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This is some of what we will be looking at in this talk. Please ask questions along the way.

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Moving forward, successful SEO will rely on: Strategic approach from beginning of project. Understanding how search technology works. Knowledge of usability, human-computer interaction. Focus on the user experience down to the page-level. Relational content modeling. Say goodbye to: Link building (outsourced or not) Page-level keyword optimization Spam content

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I always start with a review of how search engines actually work. It is a good reminder that the foundation for their functionality is quite old, dating back to the 1960’s and early document retrieval. Remarkably, the search engine stores a copy of the query terms in a searchable index and even retain a copy of the page in another index. This was much easier when the Web was a mere 15 million pages in 1997 and considerably harder now in the dynamic Web that is estimated to have topped 1 trillion URLs last year. Google has claimed to have an index in excess of 125 billion pages, this is quite a lot considering the storage required. However, it is still less than 20% of the pages out there. Who gets into the index and why?

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There are 2 kinds of searches, navigational and informational How we look for information is different between people and between people and machines. Humans are limited by their ignorance. We don’t know what we’re looking for much of the time and so do not know how to find it. We often rely on technology to provide parameters to narrow our scope and put us on the right track. Unfortunately, technology is “face value” and so does not know how to interpret our queries. Does not understand that we can have a single word mean multiple things (order a meal, put things in order) or multiple terms mean the same thing (star: celestial entity, celebrity)

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This was recently put to the test in the US with an item that caused an uproar. A woman wants to buy designer eyeglasses and save money. She chooses the #3 result on Google. The frames that are delivered are obviously fake. When she returns them for refund, the owner of the business responds with harassment and threats. To the customer, relevant means honest and high quality. To Google, relevant means many links and many, many social media mentions. What the search engine did not understand is that most of the mentions were warnings of bad quality and service. When the story came to light, Google’s response was that they would “tune” their sentiment algorithm.

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http://www.googleblog.blogspot.com/2009/03/two-new-improvements-to-google-results.html Starting today, we’re deploying a new technology that can better understand associations and concepts related to your search, and one of its first applications lets us offer you even more useful related searches (the terms found at the bottom, and sometimes at the top, of the search results page). For example, if you search for [principles of physics], our algorithms understand that “angular momentum”, “special relativity”, “big bang” and “quantum mechanic” are related terms that could help you find what you need.” http://searchengineland.com/google-implements-orion-technology-improving-search-refinements-adds-longer-snippets-17038 I spoke yesterday to Google and Ori Allon. To the extent that I understood his discussion of the way Orion’s technology had been applied to refinements here’s what’s going on at a high level: pages are being scanned in “real-time” by Google after a query is entered. Conceptually and contextually related sites/pages are then identified and expressed in the form of the improved refinements. This is not solely keyword based but derived from an “understanding” of content and context.

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If machines are methodical, as we’ve seen, and people are emotional, as we experience, where is the middle ground? Are we working harder to really find what we need or just taking what we get and calling it what we wanted in the first place? Some other search engine patents Google

•Improving Search using Population Information (November 2008) •Rendering Context Sensitive Ads for Multi-topic searchers (April 2008) •Presentation of Local Results (July 2008) •Detecting Novel Content (November 2008) •Document Scoring based on Document Content Update (May 2007) •Document Scoring based on Link-based Criteria (April 2007)

Microsoft: Launches “decision engine” with focus on multiple meaning (contexts) as well as term indexing and topic association and tracking -Lead researcher Susan Dumais at the forefront of user behavior for prediction on search relevance -Look to recent acquisition of Powerset (semantic indexing) and FAST ESP (semantic processing) Calculating Valence of Expressions within Docum0ents for Searching a Document Index (March 2009): System for natural language search and sentiment analysis through a breakdown of the valence manipulation in document Efficiently Representing Word Sense Probabilities (April 2009): Word sense probabilities stored in a semantic index and mapped to “buckets.” Tracking Storylines Around a Query (May 2008): Employ probabilistic or spectral techniques to discover themes within documents delivered over a stream of time

Compares the query with the contents of each document to discover whether query exists implicitly or explicitly in received document Builds topic models Consolidate the plurality of info around certain subjects (track stories that continue over time) Collect results over time and sort (keeps track of the current themes and alerts to new)

Track Rank (relevance) Present abstracts

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Using the Internet: Skill Related Problems in User Online Behavior; van Deursen & van Dijk; 2009 There is no such thing as “advanced search” longer. We’re all lulled into the false sense that the search engine is smarter than us. Now the search engines present a mesmerizing array of choices distracting from the original intent of the search. There are things that we can do to help…

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There was a lot that led up to Panda 1999: PageRank

First iteration of human influence over results Coders only “miserable failure”

2003: Florida Update First appearance of Hilltop Algorithm First sign of contextual analysis Search engines compare source and destination link Introduction of link inequality – some are worth more than others

2005: Big Daddy update Infrastructure change – how sites are indexed Contextual relationship of links (Topic-sensitive evaluation)

May 2010: Mayday Major infrastructure changes in index Faster indexing – real time – to catch social mentions

More Information on Google’s Panda • Google Panda Update: 5 Deadly Content Sins http://www.seomoz.org/blog/beat-google-panda • SEO Book: Google Panda Update: http://www.seobook.com/google-update-panda • Excellent Guide to Quality Resource Articles on Panda: http://www.stayonsearch.com/complete-guide-to-

the-google-panda-update-50-articles-resources • Vanessa Fox:

• Search Engine Land (March 5, 2011): http://searchengineland.com/your-sites-traffic-has-plummeted-since-googles-farmerpanda-update-now-what-66769

• Stone Temple Interview: http://www.stonetemple.com/a-holistic-look-at-panda-with-vanessa-fox/ • Who Lost in the Panda Update: http://searchengineland.com/who-lost-in-googles-farmer-algorithm-change-

66173 • Google Webmaster Central on High Quality sites

:http://googlewebmastercentral.blogspot.com/2011/05/more-guidance-on-building-high-quality.html

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Panda 1.0: Google’s first salvo against “spam” (shallow, think 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 – Click through - Conversion

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Search results are “their” first experience in getting TO your experience

Google’s algorithm is using data points to assign value to the experience that you design

Users put a lot of trust in search engine results

Confident of their skills in searching due to ease of results Ipso facto: if you’re not within Google’s top results, you must not be relevant even if you are

Search results placement is seen as a sign of credibility

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Users look to search engines for guidance. We can provide similar guidance with user controls

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Jared Spool did a site search study some time ago that found users successful 37% of the time when using site search and 50+% of the time when navigating Users don’t like navigation at the outset but will use it if contextual and in a form that they can influence

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Stuart Brand in his book “How Buildings Learn” advised waiting to put in walkways around the building so that you can see where the pathways form on the grass and ground Users will tell you how they want to get to content

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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 Related Links: related as in “next steps” not what Marketing wants to be a next step Best Bets: editorially assigned result that may not be chosen by the search engine

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Not all links are created equal. Links between pages that share context are worth more (Hilltop and HITS algorithms) DMOZ feeds the Google Directory and is rumored to be the ontology of the Web

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We’re smart, search engines are a tool The agenda is about money from advertising and local tagging Structured things are easier to find and the Web is not structured Analytics tell us what, not why – user research tells us why Need is an experience – need to know is a state of being

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Our wishes have sort-of come true. Technology is now focused on user experience to surface information. We cannot let the technologist determine what is a good experience. Know how it works + Making it work for your visitors = Bearish SEO, the right way to optimize for search engine placement.

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