This document summarizes Rand Fishkin's presentation on SEO in a two algorithm world. It discusses how Google's search algorithm has evolved from primarily considering links to now using machine learning and user signals like click-through rates and engagement. It recommends that SEO professionals optimize for both algorithms by focusing on factors like filling content gaps, driving task completion, and creating content that earns loyalty and amplification rather than just links. The future of SEO involves balancing traditional on-site optimization with a new approach focused on searcher outputs and engagement.

1. Rand Fishkin, Wizard of Moz | @randfish | rand@moz.com
SEO in a Two Algorithm World

2. bit.ly/twoalgo
Get the presentation:

3. State of Search
November 16th, 2015 8:00am
Dallas, TX

4. Remember
When…

5. We Had One Job

6. Perfectly Optimized Pages

7. The Search Quality
Teams Determined
What to Include in
the Ranking System

8. They decided
links > content

9. By 2007, Link Spam Was Ubiquitous
This paper/presentation
from Yahoo’s spam team in
2007 predicted a lot of what
Google would launch in
Penguin Oct, 2012
(including machine learning)

10. Even in 2012, It Felt Like Google Was Making Liars Out
of the White Hat SEO World
Via Wil Reynolds

11. Google’s Last 3 Years of
Advancements Erased a
Decade of Old School SEO
Practices

12. They Finally Launched EffectiveAlgorithms to Fight
Manipulative Links & Content
Via Google

13. And They Leveraged Fear + Uncertainty of
Penalization to Keep Sites Inline
Via Moz Q+A

14. Google Figured Out Intent
Rand probably
doesn’t just want
webpages filled
with the word
“beef”

16. They Looked at Language, not Just Keywords
Oh… I totally
know this one!

18. They Predicted When We Want Diverse Results
He probably
doesn’t just
want a bunch of
lists.

20. They Figured Out When We Wanted Freshness
Old pages on this
topic probably
aren’t relevant
anymore

22. Their Segmentation of Navigational from Informational
Queries Closed Many Loopholes

23. Google Learned to ID Entities of Knowledge

24. And to Connect Entities to Topics & Keywords
Via Moz

25. Brands Became a Form of Entities

26. TheseAdvancements Brought Google (mostly)
Back in Line w/ Its Public Statements
Via Google

27. During These Advances,
Google’s Search Quality
Team Underwent a
Revolution

28. Early On, Google Rejected Machine Learning in the
Organic RankingAlgo
Via Datawocky,
2008

29. Amit Singhal Shared Norvig’s ConcernsAbout ML
Via Quora

30. In 2012, Google Published a PaperAbout How
they Use ML to Predict Ad CTRs:
Via Google

31. 2012
“Our SmartASS system is a
machine learning system. It
learns whether our users
are interested in that ad,
and whether users are going
to click on them.”

32. By 2013, It Was
Something Google’s
Search Folks Talked
About Publicly
Via SELand

33. As MLTakes Over More of Google’sAlgo, the
Underpinnings of the Rankings Change
Via Colossal

34. Google is PublicAbout How They Use MLin Image
Recognition & Classification
Potential ID Factors
(e.g. color, shapes,
gradients, perspective,
interlacing, alt tags,
surrounding text, etc)
Training Data
(i.e. human-labeled images)
Learning
Process
Best
Match
Algo

35. Google is PublicAbout How They Use MLin Image
Recognition & Classification
Via Jeff Dean’s Slides on Deep Learning; a Must Read for SEOs

36. Machine Learning in Search Could Work Like This:
Potential Ranking
Factors
(e.g. PageRank, TF*IDF,
Topic Modeling, QDF, Clicks,
Entity Association, etc.)
Training Data
(i.e. good & bad search
results)
Learning
Process
Best Fit
Algo

37. Training Data
(e.g. good search results)
This is a good SERP –
searchers rarely bounce, rarely
short-click, and rarely need to
enter other queries or go to
page 2.

38. Training Data
(e.g. bad search results!)
This is a bad SERP –
searchers bounce often,
click other results, rarely
long-click, and try other
queries. They’re definitely
not happy.

39. The Machines Learn to Emulate the Good Results & Try to Fix
orTweak the Bad Results
Potential Ranking
Factors
(e.g. PageRank, TF*IDF,
Topic Modeling, QDF, Clicks,
Entity Association, etc.)
Training Data
(i.e. good & bad search
results)
Learning
Process
Best Fit
Algo

40. Deep Learning is Even MoreAdvanced:
Dean says by using deep
learning, they don’t have to
tell the system what a cat is,
the machines learn,
unsupervised, for
themselves…

41. We’re TalkingAbout
Algorithms that Build
Algorithms
(without human
intervention)

42. Googlers Don’t Feed in Ranking Factors… The Machines
Determine Those Themselves.
Potential Ranking
Factors
(e.g. PageRank, TF*IDF,
Topic Modeling, QDF, Clicks,
Entity Association, etc.)
Training Data
(i.e. good search results)
Learning
Process
Best Fit
Algo

43. No wonder these guys are stressed about Google
unleashing the Terminators 
Via CNET & Washington Post

44. What Does Deep Learning
Mean for SEO?

45. Googlers Won’t Know Why Something Ranks or
Whether a Variable’s in theAlgo
He means other Googlers.
I’m Jeff Dean. I’ll know.

46. The Query Success Metrics Will BeAll That
Matters to the Machines
Long to Short Click Ratio Relative CTR vs. Other Results
Rate of Searchers Conducting
Additional, Related Searches
Metrics of User Engagement
on the Page
Metrics of User Engagement
Across the Domain
Sharing/Amplifcation Rate
vs. Other Results

47. The Query Success Metrics Will BeAll That
Matters to the Machines
Long to Short Click Ratio Relative CTR vs. Other Results
Rate of Searchers Conducting
Additional, Related Searches
Metrics of User Engagement
on the Page
Metrics of User Engagement
Across the Domain
Sharing/Amplifcation Rate
vs. Other Results
If lots of results on a SERP
do these well, and higher
results outperform lower
results, our deep learning
algo will consider it a
success.

48. We’ll Be Optimizing Less
for Ranking Inputs
Unique Linking Domains
Keywords in Title
Anchor Text
Content Uniqueness
Page Load Speed

49. And Optimizing More for Searcher Outputs
High CTR for this position?
Good engagement?
High amplification rate?
Low bounce rate?
Strong pages/visit after
landing on this URL?These are likely to be the
criteria of on-site SEO’s future… People return to the site
after an initial search visit

50. OK… Maybe in the future. But,
do those kinds of metrics really
affect SEO today?

51. Remember Our Queries & Clicks Test from 2014?
Via Rand’s Blog

52. Since then, it’s been much harder to move the
needle with raw queries and clicks…

53. Case closed! Google says they don’t use clicks in the rankings.
Via Linkarati’s Coverage of SMX Advanced

54. But, what if we tried long
clicks vs.
short clicks?
Note SeriousEats,
ranking #4 here

55. 11:39am on June 21st,
I sent this tweet:

56. 40 Minutes & ~400
Interactions Later
Moved up 2 positions after 2+
weeks of the top 5 staying
static.

57. 70 Minutes & ~500
Interactions Total
Moved up to #1.

58. Stayed ~12 hours, when it
fell to #13+ for ~8 hours, then
back to #4.
Google? You
messing with us?

59. Via Google Trends, we can see the relative impact
of the test on query volume
~5-10X normal volume
over 3-4 hours

60. BTW – This is hard to replicate.
600+ real searchers using a
variety of devices, browsers,
accounts, geos, etc. will not look
the same to Google as a Fiverr
buy, a clickfarm, or a bot. And
note how G penalized the page
after the test… They might not put
it back if they thought the site
itself was to blame for the click
manipulation.

61. OK… Maybe in the future. But,
do those kinds of metrics really
affect SEO today?

62. Via Bloomberg Business

63. The Future:
Optimizing for Two
Algorithms

64. The Best SEOs HaveAlways
Optimized to Where Google’s Going

65. Today, I Think We Know,
Better Than Ever, Where That Is
Welcome to your new home, the User/Usage Signals + ML Model Cabin

66. We Must Choose How to Balance Our Work…

67. Hammering on the Fading Signals of Old…

68. Or Embracing Those We
Can See On the Rise

69. Classic SEO
(ranking inputs)
New SEO
(searcher outputs)
Keyword Targeting Relative CTR
Short vs. Long-Click
Content Gap Fulfillment
Task Completion
Success
Amplification & Loyalty
Quality & Uniqueness
Crawl/Bot Friendly
Snippet Optimization
UX / Multi-Device
Branded Search & TrafficLinks & Anchor Text

70. 5 New(ish) Elements of
Modern SEO

71. Punching Above Your
Ranking’s Average CTR#1

72. Optimizing the Title, Meta Description, & URL
a Little for KWs, but a Lot for Clicks
If you rank #3, but have a higher-
than-average CTR for that
position, you might get moved up.
Via Philip Petrescu on Moz

73. Every Element Counts
Does the title match
what searchers want?
Does the URL seem
compelling?
Do searchers
recognize & want to
click your domain?
Is your result fresh?
Do searchers want a
newer result?
Does the description
create curiosity &
entice a click?
Do you get the
brand dropdown?

74. Given Google Often Tests New Results Briefly on Page One…
ItMayBeWorthRepeatedPublicationonaTopictoEarnthatHighCTR
Shoot! My post only made it to #15…
Perhaps I’ll try again in a few
months.

75. Driving Up CTR Through Branding Or Branded
Searches May GiveAn Extra Boost

76. #1 Ad Spender
#2 Ad Spender
#4 Ad Spender
#3 Ad Spender
#5 Ad Spender

77. With Google
Trends’ new, more
accurate, more
customizable
ranges, you can
actually watch the
effects of events
and ads on search
query volume
Fitbit has been running ads on
Sunday NFL games that clearly
show in the search trends data.

78. Beating Out Your Fellow SERP
Residents on Engagement#2

79. Together, Pogo-Sticking & Long Clicks Might
Determine a Lot of Where You Rank (and for how
long)
Via Bill Slawski on Moz

80. What Influences Them?

81. Speed, Speed, and More Speed
Delivers the Best UX on Every Browser
Compels Visitors to Go Deeper Into Your Site
Avoids Features that Annoy or Dissuade Visitors
Content that Fulfills the Searcher’s Conscious &
Unconscious Needs
An SEO’s Checklist for Better Engagement:

82. Via NY Times
e.g. this interactive
graph that asks visitors
to draw their best
guess likely gets
remarkable
engagement

83. e.g. Poor Norbert
does a terrible job
at SEO, but the
simplicity compels
visitors to go
deeper and to
return time and
again
Via VoilaNorbert

84. e.g. Nomadlist’s
superb, filterable
database of cities and
community for remote
workers.
Via Nomadlist

85. Filling Gaps in Your
Visitors’ Knowledge#3

86. Google’s looking for
content signals that a
page will fulfill ALL of
a searcher’s needs.
I think I know a
few ways to
figure that out.

87. ML models may note
that the presence of
certain words,
phrases, & topics
predict more
successful searches

88. e.g. a page about New York that doesn’t
mention Brooklyn or Long Island may
not be very comprehensive

89. IfYour Content Doesn’t Fill the Gaps in Searcher’s Needs…
e.g. for this query, Google
might seek content that
includes topics like “text
classification,”
“tokenization,” “parsing,”
and “question answering”
Those Rankings Go to Pages/Sites That Do.

90. Moz’s Data Science Team
is Working on Something to
Help With This
The (alpha) tool extracts
likely focal topics from a
given page, which can
then be compared vs. an
engines top 10 results

91. In the meantime, check
out
AlchemyAPI
Or MonkeyLearn

92. Fulfilling the Searcher’s Task
(not just their query)#4

93. Broad search Narrower search
Even narrower
search
Website visit
Website
visit
Brand
search
Social validation Highly-specific search
Type-in/direct visit Completion of Task
Google Wants to Get SearchersAccomplishing
Their Tasks Faster

94. Broad search
All the sites (or answers) you probably
would have visited/sought along that path
Completion of Task
This is Their Ultimate Goal:

95. If Google sees
that many
people who
perform these
types of
queries:

96. Eventually end
their queries on
the topic after
visiting Ramen
Rater…
The Ramen Rater

97. They might use the
clickstream data to
help rank that site
higher, even if it
doesn’t have
traditional ranking
signals

98. They’re definitely getting and storing it.

99. APage ThatAnswers the Searcher’s Initial Query
May Not Be Enough
Searchers performing this
query are likely to have the
goal of completing a
transaction

100. Google Wants to Send Searchers
to Websites that Resolve their
Mission
This is the only site
where you can reliably
find the back issues
and collector covers

101. Earning More Shares, Links,
& Loyalty per Visit#5

102. Pages that get lots of
social activity &
engagement, but few
links, seem to
overperform…

103. Google says they
don’t use social
signals directly, but
examples like these
make SEOs
suspicious

104. Even for insanely competitive
keywords, we see this type of
behavior when a URLgets
authentically “hot” in the
social world.

105. Data from Buzzsumo & Moz
show that very few articles
earn sharesAND that links &
shares have almost no
correlation.
Via Buzzsumo &

106. I suspect Google doesn’t
use raw social shares as
a ranking input, because
we share a lot of content
with which we don’t
engage:
Via Chartbeat

107. Google Could Be Using a Lot of Other Metrics/Sources to Get
Data That Mimics Social Shares:
Clickstream (from Chrome/Android)
Engagement (from Chrome/Android)
Branded Queries (from Search)
Navigational Queries (from Search)
Rate of Link Growth (from Crawl)

108. But I Don’t Care if It’s Correlation or Causation;
I Want to Rank Like These Guys!

109. BTW – GoogleAlmost Certainly Classifies SERPs
Differently & Optimizes to Different Goals
These URLs have loads of shares & may have high
loyalty, but for medical queries, Google has different
priorities

110. Knowing What Makes OurAudience (and their
influencers) Share is Essential
From an analysis of
the 10,000 pieces of
content receiving the
most social shares on
the web by
Buzzsumo.

111. Knowing What Makes them Return (or prevents
them from doing so) Is, Too.

112. We Don’t Need “Better” Content… We Need “10X” Content.
Via Whiteboard Friday
Wrong Question:
“How do we make something as
good as this?”
Right Question:
“How do we make something 10X
better than any of these?”

113. 10X Content is the Future, Because It’s the Only Way to Stand
Out from the Increasingly-Noisy Crowd
http://www.simplereach.com/blog/facebook-continues-to-be-the-
biggest-driver-of-social-traffic/
The top 10% of content
gets all the social shares
and traffic.

114. Old School On-Site Old School Off-Site
Keyword Targeting Link Diversity
Anchor Text
Brand Mentions
3rd Party Reviews
Reputation Management
Quality & Uniqueness
Crawl/Bot Friendly
Snippet Optimization
UX / Multi-Device
None of our old school tactics will get this
done.

115. We Have to Go From This:
Wikipedia on Vince Carter (currently ranking #10 for “Vince Carter Dunks”)

116. ToThis:
Via ESPN

117. I’ve Been Curating a List of “10X” Content Over the Last
8 months… It’sAll Yours:
bit.ly/10Xcontent
FYI that’s a capital “X”

118. Welcome to the
Two-Algorithm World of
2015

119. Algo 1: Google

120. Algo 2: Subset of Humanity
that Interacts With Your
Content

121. “Make Pages for People, Not
Engines.”

122. Terrible Advice.

123. Keyword Targeting Relative CTR
Short vs. Long-Click
Content Gap Fulfillment
Amplify & Return Rates
Task Completion
Success
Quality & Uniqueness
Crawl/Bot Friendly
Snippet Optimization
UX / Multi-Device
Engines People

124. Optimize for Both:
Algo Input & Human Output

125. Rand Fishkin, Wizard of Moz | @randfish | rand@moz.com
bit.ly/twoalgo