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Bearish SEO: Defining the User Experience for Google’s Panda Search Landscape


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The search sun shifted in March 2011 when Google started rolling out the beginning of the Panda update. Instead of using the famous PageRank, a link-based relevance calculation, Panda rests on a machine interpretation of user experience to decide which sites are most relevant to a searchers quest for knowledge. This means that IA and UX practitioners need to start thinking about the machine implications of the way they structure information on the web, and think ahead about the human implications for how search engines present their sites in response to searcher queries. Bearish SEO will present real, actionable methods for content providers, information architects and user experience designers to directly influence search engine discoverability. Need is an experience. It is a state of being. The goal for this presentation is to ensure that user experience professionals become an integral part of designing search experience.

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Bearish SEO: Defining the User Experience for Google’s Panda Search Landscape

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  2. 2. This is some of what we will be looking at in this talk. Please ask questions along the way. 2
  3. 3. 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 optimizationSpam content 3
  4. 4. I always start with a review of how search engines actually work. It is a good reminder that thefoundation 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 retaina copy of the page in another index.This was much easier when the Web was a mere 15 million pages in 1997 and considerably hardernow in the dynamic Web that is estimated to have topped 1 trillion URLs last year. Google has claimedto 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? 4
  5. 5. There are 2 kinds of searches, navigational and informationalHow 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 sodo not know how to find it. We often rely on technology to provide parameters to narrow our scopeand put us on the right track. Unfortunately, technology is “face value” and so does not know how tointerpret 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) 5
  6. 6. This was recently put to the test in the US with an item that caused an uproar. A woman wants to buydesigner eyeglasses and save money. She chooses the #3 result on Google. The frames that aredelivered are obviously fake. When she returns them for refund, the owner of the business respondswith harassment and threats.To the customer, relevant means honest and high quality. To Google, relevant means many links andmany, many social media mentions. What the search engine did not understand is that most of thementions were warnings of bad quality and service.When the story came to light, Google’s response was that they would “tune” their sentimentalgorithm. 6
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  8. 8. today, we’re deploying a new technology that can better understand associations andconcepts related to your search, and one of its first applications lets us offer you even more usefulrelated 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 “angularmomentum”, “special relativity”, “big bang” and “quantum mechanic” are related terms that couldhelp you find what you need.” spoke yesterday to Google and Ori Allon. To the extent that I understood his discussion of the wayOrion’s technology had been applied to refinements here’s what’s going on at a high level: pages arebeing scanned in “real-time” by Google after a query is entered. Conceptually and contextually relatedsites/pages are then identified and expressed in the form of the improved refinements. This is notsolely keyword based but derived from an “understanding” of content and context. 8
  9. 9. 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 reallyfind what we need or just taking what we get and calling it what we wanted in the first place?Some other search engine patentsGoogle •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 andtracking-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 naturallanguage search and sentiment analysis through a breakdown of the valence manipulation in documentEfficiently Representing Word Sense Probabilities (April 2009): Word sense probabilities stored in a semantic index and mappedto “buckets.”Tracking Storylines Around a Query (May 2008): Employ probabilistic or spectral techniques to discover themes withindocuments 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 9
  10. 10. Using the Internet: Skill Related Problems in User Online Behavior; van Deursen & van Dijk; 2009There is no such thing as “advanced search” longer. We’re all lulled into the false sense that the searchengine is smarter than us. Now the search engines present a mesmerizing array of choices distractingfrom the original intent of the search.There are things that we can do to help… 10
  11. 11. There was a lot that led up to Panda1999: 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 others2005: 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 mentionsMore Information on Google’s Panda• Google Panda Update: 5 Deadly Content Sins• SEO Book: Google Panda Update:• Excellent Guide to Quality Resource Articles on Panda: the-google-panda-update-50-articles-resources• Vanessa Fox: • Search Engine Land (March 5, 2011): plummeted-since-googles-farmerpanda-update-now-what-66769 • Stone Temple Interview:• Who Lost in the Panda Update: 66173• Google Webmaster Central on High Quality sites : 11
  12. 12. Panda 1.0: Google’s first salvo against “spam” (shallow, think content sites) in the form of contentduplication and low value original content (i.e. “quick, give me 200 words on Brittany Spear’s vacation in theMaldives”) – 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, find,, and (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 isan authoritative resource on this topic Display: does the site look professional, polished Professional: is the content well constructed, well edited and without grammatical orspelling errorsPanda 2.2: Google going after site scrapers that repurpose content not their own or those who “outsource”content development and maintenancePanda 2.3: Bounce rate – Click through - Conversion 12
  13. 13. Search results are “their” first experience in getting TO your experienceGoogle’s algorithm is using data points to assign value to the experience that you designUsers 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 areSearch results placement is seen as a sign of credibility 13
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  15. 15. Users look to search engines for guidance. We can provide similar guidance with user controls 15
  16. 16. Jared Spool did a site search study some time ago that found users successful 37% of the time whenusing site search and 50+% of the time when navigatingUsers don’t like navigation at the outset but will use it if contextual and in a form that they caninfluence 16
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  19. 19. Stuart Brand in his book “How Buildings Learn” advised waiting to put in walkways around the buildingso that you can see where the pathways form on the grass and groundUsers will tell you how they want to get to content 19
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  21. 21. Guided Tours: built on analysis of other user pathways and knowledge of corpusProduced Views: page of assembled content items focused on a single subjectTask List Drop Downs: “I Want To…” links to pages of assembled content focused on single commontaskRelated Links: related as in “next steps” not what Marketing wants to be a next stepBest Bets: editorially assigned result that may not be chosen by the search engine 21
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  23. 23. Not all links are created equal. Links between pages that share context are worth more (Hilltop andHITS algorithms)DMOZ feeds the Google Directory and is rumored to be the ontology of the Web 23
  24. 24. We’re smart, search engines are a toolThe agenda is about money from advertising and local taggingStructured things are easier to find and the Web is not structuredAnalytics tell us what, not why – user research tells us whyNeed is an experience – need to know is a state of being 24
  25. 25. 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 forsearch engine placement. 25
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