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

1. SEO Masterclass
Rand Fishkin, CEO & Co-founder, SEOmoz
Webit, Sofia October 2010

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. Use Statistical Analysis to Answer
Important SEO Questions

4. Correlation ≠ Causation
The more I wear suits, the more I speak on panels.
Therefore: wearing suits causes me to speak on panels.

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. Question #1:
How to Best “Optimize” a Site for
Search Engine Rankings

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. 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. Contains All
Query Terms in
Domain Name
Exact Match
Hyphenated
Domain
Exact Match
Domain
Highest Stderr = 0.0241804

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. Highest Stderr = 0.00350211
KWs in Body
KWs in Alt Attribute
KWs in H1 Tag
KWs in URL
KWs in Title

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. Highest Stderr = 0.0269818
.gov
.edu
.info
.net
.org
.com

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. Highest Stderr = 0.0033353
Content Length
(tokes in body)
URL Length
(chars.)
Domain Name
Length (chars.)

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. Question #2:
What Kind of Links Matter & How
Should We Evaluate Links?

18. Highest Stderr = 0.00335677
# of Linking Root
Domains to URL
# of Links to URL

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. Highest Stderr = 0.00415058
# of Links w/
exact match
anchor text
# of linking root
domains w/
exact match
anchor text

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. Correlation of Page-Level Link Valuation Metrics

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. Correlation of Domain-Level Link Valuation Metrics

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. Question #3:
How Does Google Instant Change
Keyword Demand / SEO?

27. http://www.readwriteweb.com/archives/report_google_search_box_in_firefox_accounts_for_9.php
Are Most Users Seeing/Using Google Instant?

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. http://www.mecmanchester.co.uk/blog/google-instant-data-after-12-days.html
Via MEC Manchester (UK)
5 Sites, 4 Verticals, 10K+ Keywords

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. 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. 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. Goals of Successful Information
Architecture

34. Semantically Logical Structure

35. Minimize Click-Depth

36. Maximize Usability of Navigation

37. Tips for Semantically Useful
Navigation

38. Initially Design without Keyword Research

39. Add in Keyword Research Based Modifications

40. Validate Architecture/Path with Non-SEOs

41. Tips for Minimal Click-Depth

42. Imitate the Ideal Nav Pyramid

43. Broad Linking at Top Levels

44. Editorial Categorization > User-Defined

45. Editorial Categorization > User-Defined

46. HACK: Multi-Level HTML Sitemap

47. Tips for Usable Navigation

48. Obvious Navigation Elements

49. Naming Conventions that Match Intent

50. User & Usability Testing

51. Avoiding Common “Big Site”
Problems

52. Duplicate Content Issues

53. Rel Canonical Tags

54. Google Webmaster Tools

55. SEOmoz Web App

56. Scraping & Re-Publishing

57. Employ Absolute URLs
Absolute: <a href=“http://www.seomoz.org/blog”> anchor </a>
Relative: <a href=“../blog”> anchor </a>

58. C&D vs. Large, Credible Orgs that Scrape

59. Don’t Go Overboard w/ Bot Blocking

60. Incomplete Indexation

61. Track Referrals, not Site: Commands

62. Check Page “Types” that Don’t Receive Traffic

63. XML Sitemaps

64. Content Syndication

65. RSS Feeds

66. Twitter for Indexation

67. “Search Results” in the SERPs

68. Create Category “Landing” Pages

69. Remove Obvious Traces of “Search” on Landing Pages

70. Thin Content Issues

71. Bolster w/ UGC

72. Employ Scalable Content Production

73. Keep “Thin” Pages Out of the SERPs

74. Faceted Navigation

75. Rel Canonical Can Help

76. Use AJAX to Reload Pages

77. Watch Out for Google Crawling Javascript

78. Offer Facets Only to Logged-In / Cookied Users
Logged-In = 345 / Googlebot = 141

79. Overcoming Twitter’s
Cannibalization of the Link Graph

80. Way Back in 2007
Interesting content, blog posts & linkbait earned LOTS of links

81. Fast Forward to 2010
Not so many links (in comparison)

82. Fast Forward to 2010
But tons of social sharing (and tweets)

83. Are Pages Linking Out Less?
Via Linkscape’s web index

85. How Do We Earn Traditional Web
Links (the kind search engines love)?

86. Tactic #1:
Embeddable Content

87. Infographics
http://royal.pingdom.com/2010/02/24/google-facts-and-figures-massive-infographic/
+1,085 links from
356 root domains

88. Badges
http://www.zillow.com/webtools/badges/

89. Value-Add Widgets

90. Tactic #2:
Reference Material

91. Research & Data
http://www.time.com/time/health/article/0,8599,2014332,00.html

92. Awards + Rankings
http://www.moneycrashers.com/top-personal-finance-blogs/

93. Citation-Worthy Explanations
http://www.seomoz.org/blog/googles-algorithm-pretty-charts-math-stuff

94. Tactic #3:
Syndicated Content

95. Niches where content is low-supply/high-demand
http://perfectmarket.com/vault_index_summer_2010_infographic

96. ID sites that already syndicate
from someone else

97. Tactic #4:
Stick to Niches w/o Twitter Adoption

98. Find sectors where traditional
blogs/forums dominate conversation
http://www.eng-tips.com/

99. Many of these “old-school” sites have
followed external links (but don’t abuse these)
Note the nofollow
highlighting

100. Some “Web 2.0” Ones, Too 
Impressive domain and
page level metrics

101. Tactic #5:
Friends, Partners, Customers &
Vendors

102. Friends + Family

103. Partners

104. Customers
http://www.seomoz.org/blog/headsmacking-tip-1-link-requests-in-order-confirmation-emails

105. Vendors
http://burton.kontain.com/evogear/

106. Tactic #6:
Twitter May Take Your Tweets, But
They’ll Never Take Your Content!

107. Turn Your Tweets Into Content
http://twournal.com/home

108. 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

109. Tactic #7:
Can’t Beat ‘em? Join ‘em!

110. Twitter is (almost certainly) influencing (at least some) rankings
Lots of tweets, virtually no links,
but remarkable rankings

111. Twitter can send lots of direct traffic

112. You need to target the right Tweeters
http://twitaholic.com/top100/followers/

113. The Ones Who Send Real Traffic
http://mashable.com/2009/07/07/twitter-clickthrough-rate/

115. Use the “Rank”
Not the “Grade”
http://twittergrader.com

116. 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)

117. Making Analytics Actionable

118. To Make Analytics Actionable Always Ask:
#1 - “Why am I measuring this?”
#2 – “What would I do if results were different?”

119. # of Visits Per Search Engine Over Time

120. Action: Measure against search engine market shares &
volume to determine whether you’re making positive strides

121. # Pages Getting Search Referrals Over Time
Measure this number on a
weekly/monthly basis

122. 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

123. # of Keywords Sending Traffic from a
Search Engine over Time

124. Action: Determine if content additions are accretive
and what drives growth/shrinkage in search traffic
Did rankings fall? Or is
demand down?

125. Search Referral Analytics

126. # of Visits per Keyword

127. 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

128. First-Time vs. Returning Visits per Keyword
The keyword “SEO” leans
toward first-time visits

129. 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

130. Distribution of Keyword Referrals

131. Action: Discover strengths vs. opportunities (60-70% of traffic is typically
in the long tail and it converts better)

132. Keyword Rankings
www.seomoz.org/rank-tracker

133. Action: Know if traffic spikes/dropoffs are from rankings, indexation or
search demand shifts
Rankings and Traffic both
Dropped

134. Page Two Rankings
Referrals from Page 2

135. Action: Identify low hanging fruit that can be optimized quickly
Could totally 301 this to
www.opensiteexplorer.org

136. Engagement Analytics

137. Time on Site

138. 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!

139. # of Page Views

140. 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

141. Repeat Visit Ratio

142. Action: Find what content/activities/referrers send engaged traffic and
copy those while improving subpar pages

143. Sharing/Linking Activity
“Sharing Activity”
Conversions

144. 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

145. Latent Conversion Tracking

146. Removing Last-Click Attribution

147. Full Path Analysis

148. Initial Referrer
www.seomoz.org/blog/how-to-get-past-last-touch-attribution-with-google-analytics

149. ROI Analytics

150. Lifetime Customer Value

151. Cost of Acquisition

152. Return on Investment
ROI = CLTV - CAC
No. No. No.
Yes. Yes. Yes.

153. Always Be Asking “What’s the ROI?”
Get the ROI for every category (and subset)

154. New Research: Topic Modeling in
the Search Results

160. 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)

161. 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

162. Tool to Test it Out
http://www.seomoz.org/labs/lda

163. Tool to Test it Out
http://www.seomoz.org/labs/lda

164. Tool to Test it Out
We might need to
work the “relevance”
of our content
http://www.seomoz.org/labs/lda

165. 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.

166. Q+A
Rand Fishkin, CEO & Co-Founder, SEOmoz
• Twitter: @randfish
• Blog: www.seomoz.org/blog
• Email: rand@seomoz.org