2007 IA Summit: Las Vegas SEO and IA: The Makings of a Beautiful Friendship

Introduction Me: Information architect/Search specialist IA since 1998 Search since 2004 Topic: Search engine optimization and IA Shift in user locus of attention From navigation to search box Shift in our locus of attention From macro-structure to micro-wayfinding What I want IA to become a partner in developing search technology that works with the user IA community to “think” about how users find their websites when they design them Key takeaways [fingers crossed at my end] Search optimization and IA can and should co-exist One should not exist at the expense of the other

Me: Information architect/Search specialist

IA since 1998

Search since 2004

Topic: Search engine optimization and IA

Shift in user locus of attention

From navigation to search box

Shift in our locus of attention

From macro-structure to micro-wayfinding

What I want

IA to become a partner in developing search technology that works with the user

IA community to “think” about how users find their websites when they design them

Key takeaways [fingers crossed at my end]

Search optimization and IA can and should co-exist

One should not exist at the expense of the other

Search Usability Web analytics show preference for search box over any site navigation of any kind Search enables users to develop a need-specific/use-specific information path Search engine users visit more pages than those using navigation Pogo effect Ask.com now offers preview service so user does not have to click through How much of the navigation will they see in a thumbnail? Out of the top 20 results and you are out of sight and out of mind for a majority of users

Web analytics show preference for search box over any site navigation of any kind

Search enables users to develop a need-specific/use-specific information path

Search engine users visit more pages than those using navigation

Pogo effect

Ask.com now offers preview service so user does not have to click through

How much of the navigation will they see in a thumbnail?

Out of the top 20 results and you are out of sight and out of mind for a majority of users

Blame it all on Google PageRank is a pre-query valuation Based on number of links to the page 1 link=1 vote Most votes wins top placement Has no relationship to the subject of the query Googlearchy : dominant Web sites become more firmly entrenched in search results by nature of size Link rich get richer Failings soon uncovered Link farms Googlehacks

PageRank is a pre-query valuation

Based on number of links to the page

1 link=1 vote

Most votes wins top placement

Has no relationship to the subject of the query

Googlearchy : dominant Web sites become more firmly entrenched in search results by nature of size

Link rich get richer

Failings soon uncovered

Link farms

Googlehacks

Search 2.0 Web 2.0 give us Search 2.0 Harnessing the collective intelligence Online bookmarking Architecture of participation Open source Search Peer-to-peer Search Index of nodes in system Query passed to find appropriate node Remixable data sources and data transformation Local search Any of the “maps” applications Kayak.com and other travel sites Software above the level of a single device Mobile search Compensation for the commercialization of organic search Paid ads do not have to map semantically to the results they accompany Wales and Searchipedia Program not tied to a revenue model

Web 2.0 give us Search 2.0

Harnessing the collective intelligence

Online bookmarking

Architecture of participation

Open source Search

Peer-to-peer Search

Index of nodes in system

Query passed to find appropriate node

Remixable data sources and data transformation

Local search

Any of the “maps” applications

Kayak.com and other travel sites

Software above the level of a single device

Mobile search

Compensation for the commercialization of organic search

Paid ads do not have to map semantically to the results they accompany

Wales and Searchipedia

Program not tied to a revenue model

Now It is All About Meaning As Moore’s Law brings about cheaper, faster, stronger hardware, the quest changes from indexing everything to the presentation of results Search challenge to determine relevance without understanding meaning Transition from strict computation to computational techniques to determine meaning Hilltop Algorithm Topic-sensitive PageRank

As Moore’s Law brings about cheaper, faster, stronger hardware, the quest changes from indexing everything to the presentation of results

Search challenge to determine relevance without understanding meaning

Transition from strict computation to computational techniques to determine meaning

Hilltop Algorithm

Topic-sensitive PageRank

Hilltop Algorithm Segmentation of corpus into broad topics Subset that is then extrapolated to Web as a whole Created by Jon Kleinberg at Cornell in late 1990s Consultant to Google Selection of authority sources within these topic areas Authorities have lots of non-related pages on the same subject pointing to them Quality of links more important than quantity of links Determination of HUBS Pages that point to many authority sources Pre query calculations applied at query time Likely part of Google’s Florida update in 2004

Segmentation of corpus into broad topics

Subset that is then extrapolated to Web as a whole

Created by Jon Kleinberg at Cornell in late 1990s

Consultant to Google

Selection of authority sources within these topic areas

Authorities have lots of non-related pages on the same subject pointing to them

Quality of links more important than quantity of links

Determination of HUBS

Pages that point to many authority sources

Pre query calculations applied at query time

Likely part of Google’s Florida update in 2004

Topic-Sensitive PageRank Consolidation of Hypertext Induced Topic Selection [HITS] and PageRank Pre-query calculation of factors based on subset of corpus Context of term use in document Context of term use in history of queries Context of term use by user submitting query Creator now a Senior Engineer at Google

Consolidation of Hypertext Induced Topic Selection [HITS] and PageRank

Pre-query calculation of factors based on subset of corpus

Context of term use in document

Context of term use in history of queries

Context of term use by user submitting query

Creator now a Senior Engineer at Google

Search Further Down the Road Semantic search technology patents Search tool with preset categories and keywords 4-part database of information Index, categories, keywords, document-specific data Categories define topics through human-mediation Keywords extracted from document text User can iterate search results through related keywords presented from database Search manager Brokering application that facilitates selection of best search engine for the user’s query Similarity estimation Creates “sketch” or compact representation Compares sketches based on determined similarity threshold Deleted duplicate entries Personalized search Microsoft: Compares snippets of Web search engine results with data collected from user behavior and client Demonstrated in NYT article March 7, 2007 Google: user bookmarks [online and client] used to construct “personalized search object” that is used to filter Web search result Predictive search Bayesian model Compares user choices to predict more appropriate result from same vector space

Semantic search technology patents

Search tool with preset categories and keywords

4-part database of information

Index, categories, keywords, document-specific data

Categories define topics through human-mediation

Keywords extracted from document text

User can iterate search results through related keywords presented from database

Search manager

Brokering application that facilitates selection of best search engine for the user’s query

Similarity estimation

Creates “sketch” or compact representation

Compares sketches based on determined similarity threshold

Deleted duplicate entries

Personalized search

Microsoft: Compares snippets of Web search engine results with data collected from user behavior and client

Demonstrated in NYT article March 7, 2007

Google: user bookmarks [online and client] used to construct “personalized search object” that is used to filter Web search result

Predictive search

Bayesian model

Compares user choices to predict more appropriate result from same vector space

SEO and IA: Choices Capitulate No action Search technology continue on parallel path Cooperate Work with current search technology Develop best practices that build on developments in search technology Initiate Influence development of search technology Become a partner in developing user-centric search technology Action Items Influence the technology to work for not against user Site Navigation Strategy Site Organization Strategy Link Strategy Page Code Strategy Content Strategy Metadata Strategy

Capitulate

No action

Search technology continue on parallel path

Cooperate

Work with current search technology

Develop best practices that build on developments in search technology

Initiate

Influence development of search technology

Become a partner in developing user-centric search technology

Action Items

Influence the technology to work for not against user

Site Navigation Strategy

Site Organization Strategy

Link Strategy

Page Code Strategy

Content Strategy

Metadata Strategy

Initiate: Site Navigation Strategy Locus of attention has changed from navigation to search Hard-coded navigation structures are losing ground to pogo strikes Navigation Blindness Navigation Fatigue Page Paradigm Transitional Volatility Users need inducement to move further into the site Search technology rewards relationship navigation Berrypicking Information Model System approach to navigation development Systems have specific behaviors and outcomes

Locus of attention has changed from navigation to search

Hard-coded navigation structures are losing ground to pogo strikes

Navigation Blindness

Navigation Fatigue

Page Paradigm

Transitional Volatility

Users need inducement to move further into the site

Search technology rewards relationship navigation

Berrypicking Information Model

System approach to navigation development

Systems have specific behaviors and outcomes

Initiate: Site Organization Strategy Distance makes a difference Hierarchy reflects relevance MOSS 2007 and use of structural factors URL depth: the further from the homepage, the less important it must be Click distance: the further from an authority page, the less important it must be Architecture extends from the site to the page

Distance makes a difference

Hierarchy reflects relevance

MOSS 2007 and use of structural factors

URL depth: the further from the homepage, the less important it must be

Click distance: the further from an authority page, the less important it must be

Architecture extends from the site to the page

Initiate: Linking Strategy Links are human-mediated relationships Blast services are no longer worthwhile Related sites, niche directories, online bookmarking sites, provide starting points Create link-based relationship model of relevance Create or find authority Hook up to HUBs Think beyond the site

Links are human-mediated relationships

Blast services are no longer worthwhile

Related sites, niche directories, online bookmarking sites, provide starting points

Create link-based relationship model of relevance

Create or find authority

Hook up to HUBs

Think beyond the site

Cooperate: Page Code Strategy Reveal the site to the search technology Sitemap.xml Provide on the page navigation Don’t rely on dynamic navigation that spider cannot read Craft structures that cue technology on importance Illuminate the non-textual functionality Optimize JScript and Flash

Reveal the site to the search technology

Sitemap.xml

Provide on the page navigation

Don’t rely on dynamic navigation that spider cannot read

Craft structures that cue technology on importance

Illuminate the non-textual functionality

Optimize JScript and Flash

Initiate: Content Strategy Dense, subject-specific content is what is indexed People will scroll If they don't scroll, they will print it out Content to code ratio of 25% Promote a keyword-to-content ratio 10–15% Design on-the-page structure to move important information to the top Design relational content models Next steps as well as more information Develop authority sections on site Topic-based, not type-based

Dense, subject-specific content is what is indexed

People will scroll

If they don't scroll, they will print it out

Content to code ratio of 25%

Promote a keyword-to-content ratio 10–15%

Design on-the-page structure to move important information to the top

Design relational content models

Next steps as well as more information

Develop authority sections on site

Topic-based, not type-based

Cooperate: Metadata Strategy Many forms of description In the code Page title [in the browser window] Description Keywords? In the content Display title Content headings Most effective if unique to the content on the page Say goodbye to cut and paste Description rivals structure for importance for user context Ask.com thumbnails Humans determine the “meaning” of the document and inform the machine

Many forms of description

In the code

Page title [in the browser window]

Description

Keywords?

In the content

Display title

Content headings

Most effective if unique to the content on the page

Say goodbye to cut and paste

Description rivals structure for importance for user context

Ask.com thumbnails

Humans determine the “meaning” of the document and inform the machine

SEO and IA: Threats and Opportunities Threats Search technology advances without user representation Search engines have become dominant navigation tool through information spaces Bountiful Relevant? Traditional IA methodology increasingly less useful Hierarchy: pages further from the home page deemed less important Hard-coded navigation: not visible to search engines Not Authority-based Opportunities Users seeking human-mediated guides to find information Current search rewards a more flexible and intuitive IA Replaced by a new structural paradigm based on relationship and context Hub and authorities Quality over quantity Birds of a feather subject-wise

Threats

Search technology advances without user representation

Search engines have become dominant navigation tool through information spaces

Bountiful

Relevant?

Traditional IA methodology increasingly less useful

Hierarchy: pages further from the home page deemed less important

Hard-coded navigation: not visible to search engines

Not Authority-based

Opportunities

Users seeking human-mediated guides to find information

Current search rewards a more flexible and intuitive IA

Replaced by a new structural paradigm based on relationship and context

Hub and authorities

Quality over quantity

Birds of a feather subject-wise

Marianne Sweeny [email_address]