3. Agenda
1. The quick intro
2. Prioritizing and tuning top-down navigation
3. Demo: content modeling
4. Prioritizing and tuning contextual navigation
5. Group exercise: site search analytics
6. Prioritizing and tuning search
7. Changing your work and your organization
4. I’ve already dissed redesign
See the slides here:
http://www.slideshare.net/lrosenfeld/
5. The alternatives to redesign
1. Prioritize: Identify the important problems
regularly
2. Tune: Address those problems regularly
3. Be opportunistic: Look for low-hanging
fruit
7. A handful of your queries/ways to navigate/documents meet
A little data goes a long way
the 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 way
the 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 way
the needs of the few audiences that use your site most
LOVE IT
LEAVE IT
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
16. 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…
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
47. 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
48. Agenda
1. The quick intro
2. Prioritizing and tuning top-down navigation
3. Demo: content modeling
4. Prioritizing and tuning contextual navigation
5. Group exercise: site search analytics
6. Prioritizing and tuning search
7. Changing your work and your organization
49. concert calendar
album pages artist descriptions
TV listings
Demonstration:
Content Modeling
album reviews discography artist bios
50. What are the common content objects in your site?
album pages artist bios
artist descriptions
album reviews
53
51. How do they fit together? concert calendar
album pages artist descriptions
TV listings
album reviews discography artist bios
52. What content objects are missing? concert calendar
And how do they fit?
album pages artist descriptions
TV listings
album reviews discography artist bios
53. Where do you start? concert calendar
album pages artist descriptions
TV listings
album reviews discography artist bios
55. Use content models
for content that’s...
Homogeneous
High-volume
High importance
What’s the most important deep content in
your site?
56. Use content models
when you need to...
Incorporate user research into your
deep content
Improve contextual navigation
Identify missing content
Prioritize metadata choices
Really benefit from your CMS
57. Steps for developing
content models
1. 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?)
58. Agenda
1. The quick intro
2. Prioritizing and tuning top-down navigation
3. Demo: content modeling
4. Prioritizing and tuning contextual navigation
5. Group exercise: site search analytics
6. Prioritizing and tuning search
7. Changing your work and your organization
64. !
!
!
!
! !
Analyze frequent queries generated from each content sample
65. !
!
!
Can you type these queries to improve your
content model?
Link events to:
• the site’s articles on the event’s topic
• info on locales for each event
67. Important content types emerge
from content modeling concert calendar
album pages artist descriptions
TV listings
album reviews discography artist bios
69. 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%
#6) news/announcements: 4.27%
77. Some content value variables
Currency
Freshness
Authority
Follows guidelines
(e.g., titling,
I metadata)
Usability
Popularity
Credibility
78. 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
79. 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
80. Subjectively “grade” your content’s value
Subjective
assessment
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
81. Put the grades together for a more
objective “report card”
Helps prioritize content migrations, refreshes, ...
82. Put the grades together for a more
objective “report card”
Objectifies subjective
assessments
Helps prioritize content migrations, refreshes, ...
83. 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
84. Agenda
1. The quick intro
2. Prioritizing and tuning top-down navigation
3. Demo: content modeling
4. Prioritizing and tuning contextual navigation
5. Group exercise: site search analytics
6. Prioritizing and tuning search
7. Changing your work and your organization
86. Agenda
1. The quick intro
2. Prioritizing and tuning top-down navigation
3. Demo: content modeling
4. Prioritizing and tuning contextual navigation
5. Group exercise: site search analytics
6. Prioritizing and tuning search
7. Changing your work and your organization
91. Mean = 10.6 characters
Median = 10 characters
Long tail queries likely longer
92. Mean = 10.6 characters
Median = 10 characters
Long tail queries likely longer
Top queries often in low 20s
!
93. 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?
!
94. 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
104. The absolute
meaninglessness
of
advanced search
!
At University of Alaska-Fairbanks,
advanced = expanded search
105. The absolute
meaninglessness
of
advanced search
!
At University of Alaska-Fairbanks,
advanced = expanded search
At the IRS,
advanced =
narrowed search
!
108. Look to session data for
progression and context
search session patterns
1. solar energy
2. how solar energy works
109. 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
110. 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
111. 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
112. 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
119. Tuning Search Results:
Handling specialized answers
“Product quick links” come directly from product content model
These results are a strong counterbalance to raw results
132. Tuning Search Results:
0 results pages
Not helpful
Much better:
“Did you
mean?” and
Popular
Searches
133. 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
134. Agenda
1. The quick intro
2. Prioritizing and tuning top-down navigation
3. Demo: content modeling
4. Prioritizing and tuning contextual navigation
5. Group exercise: site search analytics
6. Prioritizing and tuning search
7. Changing your work and your organization
138. 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
141. User Research Landscape
Ongoing coverage
of each of these
4 quadrants
from Christian Rohrer: http://is.gd/95HSQ2
142. 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)
147. 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
148. 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
• ...
150. Talking points
for refining, against redesigning
1. Solve the problem(s)
2. Save money
3. Reduce/end radical organizational changes
151. 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
152. This can be very, very helpful
Gamestorming
by Dave Gray,
Sunni Brown, and
James Macanufo
(O’Reilly, 2010)
153. 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
154. 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
162. 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
163. Agenda
1. The quick intro
2. Prioritizing and tuning top-down navigation
3. Demo: content modeling
4. Prioritizing and tuning contextual navigation
5. Group exercise: site search analytics
6. Prioritizing and tuning search
7. Changing your work and your organization
164. Say hello
Lou Rosenfeld
lou@louisrosenfeld.com
Rosenfeld Media
www.louisrosenfeld.com | @louisrosenfeld
www.rosenfeldmedia.com | @rosenfeldmedia
Editor's Notes
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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&#x2019;s top 500 unique queries for a specific month--these accounted for exactly 37% of all search activity. We used Microsoft&#x2019;s &#x201C;LEN&#x201D; function to count the number of characters in each query, and then calculated the queries&#x2019; 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&#x2019;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&#x2019;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&#x2019;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&#x2019;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&#x2019;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&#x2019;s example\n
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Anchors will be liked by good leaders, and will outlast bad leaders\n