SEO Master Class Webit 2010

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All 166 slides on advanced SEO and social media master class from Rand Fishkin, CEO of SEOmoz, at Webit 2010 in Sofia, Bulgaria.

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SEO Master Class Webit 2010

  1. 1. SEO Masterclass Rand Fishkin, CEO & Co-founder, SEOmoz Webit, Sofia October 2010
  2. 2. Topics for the Masterclass • Correlation analysis of search results • Changes from Google Instant • Information architecture & navigation structure • Overcoming Twitter’s cannibalization of the link graph • Making Analytics Actionable • New Research: Topic Modeling in the Search Results
  3. 3. Use Statistical Analysis to Answer Important SEO Questions
  4. 4. Correlation ≠ Causation The more I wear suits, the more I speak on panels. Therefore: wearing suits causes me to speak on panels.
  5. 5. Understanding Correlation Significance No Correlation Exact Match Domain Perfect Correlation Most of our data for search rankings falls in this region (which we’d expect given algorithms w/ 200+ ranking factors)
  6. 6. Question #1: How to Best “Optimize” a Site for Search Engine Rankings
  7. 7. Methodology • 11,351 SERPs via Google AdWords Suggest • 1st Page Only (usually ~10 results per page) • Correlations are w/ Higher Position on Page 1 • Controlled for SERPs Where All (or None) of the Results Matched the Metric
  8. 8. Methodology Looking for elements that higher ranking pages have that lower ones do not NOT looking at raw counts of how many pages featured a given element
  9. 9. Contains All Query Terms in Domain Name Exact Match Hyphenated Domain Exact Match Domain Highest Stderr = 0.0241804
  10. 10. Our Interpretation • Exact match domains remain powerful in both engines (anchor text could be a factor, too) • Hyphenated versions are less powerful, though more frequent in Bing (G: 271 vs. B: 890) • Just having keywords in the domain name has substantial positive correlation
  11. 11. Highest Stderr = 0.00350211 KWs in Body KWs in Alt Attribute KWs in H1 Tag KWs in URL KWs in Title
  12. 12. Our Interpretation • The Alt attribute of images is interesting • Putting KWs in URLs is likely a best practice • Everyone optimizes titles (G: 11,115 vs. B: 11,143). Differentiating here is hard. • (Simplistic) on-page optimization isn’t a huge factor
  13. 13. Highest Stderr = 0.0269818 .gov .edu .info .net .org .com
  14. 14. Our Interpretation • More reasons to believe Google when they say .gov, .info and .edu are not special cased • The .org TLD extension is surprising – do they earn more links? Less spam? More non-commercial? • Don’t forget about branding/user behavior - .com is still probably a very good thing (at least own it)
  15. 15. Highest Stderr = 0.0033353 Content Length (tokes in body) URL Length (chars.) Domain Name Length (chars.)
  16. 16. Our Interpretation • Shorter URLs are likely a good best practice (especially on Bing) • Long domains may not be ideal, but aren’t awful • Raw content length seems marginal in correlation
  17. 17. Question #2: What Kind of Links Matter & How Should We Evaluate Links?
  18. 18. Highest Stderr = 0.00335677 # of Linking Root Domains to URL # of Links to URL
  19. 19. Our Interpretation • Links are likely still a major part of the algorithms • Bing may be slightly more naïve in their usage of link data than Google, but better than before • Diversity of link sources remains more important than raw link quantity
  20. 20. Highest Stderr = 0.00415058 # of Links w/ exact match anchor text # of linking root domains w/ exact match anchor text
  21. 21. Our Interpretation • Many anchor text links from the same domain likely don’t add much value • Anchor text links from diverse domains, however, appears highly correlated • Bing and Google are relatively similar in evaluating these metrics
  22. 22. Correlation of Page-Level Link Valuation Metrics
  23. 23. Our Interpretation • PageRank (and similar algorithms) are not particularly representative of rankings (but are somewhat correlated) • Linking domains are likely a better metric than raw links • Page Authority is reasonably good, but has a way to go
  24. 24. Correlation of Domain-Level Link Valuation Metrics
  25. 25. Our Interpretation • No single domain valuation metric is especially well correlated with rankings • Rankings of individual pages may be more disparate we typical think re: “domain authority” • Overall, we’re still very naïve when it comes to understanding how links influence search rankings
  26. 26. Question #3: How Does Google Instant Change Keyword Demand / SEO?
  27. 27. http://www.readwriteweb.com/archives/report_google_search_box_in_firefox_accounts_for_9.php Are Most Users Seeing/Using Google Instant?
  28. 28. Methodology: Keyword Referral Search Data • Look at keyword sending traffic via analytics • Distribute into groups by word-length • Analyze shifts in demand by keywords that brought visits to the site • Compare from period prior to Google Instant and directly after
  29. 29. http://www.mecmanchester.co.uk/blog/google-instant-data-after-12-days.html Via MEC Manchester (UK) 5 Sites, 4 Verticals, 10K+ Keywords
  30. 30. Via Distilled Consulting (UK) 11 Sites, Various Sizes (3.5K – 75K weekly visits), 75K+ Keywords http://www.distilled.co.uk/blog/seo/impact-of-google-instant/
  31. 31. Via Conductor Multiple sites, 880K visits, 10Ks of keywords http://blog.conductor.com/2010/09/what%E2%80%99s-been-the-impact-of-google-instant-on-searcher-behavior-so-far-not-much/
  32. 32. Interesting Takeaways • Google Instant seems not to have shifted keyword demand by much (if at all) • Google “suggest” has been out for a long time already; users are likely accustomed to this feature • The “long tail” may get longer/shorter over time, but Instant seems less responsible than other factors
  33. 33. Goals of Successful Information Architecture
  34. 34. Semantically Logical Structure
  35. 35. Minimize Click-Depth
  36. 36. Maximize Usability of Navigation
  37. 37. Tips for Semantically Useful Navigation
  38. 38. Initially Design without Keyword Research
  39. 39. Add in Keyword Research Based Modifications
  40. 40. Validate Architecture/Path with Non-SEOs
  41. 41. Tips for Minimal Click-Depth
  42. 42. Imitate the Ideal Nav Pyramid
  43. 43. Broad Linking at Top Levels
  44. 44. Editorial Categorization > User-Defined
  45. 45. Editorial Categorization > User-Defined
  46. 46. HACK: Multi-Level HTML Sitemap
  47. 47. Tips for Usable Navigation
  48. 48. Obvious Navigation Elements
  49. 49. Naming Conventions that Match Intent
  50. 50. User & Usability Testing
  51. 51. Avoiding Common “Big Site” Problems
  52. 52. Duplicate Content Issues
  53. 53. Rel Canonical Tags
  54. 54. Google Webmaster Tools
  55. 55. SEOmoz Web App
  56. 56. Scraping & Re-Publishing
  57. 57. Employ Absolute URLs Absolute: <a href=“http://www.seomoz.org/blog”> anchor </a> Relative: <a href=“../blog”> anchor </a>
  58. 58. C&D vs. Large, Credible Orgs that Scrape
  59. 59. Don’t Go Overboard w/ Bot Blocking
  60. 60. Incomplete Indexation
  61. 61. Track Referrals, not Site: Commands
  62. 62. Check Page “Types” that Don’t Receive Traffic
  63. 63. XML Sitemaps
  64. 64. Content Syndication
  65. 65. RSS Feeds
  66. 66. Twitter for Indexation
  67. 67. “Search Results” in the SERPs
  68. 68. Create Category “Landing” Pages
  69. 69. Remove Obvious Traces of “Search” on Landing Pages
  70. 70. Thin Content Issues
  71. 71. Bolster w/ UGC
  72. 72. Employ Scalable Content Production
  73. 73. Keep “Thin” Pages Out of the SERPs
  74. 74. Faceted Navigation
  75. 75. Rel Canonical Can Help
  76. 76. Use AJAX to Reload Pages
  77. 77. Watch Out for Google Crawling Javascript
  78. 78. Offer Facets Only to Logged-In / Cookied Users Logged-In = 345 / Googlebot = 141
  79. 79. Overcoming Twitter’s Cannibalization of the Link Graph
  80. 80. Way Back in 2007 Interesting content, blog posts & linkbait earned LOTS of links
  81. 81. Fast Forward to 2010 Not so many links (in comparison)
  82. 82. Fast Forward to 2010 But tons of social sharing (and tweets)
  83. 83. Are Pages Linking Out Less? Via Linkscape’s web index
  84. 84. How Do We Earn Traditional Web Links (the kind search engines love)?
  85. 85. Tactic #1: Embeddable Content
  86. 86. Infographics http://royal.pingdom.com/2010/02/24/google-facts-and-figures-massive-infographic/ +1,085 links from 356 root domains
  87. 87. Badges http://www.zillow.com/webtools/badges/
  88. 88. Value-Add Widgets
  89. 89. Tactic #2: Reference Material
  90. 90. Research & Data http://www.time.com/time/health/article/0,8599,2014332,00.html
  91. 91. Awards + Rankings http://www.moneycrashers.com/top-personal-finance-blogs/
  92. 92. Citation-Worthy Explanations http://www.seomoz.org/blog/googles-algorithm-pretty-charts-math-stuff
  93. 93. Tactic #3: Syndicated Content
  94. 94. Niches where content is low-supply/high-demand http://perfectmarket.com/vault_index_summer_2010_infographic
  95. 95. ID sites that already syndicate from someone else
  96. 96. Tactic #4: Stick to Niches w/o Twitter Adoption
  97. 97. Find sectors where traditional blogs/forums dominate conversation http://www.eng-tips.com/
  98. 98. Many of these “old-school” sites have followed external links (but don’t abuse these) Note the nofollow highlighting
  99. 99. Some “Web 2.0” Ones, Too  Impressive domain and page level metrics
  100. 100. Tactic #5: Friends, Partners, Customers & Vendors
  101. 101. Friends + Family
  102. 102. Partners
  103. 103. Customers http://www.seomoz.org/blog/headsmacking-tip-1-link-requests-in-order-confirmation-emails
  104. 104. Vendors http://burton.kontain.com/evogear/
  105. 105. Tactic #6: Twitter May Take Your Tweets, But They’ll Never Take Your Content!
  106. 106. Turn Your Tweets Into Content http://twournal.com/home
  107. 107. Turn Tweets from industry leaders into content, too (this entices them to share) http://www.seomoz.org/blog/bing-vs-google-prominence-of-ranking-elements
  108. 108. Tactic #7: Can’t Beat ‘em? Join ‘em!
  109. 109. Twitter is (almost certainly) influencing (at least some) rankings Lots of tweets, virtually no links, but remarkable rankings
  110. 110. Twitter can send lots of direct traffic
  111. 111. You need to target the right Tweeters http://twitaholic.com/top100/followers/
  112. 112. The Ones Who Send Real Traffic http://mashable.com/2009/07/07/twitter-clickthrough-rate/
  113. 113. Use the “Rank” Not the “Grade” http://twittergrader.com
  114. 114. Takeaways • # of Followers DEFINITELY isn’t everything • Tweeting heavily may not necessarily hurt the attention people pay to your tweets • Klout score probably isn’t great for predicting the reach of an individual’s tweets/messages • TwitterGrader Rank may be useful for predicting the traffic you’d get from a tweeter • Avg. CTR across 254 shared links = 1.17% (don’t feel bad if only 1/100 followers clicks a link)
  115. 115. Making Analytics Actionable
  116. 116. To Make Analytics Actionable Always Ask: #1 - “Why am I measuring this?” #2 – “What would I do if results were different?”
  117. 117. # of Visits Per Search Engine Over Time
  118. 118. Action: Measure against search engine market shares & volume to determine whether you’re making positive strides
  119. 119. # Pages Getting Search Referrals Over Time Measure this number on a weekly/monthly basis
  120. 120. Action: Discover if indexation is an issue worth effort This number sucks. Learn more about why at: www.seomoz.org/blog/indexation-for-seo-real-numbers-in-5-easy-steps
  121. 121. # of Keywords Sending Traffic from a Search Engine over Time
  122. 122. Action: Determine if content additions are accretive and what drives growth/shrinkage in search traffic Did rankings fall? Or is demand down?
  123. 123. Search Referral Analytics
  124. 124. # of Visits per Keyword
  125. 125. Action: Analyze top traffic drivers from a value perspective, check rankings for potential easy wins & get answers if traffic dips “SEO Tools” is a big win and we could rank higher
  126. 126. First-Time vs. Returning Visits per Keyword The keyword “SEO” leans toward first-time visits
  127. 127. Action: Determine value of reaching new visitors vs. converting branded users (focus efforts on the more valuable one) This metric speaks to business strategy about converting existing fans vs. reaching new customer segments
  128. 128. Distribution of Keyword Referrals
  129. 129. Action: Discover strengths vs. opportunities (60-70% of traffic is typically in the long tail and it converts better)
  130. 130. Keyword Rankings www.seomoz.org/rank-tracker
  131. 131. Action: Know if traffic spikes/dropoffs are from rankings, indexation or search demand shifts Rankings and Traffic both Dropped
  132. 132. Page Two Rankings Referrals from Page 2
  133. 133. Action: Identify low hanging fruit that can be optimized quickly Could totally 301 this to www.opensiteexplorer.org
  134. 134. Engagement Analytics
  135. 135. Time on Site
  136. 136. Action: Compare to ROI metrics; if they correlate, improve on keywords/landing pages with low time on site Average “upgrade to PRO” visitor spent a whopping 44 minutes on SEOmoz!
  137. 137. # of Page Views
  138. 138. Action: Depending on your metrics, a “sweet spot” of pages browsed often dictates a conversion event – optimize towards it Average “upgrade to PRO” visitor visits 12X the pages of an average visitor
  139. 139. Repeat Visit Ratio
  140. 140. Action: Find what content/activities/referrers send engaged traffic and copy those while improving subpar pages
  141. 141. Sharing/Linking Activity “Sharing Activity” Conversions
  142. 142. Action: Find patterns/sources that predict sharing activities (both content and CTAs) and make them testable conversion events GA allows you to set custom actions as “goals” then filter, monitor and improve on these metrics
  143. 143. Latent Conversion Tracking
  144. 144. Removing Last-Click Attribution
  145. 145. Full Path Analysis
  146. 146. Initial Referrer www.seomoz.org/blog/how-to-get-past-last-touch-attribution-with-google-analytics
  147. 147. ROI Analytics
  148. 148. Lifetime Customer Value
  149. 149. Cost of Acquisition
  150. 150. Return on Investment ROI = CLTV - CAC No. No. No. Yes. Yes. Yes.
  151. 151. Always Be Asking “What’s the ROI?” Get the ROI for every category (and subset)
  152. 152. New Research: Topic Modeling in the Search Results
  153. 153. Methodology: LDA (Latent Dirichlet Allocation) • Build an LDA model based on the English language Wikipedia dataset (8mil+ pages) • Generate scores for top 10 rankings across several thousand search results • Look at correlation of search rankings with scores (in process)
  154. 154. Chance of word is because of a topic = (Number of times the document already uses that topic a lot) X (Number of times that word has been in that topic) Simplified LDA Formula
  155. 155. Tool to Test it Out http://www.seomoz.org/labs/lda
  156. 156. Tool to Test it Out http://www.seomoz.org/labs/lda
  157. 157. Tool to Test it Out We might need to work the “relevance” of our content http://www.seomoz.org/labs/lda
  158. 158. Interesting Takeaways • There may be more to “on-page” optimization then just using target keywords in the right places / ways • Search engines keep saying “make relevant content” – perhaps we can get more scientific and precise about what “relevant” means • Our LDA topic modeling work is still in its infancy. Expect more data, correlations, etc. in weeks to come.
  159. 159. Q+A Rand Fishkin, CEO & Co-Founder, SEOmoz • Twitter: @randfish • Blog: www.seomoz.org/blog • Email: rand@seomoz.org

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