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Rand Fishkin: Two Algorithm World

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Rand Fishkin of Moz speaks at State of Search 2015 in Dallas, Texas on how we're now living in a two algorithm world.

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Rand Fishkin: Two Algorithm World

  1. 1. Rand Fishkin, Wizard of Moz | @randfish | rand@moz.com SEO in a Two Algorithm World
  2. 2. bit.ly/twoalgo Get the presentation:
  3. 3. State of Search November 16th, 2015 8:00am Dallas, TX
  4. 4. Remember When…
  5. 5. We Had One Job
  6. 6. Perfectly Optimized Pages
  7. 7. The Search Quality Teams Determined What to Include in the Ranking System
  8. 8. They decided links > content
  9. 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. 10. Even in 2012, It Felt Like Google Was Making Liars Out of the White Hat SEO World Via Wil Reynolds
  11. 11. Google’s Last 3 Years of Advancements Erased a Decade of Old School SEO Practices
  12. 12. They Finally Launched EffectiveAlgorithms to Fight Manipulative Links & Content Via Google
  13. 13. And They Leveraged Fear + Uncertainty of Penalization to Keep Sites Inline Via Moz Q+A
  14. 14. Google Figured Out Intent Rand probably doesn’t just want webpages filled with the word “beef”
  15. 15. They Looked at Language, not Just Keywords Oh… I totally know this one!
  16. 16. They Predicted When We Want Diverse Results He probably doesn’t just want a bunch of lists.
  17. 17. They Figured Out When We Wanted Freshness Old pages on this topic probably aren’t relevant anymore
  18. 18. Their Segmentation of Navigational from Informational Queries Closed Many Loopholes
  19. 19. Google Learned to ID Entities of Knowledge
  20. 20. And to Connect Entities to Topics & Keywords Via Moz
  21. 21. Brands Became a Form of Entities
  22. 22. TheseAdvancements Brought Google (mostly) Back in Line w/ Its Public Statements Via Google
  23. 23. During These Advances, Google’s Search Quality Team Underwent a Revolution
  24. 24. Early On, Google Rejected Machine Learning in the Organic RankingAlgo Via Datawocky, 2008
  25. 25. Amit Singhal Shared Norvig’s ConcernsAbout ML Via Quora
  26. 26. In 2012, Google Published a PaperAbout How they Use ML to Predict Ad CTRs: Via Google
  27. 27. 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.”
  28. 28. By 2013, It Was Something Google’s Search Folks Talked About Publicly Via SELand
  29. 29. As MLTakes Over More of Google’sAlgo, the Underpinnings of the Rankings Change Via Colossal
  30. 30. 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
  31. 31. Google is PublicAbout How They Use MLin Image Recognition & Classification Via Jeff Dean’s Slides on Deep Learning; a Must Read for SEOs
  32. 32. 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
  33. 33. 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.
  34. 34. 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.
  35. 35. 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
  36. 36. 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…
  37. 37. We’re TalkingAbout Algorithms that Build Algorithms (without human intervention)
  38. 38. 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
  39. 39. No wonder these guys are stressed about Google unleashing the Terminators  Via CNET & Washington Post
  40. 40. What Does Deep Learning Mean for SEO?
  41. 41. 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.
  42. 42. 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
  43. 43. 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.
  44. 44. We’ll Be Optimizing Less for Ranking Inputs Unique Linking Domains Keywords in Title Anchor Text Content Uniqueness Page Load Speed
  45. 45. 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
  46. 46. OK… Maybe in the future. But, do those kinds of metrics really affect SEO today?
  47. 47. Remember Our Queries & Clicks Test from 2014? Via Rand’s Blog
  48. 48. Since then, it’s been much harder to move the needle with raw queries and clicks…
  49. 49. Case closed! Google says they don’t use clicks in the rankings. Via Linkarati’s Coverage of SMX Advanced
  50. 50. But, what if we tried long clicks vs. short clicks? Note SeriousEats, ranking #4 here
  51. 51. 11:39am on June 21st, I sent this tweet:
  52. 52. 40 Minutes & ~400 Interactions Later Moved up 2 positions after 2+ weeks of the top 5 staying static.
  53. 53. 70 Minutes & ~500 Interactions Total Moved up to #1.
  54. 54. Stayed ~12 hours, when it fell to #13+ for ~8 hours, then back to #4. Google? You messing with us?
  55. 55. Via Google Trends, we can see the relative impact of the test on query volume ~5-10X normal volume over 3-4 hours
  56. 56. 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.
  57. 57. OK… Maybe in the future. But, do those kinds of metrics really affect SEO today?
  58. 58. Via Bloomberg Business
  59. 59. The Future: Optimizing for Two Algorithms
  60. 60. The Best SEOs HaveAlways Optimized to Where Google’s Going
  61. 61. Today, I Think We Know, Better Than Ever, Where That Is Welcome to your new home, the User/Usage Signals + ML Model Cabin
  62. 62. We Must Choose How to Balance Our Work…
  63. 63. Hammering on the Fading Signals of Old…
  64. 64. Or Embracing Those We Can See On the Rise
  65. 65. 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
  66. 66. 5 New(ish) Elements of Modern SEO
  67. 67. Punching Above Your Ranking’s Average CTR#1
  68. 68. 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
  69. 69. 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?
  70. 70. 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.
  71. 71. Driving Up CTR Through Branding Or Branded Searches May GiveAn Extra Boost
  72. 72. #1 Ad Spender #2 Ad Spender #4 Ad Spender #3 Ad Spender #5 Ad Spender
  73. 73. 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.
  74. 74. Beating Out Your Fellow SERP Residents on Engagement#2
  75. 75. Together, Pogo-Sticking & Long Clicks Might Determine a Lot of Where You Rank (and for how long) Via Bill Slawski on Moz
  76. 76. What Influences Them?
  77. 77. 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:
  78. 78. Via NY Times e.g. this interactive graph that asks visitors to draw their best guess likely gets remarkable engagement
  79. 79. 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
  80. 80. e.g. Nomadlist’s superb, filterable database of cities and community for remote workers. Via Nomadlist
  81. 81. Filling Gaps in Your Visitors’ Knowledge#3
  82. 82. 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.
  83. 83. ML models may note that the presence of certain words, phrases, & topics predict more successful searches
  84. 84. e.g. a page about New York that doesn’t mention Brooklyn or Long Island may not be very comprehensive
  85. 85. 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.
  86. 86. 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
  87. 87. In the meantime, check out AlchemyAPI Or MonkeyLearn
  88. 88. Fulfilling the Searcher’s Task (not just their query)#4
  89. 89. 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
  90. 90. Broad search All the sites (or answers) you probably would have visited/sought along that path Completion of Task This is Their Ultimate Goal:
  91. 91. If Google sees that many people who perform these types of queries:
  92. 92. Eventually end their queries on the topic after visiting Ramen Rater… The Ramen Rater
  93. 93. They might use the clickstream data to help rank that site higher, even if it doesn’t have traditional ranking signals
  94. 94. They’re definitely getting and storing it.
  95. 95. 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
  96. 96. 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
  97. 97. Earning More Shares, Links, & Loyalty per Visit#5
  98. 98. Pages that get lots of social activity & engagement, but few links, seem to overperform…
  99. 99. Google says they don’t use social signals directly, but examples like these make SEOs suspicious
  100. 100. Even for insanely competitive keywords, we see this type of behavior when a URLgets authentically “hot” in the social world.
  101. 101. Data from Buzzsumo & Moz show that very few articles earn sharesAND that links & shares have almost no correlation. Via Buzzsumo &
  102. 102. 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
  103. 103. 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)
  104. 104. But I Don’t Care if It’s Correlation or Causation; I Want to Rank Like These Guys!
  105. 105. 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
  106. 106. 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.
  107. 107. Knowing What Makes them Return (or prevents them from doing so) Is, Too.
  108. 108. 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?”
  109. 109. 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.
  110. 110. 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.
  111. 111. We Have to Go From This: Wikipedia on Vince Carter (currently ranking #10 for “Vince Carter Dunks”)
  112. 112. ToThis: Via ESPN
  113. 113. 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”
  114. 114. Welcome to the Two-Algorithm World of 2015
  115. 115. Algo 1: Google
  116. 116. Algo 2: Subset of Humanity that Interacts With Your Content
  117. 117. “Make Pages for People, Not Engines.”
  118. 118. Terrible Advice.
  119. 119. 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
  120. 120. Optimize for Both: Algo Input & Human Output
  121. 121. Rand Fishkin, Wizard of Moz | @randfish | rand@moz.com bit.ly/twoalgo

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