglessness of advanced search
  • 159. The absolute meaninglessness of advanced search 
 At University of Alaska-Fairbanks, advanced = expanded search
  • 160. The absolute meaninglessness of advanced search 
 At University of Alaska-Fairbanks, advanced = expanded search At the IRS, advanced = narrowed search 

  • 162. Look to session data for progression and context
  • 163. Look to session data for progression and context search session patterns 1. solar energy 2. how solar energy works
  • 164. Look to session data for progression and context search session patterns 1. solar energy 2. how solar energy works search session patterns 1. solar energy 2. energy
  • 165. Look to session data for progression 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
  • 166. Look to session data for progression 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
  • 167. Look to session data for progression 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
  • 171. Tuning Search Results: Handling specialized answers
  • 172. Tuning Search Results: Handling specialized answers
  • 173. Tuning Search Results: Handling specialized answers
  • 174. Tuning Search Results: Handling specialized answers “Product quick links” come directly from product content model These results are a strong counterbalance to raw results
  • 175. When raw isn’t good enough: best bet search results
  • 178. best bet #1 best bet #2
  • 179. best bet #1 best bet #2 even more best bets
  • 180. best bet #1 best bet #2 even more best bets raw results
  • 181. best bet #1 best bet #2 even more best bets raw results
  • 182. best bet #1 best bet #2 even more best bets competition raw results
  • 183. best bet #1 best bet #2 even more best bets competition danger? raw results
  • 184. best bet #1 best bet #2 even more best bets competition danger? data raw results
  • 185. The 0 search results page: search’s equivalent of the 404
  • 186. Tuning Search Results: 0 results pages Not helpful
  • 187. Tuning Search Results: 0 results pages Not helpful Much better: “Did you mean?” and Popular Searches
  • 188. 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
  • 189. Changing your work and your organization
  • 190. Doing your work differently 1. Processes, not projects 2. Rebalancing your research and design
  • 191. From time-boxed projects to ongoing processes Example: the rolling content inventory
  • 192. What else can roll? Each week, for example... • Analyze analytics for trends • Task analysis of common needs Each month... • User survey • Exploratory analysis of analytics data Each quarter... • Field study • Card sorting
  • 193. Build a practice that’s balanced and data-driven
  • 194. User Research Landscape from Christian Rohrer: http://is.gd/95HSQ2
  • 195. User Research Landscape Ongoing coverage of each of these 4 quadrants from Christian Rohrer: http://is.gd/95HSQ2
  • 196. A balanced research regimen Each week... • 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)
  • 197. 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)
  • 198. Getting your organization to support your work 1. Making friends and allies 2. Changing your leaders’ minds
  • 200. Showing content owners how their content performs
  • 201. Showing content owners how their content performs
  • 202. Helping marketing develop better messaging Jargon 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
  • 203. Helping IT say “no” with authority Reduce pressure to solve problems with technologies by making what we have work Minimize radical changes to platforms • Enterprise search • Content management systems • Analytics applications • ...
  • 205. Talking points for refining, against redesigning 1. Solve the problem(s) 2. Save money 3. Reduce/end radical organizational changes
  • 206. 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
  • 207. Steward Brand’s Pace Layering model Typical design focus Stuff that gets ignored: mission, vision, charter, goals, KPI, objectives
  • 208. Example of an anchor: your elevator pitch Read Gamestorming (Gray, Brown, Macanufo); O’Reilly, 2010). http://amzn.to/nnpERG
  • 209. 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
  • 210. Reduce/end radical organizational 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
  • 213. Sometimes your leaders are not very smart
  • 215. Nurit Peres’ Company UX Maturity Model (http://is.gd/x1dOuP)
  • 216. Renato Feijó’s UX Maturity Model (http://is.gd/dul2t2)
  • 217. Always be ready to go under the radar
  • 218. Summary: changing your work and 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
  • 220. Agenda 1. Hello / What is information architecture? 2. Why redesign should die / The alternatives 3. Prioritizing and tuning top-down navigation 4. Break 5. Exercise: content modeling 6. Lunch 7. Prioritizing and tuning contextual navigation 8. Exercise: site search analytics 9. Break 10. Prioritizing and tuning search 11. Changing your work and your organization / Discussion
  • 221. Say hello Lou Rosenfeld lou@louisrosenfeld.com Rosenfeld Media  www.louisrosenfeld.com | @louisrosenfeld www.rosenfeldmedia.com | @rosenfeldmedia

Notes de l'éditeur

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  8. Need to make strong point of context of large orgs\n
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  10. Explain what I mean by redesign\n
  11. MICHIGAN STORY SHOULD BE SHORTER\nALSO, TRY TO COME UP WITH A NON-ACADEMIC SITE AS SHORTER EXAMPLES (MICHIGAN AS DEEP DIVE)\n
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  41. Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  42. Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  43. Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  44. Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
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  52. More great illustrations by Eva-Lotta Lamm\n
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  122. 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
  123. 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
  124. 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
  125. 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
  126. 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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