In February of 2012, Google began launching the Panda Update (bears), the first of many steps away from a link-based model of relevance to a user experience model of relevance. This bearish focus on relevance use algorithms to determine a positive user experience focused on click-through (does the user select the result), bounce rate (does the user take action once they arrive at the landing page) and conversion (does the landing page satisfy the user’s information need). Content and information design became the foundation for relevance. Sadly, no one at Google told the content strategists, user experience professionals and information architects about their new influence on search engine performance. In April of 2012, Google followed up with the Penguin update (birds), a direct assault on link building, a mainstay of traditional search engine optimization (SEO). The Penguin algorithm evaluates the context and quality of links pointing to a site. Website found to be “over optimized” with low quality links are removed from Google’s index. Matt Cutts, GOogle Webmaster and the public face of Google, summed this up best: “And so that’s the sort of thing where we try to make the web site, uh Google Bot smarter, we try to make our relevance more adaptive so that people don’t do SEO, we handle that...” Sadly, Google is short on detail about how they are handling SEO, what constitutes adaptive relevance and how user experience professionals, information architects and content strategists can contribute thought-processing biped wisdom to computational algorithmic adaptive relevance so that searchers find what they are looking for even when they do not know that that is. This presentation will provide a brief introduction to the inner workings of information retrieval, the foundation of all search engines, even Google. On this foundation, I will dive deep into the Bs of how to optimize Web sites for today’s search technology: Be focused, Be authoritative, Be contextual and Be engaging. Birds (Penguin), Bears (Panda) & Bees: Optimal SEO will provide insight into recent search engine changes, proscriptive optimization guidance for usability and content strategy and foresight into the future direction of search.

1. Some time ago, we fell asleep at the switch. Search engines are now “evaluating the merit” of
our content and are not entirely clear about the criteria that they are using.
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2. This presentation is about Google’s latest updates, Panda and Penguin, and how they impact
the content that is retained by the search engines and presented in search results. We will
look at:
1. What has happened with search engine technology over the years and what it is today
2. Why we should care. How search engine technology impacts what we do. How what we
do can impact the performance of search engines.
3. What we can do about it.
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3. Search engines came first. They have been around for over 70 years, since the their early
days of “information retrieval” when text began to be electronically transformed in the late
40’s. However, information organization and retrieval goes back even further than that…
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4. An argument could be made that “search engine” optimization came first with the
early great care was taken to present information in a “findable” fashion…e.g. great
care by a designated few to make information available in limited format to the
limited few who would consume and make available to the masses. People optimized
text for people.
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5. Then came the beautiful places where the information was organized in a standardized way
so that people could find it. And helpful people to ask for help finding information if we got
lost.
Early search engines used traditional information retrieval concepts and structured content
repositories that were mediated by human generated metadata. Dialog & ProQuest where
SQL queries rules, thought-processing bipeds associated tags, categories and abstracts to the
content item. dB methods of linear query construction delivered most success.
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6. First web page can still be found here http://www.w3.org/History/19921103-
hypertext/hypertext/WWW/TheProject.html
Then came the World Wide Web, altruistically developed by Tim Berners Lee so that the
military, industrial and scientific complexes could communicate with each other, be on the
same page and save money in the long distance exchange of information.
This worked well until the medium was made available to the rest of us.
The result….
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7. Then limitless growth, questionable quality and zero governance with no end in sight
• 1997: 15 million pages
• 2010: Google announces its 100 billion+ page index
• 2012: rumored 1 trillion URLs found
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8. © Tefko Saracevic
Source: Saracevic 1997, Information Today
One thing that did not change was information retrieval (IR). Despite the technology
advancements, the IR process remained the same.
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9. Slide from LIS 544 IMT 542 INSC 544 by Jeff Huang lazyjeff@uw.edu and Shawn Walker stw3@uw.edu
1. Documents were selected from the index based on the presence of query terms in
document text.
2. Documents containing more of the term(s) scored higher
3. Longer documents discounted
4. Rare terms weighted higher
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10. The environment, devices, participants and content has changed. What does that
mean for IR? Search Engines?
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11. IR’s locked in legacies are centered on
• text deconstruction
• the capacity for sequential instructions to derive meaning,
• its reliance on systems that do not scale well and while incorporating human
behavior, do not fully understand it
Search engines today believe that it is perfectly natural for them to abstract the
whole based on the nature of a small subset = “digital Maoism”
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12. Using Google’s Latent Semantic Indexing, a machine-learning technique that manually
maps relationships, a search for ~vacation turns up results for: hotels, rentals, travel,
tourism, resorts…
Machines know only what they are trained to know. Rules are based on an analysis of
a subset and applied to the content corpus writ large. Machines have no sense of
accountability when things go bad.
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13. Stanford research project that was once greeted as a savior due to the simplicity and seeming
incorruptability.
Both creators PHD students in data mining
Standard IR with introduction of 2 human elements
1. Random Surfer model
•At any time t, surfer is on some page P
•At time t+1, the surfer follows an outlink from uniformly at random
•Ends up on some page Q (from page P)
•Process repeats indefinitely
2. Link = vote
Unfortunately, flaws in this system were soon revealed:
1. Those who were able to build links dictated relevance for the rest
2. The cottage industry of SEO started building links for reasons other then endorsing the
merits of site content
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14. Google goes public around this time and the cash infusion enables expansion
Starts acquiring top computer scientists
Google purchases technology (Kaltix – personalized search, context sensitive search)
This is the first step away from the PageRank model, not entirely though as PageRank
is part of Google’s locked-in technology foundation.
And the response from us thought-processing bipeds?
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15. We’re constructing worse queries but feel that we’re getting better results.
Which canary in what coal mine just died?
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16. Using the Internet: Skill Related Problems in User Online Behavior; van Deursen & van Dijk; 2009
Pew Internet Trust Study of Search engine behavior
http://www.pewinternet.org/Reports/2012/Search-Engine-Use-2012/Summary-of-findings.aspx
In January 2002, 52% of all Americans used search engines. In February 2012 that figure grew to 73%
of all Americans. On any given day in early 2012, more than half of adults using the internet use a
search engine (59%). That is double the 30% of internet users who were using search engines on a
typical day in 2004. And people’s frequency of using search engines has jumped dramatically.
Moreover, users report generally good outcomes and relatively high confidence in the capabilities of
search engines:
91% of search engine users say they always or most of the time find the information they are seeking
when they use search engines
73% of search engine users say that most or all the information they find as they use search engines is
accurate and trustworthy
66% of search engine users say search engines are a fair and unbiased source of information
55% of search engine users say that, in their experience, the quality of search results is getting better
over time, while just 4% say it has gotten worse
52% of search engine users say search engine results have gotten more relevant and useful over time,
while just 7% report that results have gotten less relevant
And Google’s response…
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17. Location on the page = good quality content
“The goal of many of our ranking changes is to help searchers find sites that
provide a great user experience and fulfill their information needs. We also
want the “good guys” making great sites for users, not just algorithms, to see
their effort rewarded. To that end we’ve launched Panda changes that
successfully returned higher-quality sites in search results. And earlier this
year we launched a page layout algorithm that reduces rankings for sites that
don’t make much content available “above the fold.”
Matt Cutts http://googlewebmastercentral.blogspot.com/2012/04/another-
step-to-reward-high-quality.html
UX run Amok: if not enough content appears above the fold, the page will be seen as
less relevant? How many are dictating this for the rest of us? Where did they get this
from?
“As we’ve mentioned previously, we’ve heard complaints from users that if
they click on a result and it’s difficult to find the actual content, they aren’t
happy with the experience. Rather than scrolling down the page past a slew of
ads, users want to see content right away. So sites that don’t have much
content “above-the-fold” can be affected by this change. If you click on a
website and the part of the website you see first either doesn’t have a lot of
visible content above-the-fold or dedicates a large fraction of the site’s initial
screen real estate to ads, that’s not a very good user experience. Such sites
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18. may not rank as highly going forward.”
http://insidesearch.blogspot.com/2012/01/page-layout-algorithm-
improvement.html
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19. Panda 1.0: Google’s first salvo against “spam” (shallow, thin content sites) in the form of content duplication and low value
original content (i.e. “quick, give me 200 words on Brittany Spear’s vacation in the Maldives”) – biggest target was content
farms – Biggest Impact: keyword optimization and link building
Keyword optimization: Shift in focus from text on page to user experience makes optimizing for keywords counter
intuitive. Biggest impact: shift from developer/shady SEO influence to usability/user experience focus – average loss in
positioning (% of KWs falling out of top 10 search results) – 70 to 90% for sites like merchantcircle.com, find articles.com,
buzzle.com, mahalo.com and ezinearticles.com (SISTRIX)
Link building: PageRank does not scale well to a 1 trillion page Web. Google cannot calculate PR fast enough to rerank
sites. PR now devalued as strongest influence behind ranking. Biggest impact: link building for higher PR = “what’s the
point?”
Panda 2.0: Changed rolled out to all English language queries English speaking countries , UK, Australia, etc., and in
countries where English Language results are stipulated. Ranking incorporates searcher “blocking” data (from Google
Chrome feature).
Panda 2.1: Having unique content not enough – quality factors introduced (some below)
Trustworthiness: with my credit card information
Uniqueness: is this saying what I’ve found somewhere else
Origination: does the person writing the content have “street cred,” do I believe that this is an
authoritative resource on this topic
Display: does the site look professional, polished
Professional: is the content well constructed, well edited and without grammatical or spelling errors
Panda 2.2: Google going after site scrapers that repurpose content not their own or those who “outsource” content
development and maintenance
Panda 2.3: Bounce rate (whether the user engages with the page at all) – Click through - Conversion
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20. And sort of blames SEO for it (not outright but in a passive/aggressive) kind of way
2007 Google Patent: Methods and Systems for Identifying Manipulated Articles (November
2007)
Manipulation:
• Keyword stuffing (article text or metadata)
• Unrelated links
• Unrelated redirects
• Auto-generated in-links
• Guestbook pages (blog post comments)
Followed up: Google Patent: Content Entity Management (May 2012)
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21. February 2011: algorithm focused on content quality - originally thought to be aimed at content
farms
June 2011: update to identify scraped or duplicated content
October 2011: unannounced update to rectify site “unfairly impacted” by original updates
January 2012: sites with too much ad space above the fold are devalued
The slide lists approximately 10% of the changes that Google told us about and what they tell us
about likely represents .10% of the changes that they actually make. (source:
http://insidesearch.blogspot.com)
Re: freshness bug fix: “This change turns off a freshness algorithm component in certain cases
when it should be affecting the search results.”
Will serve up the newer document when choosing between two (from a given site)
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22. Where’s Heidi Klum when we need her. Google’s quality content bar is higher and more
subjective than Project Runway.
Google: Arbiter of Content & Relevance http://www.stonetemple.com/matt-cutts-and-eric-
talk-about-what-makes-a-quality-site/
“Those other sites are not bringing additional value. While they’re not duplicates they bring
nothing new to the table.”
Google’s advice to site owners:
“If it is already a crowded space with entrenched players, consider focusing on a niche area
initially, instead of going head to head with the existing leaders of the space.”
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23. The Penguin update is a bit different because it is an aggressive move on Google’s part that
starts with an algorithmic review. If a threshold is crossed, a human review takes place and
most sites are then significantly demoted in rankings or removed all together.
• Overly repetitive anchor text (“manipulative, repetitive anchor text”)
• Blog comments filled with spam (reviews/comments that contain links to “spam”) –
Google’s definition of spam similar to Supreme Court for
• Porn, no explanation of what this is. The search engine spiders just know it when they see
it
• Obscene content
• Web “clusters” – multiple Web sites on the same host, from same domain owner, linking
to article in artificial way
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24. Targets “exact match” keyword-ed links or aggressive anchor text to google
• sites penalized had “moneyed keywords” in 65% of their incoming links
• Obviously aimed at the long standing practice of outsourcing link building to 3rd
world countries and the weed-like growth of useless directories (i.e. link farms)
Too many links from “related sites
• Same niche
• Same domain host
• Same domain owner
Standard SEO signals
• Stuffed <title> and metaDescription
• Hidden text
• Unrelated links on and pointing to the page
• Computer generated text (i.e. dynamically rendered product pages)
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26. The search engines think that we’re superfluous because we don’t “get search” That’s what
I’m here to end. I want you to “get search.” We are information professionals, not mice!
We’re going to use every neuron, synapsis and gray cell to fight back.
We will shift from trying to optimize search engine behavior to optimizing what the search
engines consume, move from search engine optimization to information optimization
• We will Focus
• We will be Collaborative
• We will get Connected
• We will stay Current
Because we are user experience professionals, not Matt Cutts, Sergey Brin or Larry Page.
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28. Tools:
Core Metadata: 20-30 terms that represent intersection between client objectives and how
their customers search for the product/service
Content analytics: top pages, bounce rate, visitor flow
Content audit: keep/kill/revise based on thorough review using manual audit or tools
available through resources those from @content_insight
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29. Stronger G+ profile = more organic search traffic
http://www.portent.com/blog/seo/google-plus-will-build-your-search-traffic.htm
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30. If it barks, sings, dances, plays, changes whatever, annotate with something the
search engine can crawl, deconstruct, associate with surrogate and store in the index
• Relational content model: Next Steps as well as More Information using: guided
tours, Best Bets, produced view, etc
• Best Bets: editorially assigned result that may not be chosen by the search engine
• Guided Tours: built on analysis of other user pathways and knowledge of corpus
Produced Views: page of assembled content items focused on a single subject
• Task List Drop Downs: “I Want To…” links to pages of assembled content focused
on single common task
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32. This is a team effort.
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33. It is not too soon to get started.
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