What does the data tell us about the relationship between links and rankings in 2017? Is there a story that is consistent with everything we see?

1. Links & Rankings
The Story in the Data

2. 1998
@THCapper

3. You navigate
the web using
links
@THCapper

4. So links are a useful
proxy for popularity & trust
@THCapper

5. 2017
@THCapper

6. There are now other ways
to browse the web
@THCapper

7. & Google doesn’t need to
approximate popularity
@THCapper

8. Google is a browser
@THCapper

9. Google is an ISP
@THCapper

10. Google is, of course,
a dominant search engine
@THCapper

11. So they have the real data
on user behaviour.
No need for a proxy.
@THCapper

12. & links have become a dirty signal
@THCapper

13. So what, for Google, is the point in links?
Are they obsolete?
@THCapper

14. First: House rules
@THCapper

15. Don’t tweet this:
@THCapper

16. Do tweet this:
@THCapper

17. Has it already happened?
What could replace links?
What should you do next?

18. 1. What could replace links?

19. The obvious answer:
Machine learning + user signals

20. @THCapper
http://dis.tl/LarryCTR

21. The less obvious answer:
Brand

22. What if you could find a way to measure brand?
We all struggle with this.
@THCapper

23. This is elementary for Google.
@THCapper

24. Has it already happened?
What could replace links?
What should you do next?

25. Has it already happened?

26. What does Google say?

27. @THCapper
https://youtu.be/l8VnZCcl9J4

28. @THCapper
“And I can tell you what they are.
It is content. And it’s links pointing to your site.”
Andrey Lipattsev, Search Quality Senior Strategist, Google
https://youtu.be/l8VnZCcl9J4

29. @THCapper
Question: Are links already redundant?
● Google: No

30. Counterclaim:
Google is routinely wrong technically
correct about how Google works
@THCapper

31. Classic examples:
● HTTPS migrations pre-2016
● 302s are as good as 301s
● Subdomains are as good as sub-folders
● CCTLDs are as good as .com
@THCapper

32. @THCapper
Question: Are links already redundant?
● Google: No

33. Correlations

34. Lots of people have found correlations
@THCapper

35. @THCapper
http://dis.tl/MozCorrelations

36. @THCapper
http://dis.tl/MozCorrelations

37. We all know that correlation
does not imply causation
@THCapper

38. But causation & coincidence
are not the only possibilities
@THCapper

39. We’ve all enjoyed this
@THCapper
http://dis.tl/TylerVigen

40. And this
@THCapper
http://dis.tl/TylerVigen

41. But how do these happen?
@THCapper

42. Potential Mechanisms
1. Complete coincidence - Nicholas Cage and drownings are in fact unrelated (!)
@THCapper

43. Potential Mechanisms
1. Complete coincidence - Nicholas Cage and drownings are in fact unrelated (!)
2. Linearity - both cheese consumption and bedsheet-related deaths are
trending linearly, and thus loosely correlated
@THCapper

44. Potential Mechanisms
1. Complete coincidence - Nicholas Cage and drownings are in fact unrelated (!)
2. Linearity - both cheese consumption and bedsheet-related deaths are
trending linearly , and thus loosely correlated
@THCapper

45. Potential Mechanisms
1. Complete coincidence - Nicholas Cage and drownings are in fact unrelated (!)
2. Linearity - both cheese consumption and bedsheet-related deaths are
trending linearly, and thus loosely correlated
3. Reverse causation - it is in fact drownings that cause Nicholas Cage films, not
vice versa
@THCapper

46. Potential Mechanisms
1. Complete coincidence - Nicholas Cage and drownings are in fact unrelated (!)
2. Linearity - both cheese consumption and bedsheet-related deaths are
trending linearly, and thus loosely correlated
3. Reverse causation - it is in fact drownings that cause Nicholas Cage films, not
vice versa
Or in our case...
@THCapper

47. Potential Mechanisms
1. Complete coincidence - Nicholas Cage and drownings are in fact unrelated (!)
2. Linearity - both cheese consumption and bedsheet-related deaths are
trending linearly, and thus loosely correlated
3. Reverse causation - it is in fact drownings that cause Nicholas Cage films, not
vice versa
4. Joint causation - both cheese consumption and deaths in bedsheets are
related to increasing affluence (& effluence)
@THCapper

48. Affluence causes:
● Cheese consumption
● Bedsheet deaths
@THCapper

49. @THCapper
Brand awareness causes:
● Links
● Rankings?

50. Brand awareness causes:
● Links
● Rankings?
This would explain those studies.
@THCapper

51. @THCapper
Question: Are links already redundant?
● Google: No
● Correlation Studies: Inconclusive

52. So how does brand awareness compare?
@THCapper

53. @THCapper
http://dis.tl/MozCorrelations

54. @THCapper
http://dis.tl/BSVdata

55. @THCapper

56. Therefore:
If you care about DA, you should care
about Branded Search Volume
@THCapper

57. @THCapper

58. @THCapper
Question: Are links already redundant?
● Google: No
● Correlation Studies: Inconclusive
● My Data: Yes

59. But
@THCapper

60. Statistical Significance: 99.9999999999999999
999999999999999999999999999999999999999999999999999999
9999999999999999999999999%
R Squared: ~1%
Pearson’s Correlation: ~0.1
Which means that there’s something I’m
missing.
@THCapper

61. @THCapper

62. Qualitatively, what does
ranking flux look like?

63. Real World Example 1: Flowers

64. @THCapper
Keyword: Flowers
Market: GB-en
Period: May-Dec 2016
Device: Smartphone
(This is all public data)

65. @THCapper

66. @THCapper
What do we notice?
1. Highly erratic

67. @THCapper

68. @THCapper
What do we notice?
1. Highly erratic
2. Dominant site collapsed

69. @THCapper

70. @THCapper
What do we notice?
1. Highly erratic
2. Dominant site collapsed
3. DA 33 site overtakes DA 53 site(s)

71. @THCapper
Old-school ranking factors:
1. On-site
2. Algorithm updates
3. Links

72. @THCapper
Old-school ranking factors:
1. On-site
2. Algorithm updates
3. Links

73. @THCapper
http://dis.tl/2016algo

74. @THCapper
Old-school ranking factors:
1. On-site
2. Algorithm updates
3. Links

75. @THCapper
Site A (DA 53)
Site B (DA 33)

76. Site A (DA 53)
Site B (DA 33)
@THCapper
40 domains
40 domains

77. @THCapper
Old-school ranking factors:
1. On-site
2. Algorithm updates
3. Links

78. @THCapper

79. This is not unusual.
@THCapper

80. Takeaway 1:
Google is continuously iterating
@THCapper

81. Takeaway 2:
(Users like) Aesthetics & Price
@THCapper

82. Real World Example 2: Fleximize.com

83. @THCapper

84. @THCapper

85. @THCapper

86. @THCapper

87. @THCapper
Content piece gains 168 referring domains

88. @THCapper
Content piece gains 22 referring domains

89. @THCapper
Content piece gains 191 referring domains

90. Takeaway:
Links move the needle ...sometimes?
@THCapper

91. Question: Are links already redundant?
● Google: No
● Correlation Studies: Inconclusive
● My Data: Yes
● Anecdotal: Mixed
@THCapper

92. Bringing all this together

93. An explanation that is consistent
with all of this
@THCapper

94. Perhaps there are now two tiers.
@THCapper

95. 1. At the competitive, data-rich top end,
links mean increasingly little
@THCapper

96. @THCapper
1. At the competitive, data-rich top end,
links mean increasingly little
2. But, for now, links might be a big part
of what gets you into that shortlist.

97. Has it already happened?
What could replace links?
What should you do next?

98. What should you do next?

99. Win at user testing

100. User testing for SEO: Places to start
1. Panda surveys
2. Click-through rate experiments
3. Plain old CRO - especially focusing on initial bounce
4. All of the above: Mobile first
None of this is new!
@THCapper

101. Win at brand awareness & perception

102. @THCapper

103. If you want to build links, think:

104. Would Google value this tactic
in a world without links?
@THCapper

105. Thank You
@THCapper