Slides for my full-day information architecture workshop. Will teach in Minneapolis, MN (November 12, 2012) and Toronto, ON (November 29, 2012) Details: http://rosenfeldmedia.com/workshops/
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
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
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
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
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
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
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
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
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
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
121. Important content types emerge
from content modeling concert calendar
album pages artist descriptions
TV listings
album reviews discography artist bios
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%
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
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
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
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
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
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
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
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
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)
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
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
Hinweis der Redaktion
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Need to make strong point of context of large orgs\n
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Explain what I mean by redesign\n
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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http://en.wikipedia.org/wiki/File:Voltaire.jpg\n
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Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
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