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Links & Rankings
The Story in the Data
1998
@THCapper
You navigate
the web using
links
@THCapper
So links are a useful
proxy for popularity & trust
@THCapper
2017
@THCapper
There are now other ways
to browse the web
@THCapper
& Google doesn’t need to
approximate popularity
@THCapper
Google is a browser
@THCapper
Google is an ISP
@THCapper
Google is, of course,
a dominant search engine
@THCapper
So they have the real data
on user behaviour.
No need for a proxy.
@THCapper
& links have become a dirty signal
@THCapper
So what, for Google, is the point in links?
Are they obsolete?
@THCapper
First: House rules
@THCapper
Don’t tweet this:
@THCapper
Do tweet this:
@THCapper
Has it already happened?
What could replace links?
What should you do next?
1. What could replace links?
The obvious answer:
Machine learning + user signals
@THCapper
http://dis.tl/LarryCTR
The less obvious answer:
Brand
What if you could find a way to measure brand?
We all struggle with this.
@THCapper
This is elementary for Google.
@THCapper
Has it already happened?
What could replace links?
What should you do next?
Has it already happened?
What does Google say?
@THCapper
https://youtu.be/l8VnZCcl9J4
@THCapper
“And I can tell you what they are.
It is content. And it’s links pointing to your site.”
Andrey Lipattsev, Searc...
@THCapper
Question: Are links already redundant?
● Google: No
Counterclaim:
Google is routinely wrong technically
correct about how Google works
@THCapper
Classic examples:
● HTTPS migrations pre-2016
● 302s are as good as 301s
● Subdomains are as good as sub-folders
● CCTLDs ...
@THCapper
Question: Are links already redundant?
● Google: No
Correlations
Lots of people have found correlations
@THCapper
@THCapper
http://dis.tl/MozCorrelations
@THCapper
http://dis.tl/MozCorrelations
We all know that correlation
does not imply causation
@THCapper
But causation & coincidence
are not the only possibilities
@THCapper
We’ve all enjoyed this
@THCapper
http://dis.tl/TylerVigen
And this
@THCapper
http://dis.tl/TylerVigen
But how do these happen?
@THCapper
Potential Mechanisms
1. Complete coincidence - Nicholas Cage and drownings are in fact unrelated (!)
@THCapper
Potential Mechanisms
1. Complete coincidence - Nicholas Cage and drownings are in fact unrelated (!)
2. Linearity - both c...
Potential Mechanisms
1. Complete coincidence - Nicholas Cage and drownings are in fact unrelated (!)
2. Linearity - both c...
Potential Mechanisms
1. Complete coincidence - Nicholas Cage and drownings are in fact unrelated (!)
2. Linearity - both c...
Potential Mechanisms
1. Complete coincidence - Nicholas Cage and drownings are in fact unrelated (!)
2. Linearity - both c...
Potential Mechanisms
1. Complete coincidence - Nicholas Cage and drownings are in fact unrelated (!)
2. Linearity - both c...
Affluence causes:
● Cheese consumption
● Bedsheet deaths
@THCapper
@THCapper
Brand awareness causes:
● Links
● Rankings?
Brand awareness causes:
● Links
● Rankings?
This would explain those studies.
@THCapper
@THCapper
Question: Are links already redundant?
● Google: No
● Correlation Studies: Inconclusive
So how does brand awareness compare?
@THCapper
@THCapper
http://dis.tl/MozCorrelations
@THCapper
http://dis.tl/BSVdata
@THCapper
Therefore:
If you care about DA, you should care
about Branded Search Volume
@THCapper
@THCapper
@THCapper
Question: Are links already redundant?
● Google: No
● Correlation Studies: Inconclusive
● My Data: Yes
But
@THCapper
Statistical Significance: 99.9999999999999999
999999999999999999999999999999999999999999999999999999
999999999999999999999...
@THCapper
Qualitatively, what does
ranking flux look like?
Real World Example 1: Flowers
@THCapper
Keyword: Flowers
Market: GB-en
Period: May-Dec 2016
Device: Smartphone
(This is all public data)
@THCapper
@THCapper
What do we notice?
1. Highly erratic
@THCapper
@THCapper
What do we notice?
1. Highly erratic
2. Dominant site collapsed
@THCapper
@THCapper
What do we notice?
1. Highly erratic
2. Dominant site collapsed
3. DA 33 site overtakes DA 53 site(s)
@THCapper
Old-school ranking factors:
1. On-site
2. Algorithm updates
3. Links
@THCapper
Old-school ranking factors:
1. On-site
2. Algorithm updates
3. Links
@THCapper
http://dis.tl/2016algo
@THCapper
Old-school ranking factors:
1. On-site
2. Algorithm updates
3. Links
@THCapper
Site A (DA 53)
Site B (DA 33)
Site A (DA 53)
Site B (DA 33)
@THCapper
40 domains
40 domains
@THCapper
Old-school ranking factors:
1. On-site
2. Algorithm updates
3. Links
@THCapper
This is not unusual.
@THCapper
Takeaway 1:
Google is continuously iterating
@THCapper
Takeaway 2:
(Users like) Aesthetics & Price
@THCapper
Real World Example 2: Fleximize.com
@THCapper
@THCapper
@THCapper
@THCapper
@THCapper
Content piece gains 168 referring domains
@THCapper
Content piece gains 22 referring domains
@THCapper
Content piece gains 191 referring domains
Takeaway:
Links move the needle ...sometimes?
@THCapper
Question: Are links already redundant?
● Google: No
● Correlation Studies: Inconclusive
● My Data: Yes
● Anecdotal: Mixed
...
Bringing all this together
An explanation that is consistent
with all of this
@THCapper
Perhaps there are now two tiers.
@THCapper
1. At the competitive, data-rich top end,
links mean increasingly little
@THCapper
@THCapper
1. At the competitive, data-rich top end,
links mean increasingly little
2. But, for now, links might be a big p...
Has it already happened?
What could replace links?
What should you do next?
What should you do next?
Win at user testing
User testing for SEO: Places to start
1. Panda surveys
2. Click-through rate experiments
3. Plain old CRO - especially foc...
Win at brand awareness & perception
@THCapper
If you want to build links, think:
Would Google value this tactic
in a world without links?
@THCapper
Thank You
@THCapper
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Links & rankings, the story in the data - BrightonSEO April 2017

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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?

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Links & rankings, the story in the data - BrightonSEO April 2017

  1. 1. Links & Rankings The Story in the Data
  2. 2. 1998 @THCapper
  3. 3. You navigate the web using links @THCapper
  4. 4. So links are a useful proxy for popularity & trust @THCapper
  5. 5. 2017 @THCapper
  6. 6. There are now other ways to browse the web @THCapper
  7. 7. & Google doesn’t need to approximate popularity @THCapper
  8. 8. Google is a browser @THCapper
  9. 9. Google is an ISP @THCapper
  10. 10. Google is, of course, a dominant search engine @THCapper
  11. 11. So they have the real data on user behaviour. No need for a proxy. @THCapper
  12. 12. & links have become a dirty signal @THCapper
  13. 13. So what, for Google, is the point in links? Are they obsolete? @THCapper
  14. 14. First: House rules @THCapper
  15. 15. Don’t tweet this: @THCapper
  16. 16. Do tweet this: @THCapper
  17. 17. Has it already happened? What could replace links? What should you do next?
  18. 18. 1. What could replace links?
  19. 19. The obvious answer: Machine learning + user signals
  20. 20. @THCapper http://dis.tl/LarryCTR
  21. 21. The less obvious answer: Brand
  22. 22. What if you could find a way to measure brand? We all struggle with this. @THCapper
  23. 23. This is elementary for Google. @THCapper
  24. 24. Has it already happened? What could replace links? What should you do next?
  25. 25. Has it already happened?
  26. 26. What does Google say?
  27. 27. @THCapper https://youtu.be/l8VnZCcl9J4
  28. 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. 29. @THCapper Question: Are links already redundant? ● Google: No
  30. 30. Counterclaim: Google is routinely wrong technically correct about how Google works @THCapper
  31. 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. 32. @THCapper Question: Are links already redundant? ● Google: No
  33. 33. Correlations
  34. 34. Lots of people have found correlations @THCapper
  35. 35. @THCapper http://dis.tl/MozCorrelations
  36. 36. @THCapper http://dis.tl/MozCorrelations
  37. 37. We all know that correlation does not imply causation @THCapper
  38. 38. But causation & coincidence are not the only possibilities @THCapper
  39. 39. We’ve all enjoyed this @THCapper http://dis.tl/TylerVigen
  40. 40. And this @THCapper http://dis.tl/TylerVigen
  41. 41. But how do these happen? @THCapper
  42. 42. Potential Mechanisms 1. Complete coincidence - Nicholas Cage and drownings are in fact unrelated (!) @THCapper
  43. 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. 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. 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. 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. 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. 48. Affluence causes: ● Cheese consumption ● Bedsheet deaths @THCapper
  49. 49. @THCapper Brand awareness causes: ● Links ● Rankings?
  50. 50. Brand awareness causes: ● Links ● Rankings? This would explain those studies. @THCapper
  51. 51. @THCapper Question: Are links already redundant? ● Google: No ● Correlation Studies: Inconclusive
  52. 52. So how does brand awareness compare? @THCapper
  53. 53. @THCapper http://dis.tl/MozCorrelations
  54. 54. @THCapper http://dis.tl/BSVdata
  55. 55. @THCapper
  56. 56. Therefore: If you care about DA, you should care about Branded Search Volume @THCapper
  57. 57. @THCapper
  58. 58. @THCapper Question: Are links already redundant? ● Google: No ● Correlation Studies: Inconclusive ● My Data: Yes
  59. 59. But @THCapper
  60. 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. 61. @THCapper
  62. 62. Qualitatively, what does ranking flux look like?
  63. 63. Real World Example 1: Flowers
  64. 64. @THCapper Keyword: Flowers Market: GB-en Period: May-Dec 2016 Device: Smartphone (This is all public data)
  65. 65. @THCapper
  66. 66. @THCapper What do we notice? 1. Highly erratic
  67. 67. @THCapper
  68. 68. @THCapper What do we notice? 1. Highly erratic 2. Dominant site collapsed
  69. 69. @THCapper
  70. 70. @THCapper What do we notice? 1. Highly erratic 2. Dominant site collapsed 3. DA 33 site overtakes DA 53 site(s)
  71. 71. @THCapper Old-school ranking factors: 1. On-site 2. Algorithm updates 3. Links
  72. 72. @THCapper Old-school ranking factors: 1. On-site 2. Algorithm updates 3. Links
  73. 73. @THCapper http://dis.tl/2016algo
  74. 74. @THCapper Old-school ranking factors: 1. On-site 2. Algorithm updates 3. Links
  75. 75. @THCapper Site A (DA 53) Site B (DA 33)
  76. 76. Site A (DA 53) Site B (DA 33) @THCapper 40 domains 40 domains
  77. 77. @THCapper Old-school ranking factors: 1. On-site 2. Algorithm updates 3. Links
  78. 78. @THCapper
  79. 79. This is not unusual. @THCapper
  80. 80. Takeaway 1: Google is continuously iterating @THCapper
  81. 81. Takeaway 2: (Users like) Aesthetics & Price @THCapper
  82. 82. Real World Example 2: Fleximize.com
  83. 83. @THCapper
  84. 84. @THCapper
  85. 85. @THCapper
  86. 86. @THCapper
  87. 87. @THCapper Content piece gains 168 referring domains
  88. 88. @THCapper Content piece gains 22 referring domains
  89. 89. @THCapper Content piece gains 191 referring domains
  90. 90. Takeaway: Links move the needle ...sometimes? @THCapper
  91. 91. Question: Are links already redundant? ● Google: No ● Correlation Studies: Inconclusive ● My Data: Yes ● Anecdotal: Mixed @THCapper
  92. 92. Bringing all this together
  93. 93. An explanation that is consistent with all of this @THCapper
  94. 94. Perhaps there are now two tiers. @THCapper
  95. 95. 1. At the competitive, data-rich top end, links mean increasingly little @THCapper
  96. 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. 97. Has it already happened? What could replace links? What should you do next?
  98. 98. What should you do next?
  99. 99. Win at user testing
  100. 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. 101. Win at brand awareness & perception
  102. 102. @THCapper
  103. 103. If you want to build links, think:
  104. 104. Would Google value this tactic in a world without links? @THCapper
  105. 105. Thank You @THCapper
  • therory

    Feb. 20, 2019
  • jegams

    Mar. 27, 2018
  • neilkwhitehead

    May. 16, 2017
  • AhmedMakda3

    Apr. 24, 2017
  • MarkUrbanov

    Apr. 21, 2017
  • leonievanderplas3

    Apr. 12, 2017
  • illorenzo

    Apr. 8, 2017

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?

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