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SearchLove San Diego 2017 | Tom Capper | Does Google Still Need Links?

SearchLove San Diego 2017 | Tom Capper | Does Google Still Need Links?

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Google now has a whole host of signals to choose from, and while links still seem to correlate with rankings, the relationship is more complex than ever. In this presentation, Tom will explore whether and when links still count, what might replace them, and how SEOs should build site equity in 2017 and beyond.

Google now has a whole host of signals to choose from, and while links still seem to correlate with rankings, the relationship is more complex than ever. In this presentation, Tom will explore whether and when links still count, what might replace them, and how SEOs should build site equity in 2017 and beyond.

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SearchLove San Diego 2017 | Tom Capper | Does Google Still Need Links?

  1. 1. Does Google Still Need Links? Off-Site Ranking Factors for 2017
  2. 2. MozCon, September 2016 @THCapper
  3. 3. http://dis.tl/RandTipping @THCapper
  4. 4. http://dis.tl/RandTipping @THCapper
  5. 5. PageRank & links were a proxy for user behaviour @THCapper
  6. 6. Google doesn’t need a proxy anymore @THCapper
  7. 7. Google is a browser @THCapper
  8. 8. Google is an ISP @THCapper
  9. 9. Google is, of course, a dominant search engine @THCapper
  10. 10. & links have become a dirty signal @THCapper
  11. 11. (Rand says) Build links that might genuinely drive high quality traffic @THCapper
  12. 12. Today, taking this further @THCapper
  13. 13. I’m going to try to present both sides of this argument @THCapper
  14. 14. & I have some data to share with you @THCapper
  15. 15. Don’t tweet this: @THCapper
  16. 16. Do tweet this: @THCapper
  17. 17. Over the next 30 minutes: @THCapper
  18. 18. Has it already happened? What could replace links? What should you do next?
  19. 19. What could replace links?
  20. 20. What would you do? @THCapper
  21. 21. Machine learning
  22. 22. @THCapper http://dis.tl/LarryCTR
  23. 23. Brand
  24. 24. What if you could find a way to measure brand? We all struggle with this. @THCapper
  25. 25. This is elementary for Google. @THCapper
  26. 26. All of the above & much more besides
  27. 27. @THCapperhttp://dis.tl/CuttsPorn
  28. 28. All of these factors correlate with each other, and links @THCapper
  29. 29. Has it already happened? What could replace links? What should you do next?
  30. 30. Has it already happened?
  31. 31. What does Google say?
  32. 32. @THCapper https://youtu.be/l8VnZCcl9J4
  33. 33. @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
  34. 34. @THCapper Question: Are links already redundant? ●Google: No
  35. 35. End of talk?
  36. 36. Counterclaim: Google is routinely wrong technically correct about how Google works @THCapper
  37. 37. 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
  38. 38. @THCapper http://bit.ly/GaryDA
  39. 39. @THCapper Question: Are links already redundant? ●Google: No
  40. 40. Correlations
  41. 41. Lots of people have found correlations @THCapper
  42. 42. @THCapper http://dis.tl/MozCorrelations
  43. 43. @THCapper http://dis.tl/MozCorrelations
  44. 44. We all know that correlation does not imply causation @THCapper
  45. 45. But causation & coincidence are not the only possibilities @THCapper
  46. 46. We’ve all enjoyed this @THCapper http://dis.tl/TylerVigen
  47. 47. And this @THCapper http://dis.tl/TylerVigen
  48. 48. @THCapper
  49. 49. But how do these happen? @THCapper
  50. 50. Potential Mechanisms 1. Complete coincidence - Nicholas Cage and drownings are in fact unrelated (!) @THCapper
  51. 51. 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
  52. 52. 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
  53. 53. 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
  54. 54. 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
  55. 55. Affluence causes: ●Cheese consumption ●Bedsheet deaths @THCapper
  56. 56. Brand awareness causes: ●Links ●Rankings? @THCapper
  57. 57. @THCapper Question: Are links already redundant? ●Google: No ●Correlation Studies: Inconclusive
  58. 58. So how does brand awareness compare? @THCapper
  59. 59. @THCapper http://dis.tl/MozCorrelations
  60. 60. @THCapper Moz Study My Study 17,600 queries from KWP 4,900 queries from STAT
  61. 61. @THCapper Moz Study My Study 17,600 queries from KWP 4,900 queries from STAT Top 50 results Top 10 results
  62. 62. @THCapper Moz Study My Study 17,600 queries from KWP 4,900 queries from STAT Top 50 results Top 10 results Desktop only (?) Desktop & Smartphone
  63. 63. @THCapper Moz Study My Study 17,600 queries from KWP 4,900 queries from STAT Top 50 results Top 10 results Desktop only (?) Desktop & Smartphone Mean Spearman correlations Mean Spearman correlations
  64. 64. Quantifying Brand Awareness @THCapper
  65. 65. Branded Search Volume @THCapper
  66. 66. @THCapper
  67. 67. @THCapper
  68. 68. Therefore: If you care about DA, you should care about Branded Search Volume @THCapper
  69. 69. & here’s another interesting thing @THCapper
  70. 70. For my main data set, both variables are incredibly statistically significant @THCapper
  71. 71. @THCapper DA significance: 99.99999999999999999999999999999999 99999999999999999999999%
  72. 72. Log(branded search volume) significance: 99.99999999999999999999999999999999 9999999999999999999999999999999999 9999999999999999999999999999% @THCapper
  73. 73. For some clients, including both in the same model knocks DA out of statistical significance @THCapper
  74. 74. What does this mean? @THCapper
  75. 75. Branded Search Volume explains most of what can be explained with DA @THCapper
  76. 76. The reverse is not true. @THCapper
  77. 77. (Yes I will be publishing this data) @THCapper
  78. 78. @THCapper Question: Are links already redundant? ●Google: No ●Correlation Studies: Inconclusive ●My Data: Yes
  79. 79. @THCapper http://dis.tl/MarcusTober
  80. 80. Counterclaim: This might have been true in 1998 @THCapper
  81. 81. Qualitatively, what does ranking flux look like?
  82. 82. Real World Example 1: Flowers
  83. 83. @THCapper Keyword: Flowers Market: GB-en Period: May-Dec 2016 Device: Smartphone
  84. 84. @THCapper
  85. 85. @THCapper What do we notice? 1.Highly erratic
  86. 86. @THCapper
  87. 87. @THCapper What do we notice? 1.Highly erratic 2.Interflora collapsed
  88. 88. @THCapper
  89. 89. @THCapper What do we notice? 1.Highly erratic 2.Interflora collapsed 3.DA 33 site overtakes DA 53 site(s)
  90. 90. @THCapper Old-school ranking factors: 1.On-site 2.Algorithm updates 3.Links
  91. 91. @THCapper Old-school ranking factors: 1.On-site 2.Algorithm updates 3.Links
  92. 92. @THCapper http://dis.tl/2016algo
  93. 93. @THCapper Old-school ranking factors: 1.On-site 2.Algorithm updates 3.Links
  94. 94. @THCapper Interflora.co.uk Flyingflowers.co.uk
  95. 95. @THCapper Interflora.co.uk Flyingflowers.co.uk 40 domains 40 domains
  96. 96. @THCapper Old-school ranking factors: 1.On-site 2.Algorithm updates 3.Links
  97. 97. @THCapper
  98. 98. This is not unusual. @THCapper
  99. 99. Takeaway 1: Google is continuously iterating @THCapper
  100. 100. Takeaway 2: (Users like) Aesthetics & Price @THCapper
  101. 101. @THCapper
  102. 102. @THCapper
  103. 103. Real World Example 2: Fleximize.com
  104. 104. @THCapper
  105. 105. @THCapper
  106. 106. @THCapper
  107. 107. @THCapper
  108. 108. @THCapper Content piece gains 168 referring domains
  109. 109. @THCapper Content piece gains 22 referring domains
  110. 110. @THCapper Content piece gains 191 referring domains
  111. 111. Takeaway: Links move the needle ...sometimes? @THCapper
  112. 112. Question: Are links already redundant? ●Google: No ●Correlation Studies: Inconclusive ●My Data: Yes ●Anecdotal: Mixed @THCapper
  113. 113. So: Are links dead yet?
  114. 114. There is quantitative and qualitative evidence to suggest that links are not always the most important off-site factor. @THCapper
  115. 115. Bringing all this together
  116. 116. An explanation that is consistent with all of this @THCapper
  117. 117. There are now two tiers. @THCapper
  118. 118. 1.At the competitive, data-rich top end, links mean increasingly little @THCapper
  119. 119. @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.
  120. 120. Has it already happened? What could replace links? What should you do next?
  121. 121. What should you do next?
  122. 122. Win at user testing
  123. 123. User testing for SEO: Places to start @THCapper
  124. 124. User testing for SEO: Places to start 1. Panda surveys @THCapper https://youtu.be/At51X-aZ4Y4
  125. 125. User testing for SEO: Places to start 1. Panda surveys 2. Click-through rate experiments @THCapper
  126. 126. User testing for SEO: Places to start 1. Panda surveys 2. Click-through rate experiments 3. Plain old CRO - especially focusing on initial bounce @THCapper
  127. 127. 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 @THCapper
  128. 128. 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
  129. 129. Win at brand awareness & perception
  130. 130. (Content marketing, anyone?) @THCapper
  131. 131. (& this has additional benefits outside of digital) @THCapper
  132. 132. Google is trying to think like a person @THCapper
  133. 133. So cut out the middleman: Optimize for people @THCapper
  134. 134. If you want to build links, think:
  135. 135. Would Google value this tactic in a world without links? @THCapper
  136. 136. Closing thoughts
  137. 137. Has it already happened? What could replace links? What should you do next?
  138. 138. Thank You @THCapper

Notas del editor

  • So, back in September, I was in Seattle for MozCon
  • And this guy, who you may recognise as an up and coming face in our industry, gave a talk on link building
  • During his introduction, Rand said something that really got me thinking -

    about how Google has gone from being a link analysis company, with a conceptual understanding of how users surf the web,

    To being a machine learning company that uses real world data to inform its results.
  • In 1998, people navigated the web using links.

    Pages with lots of links in and out of them were the equivalent of the day of search engines

    and being a small number of links away from one of these key nodes was a big deal.

    It meant you were popular
  • For a whole bunch of reasons, Google no longer needs a proxy for popularity
  • For example, Google is a browser

    - imagine the data collection opportunities if you ran the world’s biggest browser and you wanted to figure out which websites are most popular
  • A lot of research platforms in our space buy data from ISPs - Moz, Similarweb, Hitwise, for example

    It’s expensive and limited.

    But Google IS an ISP.
  • And of course, they’re a dominant search engine - they know what people search for, and what they do next

    And this to name just a few of the ways that pagerank could be rendered redundant as a way of figuring out whether something is popular.
  • And of course, compared to those methods, links are a pretty dirty signal.

    That’s our fault - we, as an industry, have polluted this data
  • Rand’s conclusion that was we should understand what Google is trying to understand via links, and we should optimise for that

    In other words, we should build links that might genuinely drive high quality traffic
  • Today, I want to take this a step further, and ask whether Google needs to use links at all
  • Before we any further: At no point may you Tweet this
  • If you do want to tweet something during this deck, tweet this.

    I’m advocating a little skepticism, not a complete paradigm shift
  • Here are the three questions I aim to answer -
    we actually could have easily asked these 3 questions 5 years ago, and some people were doing
    But today I want to talk through their 2017 answers
  • Right, question 1.

    If Google abandons links as its primary off site ranking factor, what next?
  • This is actually something that, at Distilled, we often ask candidates in interviews. We say, if you had to build a search engine today, with access to Google’s resources, but only with your design, how would it work? And people whose background is in SEO often talk about links. People without an SEO background NEVER say links. The better answers of either kind, talk about machine learning.
  • The cleverest answers are around machine learning, of course
  • This is a slide from Larry Kim’s presentation at SearchLove London. What this shows, is that over time, the higher ranking positions are gaining click through rate. Despite 4 ads, despite answer boxes, and so on. And I think Larry is right when he says that one persuasive explanation of this is that it is a signature of machine learning - of Google procedurally optimising its own results for click through rate. I’ll tell you a story about what this looks like in the real world later
  • Another option that you might not have thought about in this context is brand
  • Quantifying brand is of course something that the marketing industry has struggled with for perhaps a century or more now
  • But this is elementary for Google

    Branded search, click through rates, maybe even signals from gmail, appstore and so on.
  • There’s no reason for Google to stick to one signal, of course
  • In fact, Google has been working on this problem for years

    although this article says Last updated April 2015, it was actually first published in April 2014.

    And basically this concerns the use of popularity, which porn sites do have, as opposed to authority, which they don’t have
  • But the difficult bit is picking all this apart, because of this.

    It’s very difficult to figure out which factor or factors are actually moving the needle, when they’re also so closely related to one another
  • Way back in March last year, Google had come out and said that rankbrain was its third most important ranking factor, and someone asked in this Q&A what 1 & 2 were.
  • And this search quality strategist at Google said, without explicitly giving a hierarchy, that they were content and links.
  • So, pretty unequivocal from Google there. Links are still the big thing.
  • Google has come out routinely over the years with bad SEO advice
  • Some classic examples. And these cases, in my view, tend to be examples of Google getting more complicated than Google itself understands

    Just because links are an input, doesn’t mean they’re interacting with rankings in a way that’s easy to understand (more on this from Will)
  • And, to complicate matters further, they also had this statement about domain authority - i.e., it’s not a thing, which rather confounds our traditional understanding of links as a ranking factor.
  • Nonetheless: Google’s answer to our question is still no. Links are important, Google says.
  • Now this is slightly unfair, because these aren’t exactly analogous - but this is what happens when we compare Moz’s domain authority correlation with those two we just looked at The maximum, by the way, is 1, which the drownings come impressively close to
  • I’m going to talk to you about four potential mechanisms.

    You don’t have to remember what they’re called, I just want you to consider each possibility when you’re looking at, for example, a traffic drop on the same day as a Google update.
  • Some mechanisms to think about before you claim that, for example, your work created an uplift
  • So these two lines are perfectly correlated, because they’re both straight - and this can be a big problem when looking at trends over time, like cheese consumption
  • Or in our case that whenever a site ranks first, it gets a load of links
  • This last point is the badger
  • And that would explain, in one fell swoop, all of these correlation studies
  • So we have to say that, having failed to control for this, all those correlation studies are actually pretty inconclusive
  • I just talked to you about this Moz study, and I actually decided to replicate it, and look at how brand awareness and links compare
  • I talked to you earlier about this Moz study
  • I talked to you earlier about this Moz study
  • I talked to you earlier about this Moz study
  • I talked to you earlier about this Moz study
  • I talked earlier about how quantifying brand awareness is actually pretty difficult
  • I’m using this as a proxy for brand awareness (If you want to know how to pull branded search volume for around 20,000 domains, come talk to me afterwards)
  • This graph shows a data point for every site in my 10,000 pages of search results, with domain authority on one axis and branded search volume on the other It turns out that Domain Authority and Branded Search Volume are, as I alluded to earlier, already pretty closely related, but which is the better predictor of rankings?
  • So the first thing to note is that using Moz’s methodology, branded search volume outperforms their best domain level link-based ranking factor A second and slightly less useful point is that domain authority is far more weakly correlated in my study than theirs - perhaps because I’m only using the top 10
  • Nonetheless: If you would be concerned if I told you that your DA had dropped, then you should be looking at this metric AT LEAST as closely
  • So when I looked at this data using regression analysis, I found that both of these variables were incredibly powerful -
    we’re talking about getting to 99.99999…. And 56 more 9s, % significant
  • You might be familiar with this metric from CRO, where 95% is considered the benchmark That’s 56 9s
  • And that’s 95
  • But when I looked at this for client data rather than the big sample, typically only taking 500-1000 keywords, I noticed this
  • But when I looked at this for client data rather than the big sample, typically only taking 500-1000 keywords, I noticed this
  • And I’m not the only one finding this This graph is from Marcus Tober’s presentation at SearchLove London, and it shows rankings across the bottom vs. the average number of referring domains on the vertical axis - and it turns out that in the health vertical, the correlation is actually the opposite to what you’d expect Now neither Marcus nor I are saying that more links makes your rankings drop, but the point is that this is nuanced and that under certain circumstances links are fairly irrelevant
  • This graph shows baserank over time for two sites - baserank being the ranking if we ignore SERP features like answer boxes So, what do we notice about this graph?
  • Three things
  • Both sites are all over the place on a week by week basis
  • Three things
  • This graph shows baserank over time for two sites - baserank being the ranking if we ignore SERP features like answer boxes So, what do we notice about this graph?
  • Three things
  • Our traditional understanding of what could cause this falls into these three buckets.
  • We don’t have time to get stuck into wayback machine now, but you’ll have to take my word for it: neither of these sites had substantive changes in this period
  • This is Moz’s record of every known algorithm change, which is available publicly on their website if you haven’t seen it. Obviously the algorithm is continuously evolving, but the the only named update during this period is related primarily to local packs.
  • So, these graphs are from Majestic. They show newly discovered links over time.

    Neither site was rapidly gaining links in the run up to that drop, which is the blue area on the left
  • In fact the biggest spikes on FlyingFlowers’ graph are comparable to the smallest weeks on Interflora’s. Incidentally it’s also not a question of link velocity - the above charts for August and September do not represent a significant departure from their historical norms
  • So that leaves us without a sensible explanation for this
  • And it’s not just those two sites by the way - when we add in the other competitors, this whole SERP is all over the place.
  • My suggestions as to what Google is looking for in these iterations is this - aesthetics and price
  • This is the Interflora page, which dropped away, and what I notice on this page is how busy it is, and the prices that are stuck up there.
  • This is Flying Flowers - It’s a much lighter site, and the average price of a product on the homepage is about 30% less.

    That’s the biggest difference between these two sites - the actual content is nearly identical. But it’s all about that first impression.
  • Fleximize is a company that sells small business loans, and worked with Distilled on a content campaign
  • Okay, so this is a graph from STAT of their commercial keywords.
  • You can see that in the middle of this graph, they go from having 4% of their keywords ranking between 4 and 10, to having the same number ranking between 1 and 3, where they’d previously had none
    - so this is still early days for their SEO success -

  • And they also gain a bunch of extra keywords in the 11-20 bracket
  • But if we look at how this happened - first there was a content piece which gained 168 referring domains, and did nothing to their commercial rankings
  • Then another smaller one gained 22 referring domains, still nothing
  • Then a piece gains 191, and suddenly everything moves
  • Will and Rand did a Whiteboard Friday on this way back in 2012
    But Panda was arguably the first big jump in Google looking to understand what users were looking for, so you could do a lot worse than replicating their research with your own site
  • This is one of the types of thing that falls under SEO split testing - changing half your category pages, for example, with some alternative meta descriptions, and playing around with it to see what makes you more traffic I’m sure you’ll hear more about SEO split testing over the next couple of days
  • Doing what you can to avoid that jump back to the search results when they do actually click through
  • And of course, in all of these activities, do them first for mobile then figure out how it applies to desktop, not the other way round
  • None of this is new!

    It’s just more important than ever that you get it right.
  • (Vicke stealing this slide)
  • This looks a lot like what has gone before
  • My deck should be seen as part of a two part series with Vicke’s which you’ll see later today
  • What it all comes down to is this
  • So rather than trying to keep up, aim for what they’re aiming at, and optimize for people
  • What could replace links: I’ve talked about brand and user signals as two prime candidates Has it already happened: Depending on your particular circumstance, quite possibly

    What should you do next: Focus on optimising for the things that Google is trying to optimise for

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