Hummingbird unleashed. Understanding the new Google Search Algorithm

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How does Hummingbird work? We cannot tell it and very few has been explicitly told about it by Amit Singhal and others Google spokespersons.
But we can reasonably try to figure out the basis of its functioning and, therefore, understand how SEO is definitively changed.

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Hummingbird unleashed. Understanding the new Google Search Algorithm

  1. 1. Hummingbird Unleashed Sugges&ng  a  new  SEO  methodology   Gianluca Fiorelli - @gfiorelli1 Global  Associate  
  2. 2. Hummingbird is a complete rewrite of our search system
  3. 3. It’s not me saying it, it’s this guy
  4. 4. Before talking about
  5. 5. we should talk about
  6. 6. and
  7. 7. Caffeine   Panda   Penguin   Because a logic in the Google Updates sequence exists
  8. 8. The mobile revolution forced us to change how we think
  9. 9. Mobile may overtake desktop for Google searches within a year (SMXW 2014) It’s not me saying it, it’s this guy
  10. 10. Do you remember when Google what showing us “searches similar to…” in the SERPs?
  11. 11. Usually it was for queries like: “The best way of cooking a pizza with antichokes in a electric oven”
  12. 12. (Long) Long Tails Verbose Queries
  13. 13. (Long) Long Tails Verbose Queries
  14. 14. Hummingbird =/ Humminguin
  15. 15. How Hummingbird (possibly) works
  16. 16. To take synonyms and Knowledge Graph and other things
  17. 17. It’s not me saying it, it’s (again) this guy
  18. 18. SYNONYMS
  19. 19. Google is dealing with them since a long time
  20. 20. Read  this  post  by  Vanessa  Fox  on  Search  Engine  Land:  hDp://itseo.org/OEJCDG     Keyphrases don’t need to be in their original form. We do a lot of synonym work, so we can find good pages that don’t happen to use the same words a the user typed (Matt Cutts)
  21. 21. The issue is that a word can be a synonym or not depending on the Context Coche Automóvil Carro
  22. 22. That Contest was the problem was clear since the beginning, as we can understand reading this patent by Amit Singhal: http://itseo.org/1gPt32l The issue is that a word can be a synonym or not depending on the Context
  23. 23. Hummingbird is how Google solves the Contest issue, thanks to Search Entities and Semantics Search Entities: http://itseo.org/1fwbfoL
  24. 24. Search Entities • A query a searcher submits; • The documents responsive to the query; • The search session during which the searcher submits the query; • The time at which the query is submitted; • Advertisements presented in response to the query; • Anchor text in a link in a document; • The domain associated with a document. Suggested read: this post by Bill Slawski
  25. 25. Words =/ Things
  26. 26. Words = Verbal Representation of Things
  27. 27. Google transforms Words into Concepts thanks to Search Entities
  28. 28. And Concepts can be disambiguated over the base of their Context
  29. 29. This is what Google already started to do with Knowledge Graph
  30. 30. BEWARE! SEMANTIC SEO =/ SCHEMA.ORG Schema.org is instrumental to Semantic, it’s not SEMANTIC
  31. 31. OTHERS THINGS… I suspect that the third factor are co- occurrences
  32. 32. Consequences
  33. 33. Google understands better the queries and their intentions
  34. 34. Google may expand the number of documents that respond significantly to a query
  35. 35. If verbose query A = simplier query C and If verbose query B = simplier query C and similar to verbose query A then I’ll show just the SERPs answering to query C
  36. 36. Tl;dr: Simpyfing the queries, Google also reduces the number of unnecessary SERPs
  37. 37. And that’s good also in term of Adwords…
  38. 38. What about links? Won’t they be an essential factor anymore?
  39. 39. Links are clearly an important signal about the importance of your content. They’re still very valuable
  40. 40. It’s not me saying it, it’s (again!) this guy
  41. 41. From theory to practice. For a new model of SEO
  42. 42. Mario has a small pizzerias’ chain in NYC
  43. 43. He has only a very small difficulty
  44. 44. The old way (not considering Local Search) 1) Pizzeria Tribeca; 2) Best pizzeria in NoLiTa; 3) Calzone Theater District; 4) Pizza Special Chelsea; 5) Where to eat the best pizza in Manhattan 6) etc etc
  45. 45. #FAIL (not provided)
  46. 46. #FAIL Penguin (monster)
  47. 47. #FAIL Bounce Rate (tons of it)
  48. 48. #FAIL Hummingbird (killing the long tail)
  49. 49. Il Nuovo Metodo We identify the Entities related to our niche and how they are connected We match them with our Audience interests We creat Content Architecture based on Content Hub using Ontology We conducts a Keywords Research and Mapping with Entities in mind
  50. 50. Entities identification Freebase APIs: http://itseo.org/1h3RgOS
  51. 51. Entities identification Yahoo Glimmer: http://glimmer.research.yahoo.com/
  52. 52. Entities identification Bottlenose: http://bottlenose.com/
  53. 53. Entities identification RelFinder: http://www.visualdataweb.org/relfinder.php
  54. 54. Audience Matching Read this: http://itseo.org/1gYLi5q
  55. 55. Audience Matching (audience personas) Followerwonk first
  56. 56. Audience Matching (audience personas) And after we use Tribalytics: http://tribalytics.com/
  57. 57. Ontology & Taxonomy Read twice this deck by Abby Covert: http://itseo.org/1oHVWjt
  58. 58. Ontology & Taxonomy (based on Entities Search and Audience Matching Pizza Thin Thick Regular crust Organic Rossa Bianca Napoletana Romana
  59. 59. Content Hub Creation Home Tribeca Our Stories From the oven People of Tribeca Why we love Tribeca SOCIAL
  60. 60. From Content Strategy to Content Marketing Recipes > Schema.org > Rich Snippets Recipes > Video Marketing > VideoObject > Rich Snippets Infografics – Data Visualization – Charts (passive link building opportunities) Guides > Long Form > Authorship > In-Depth Articles UGC Q&A
  61. 61. Protip – Newsjacking & Unconventional Marketing as a Plus
  62. 62. Keyword Research based on Entities Much more ideas in this post by Dan Shure: http://itseo.org/1h479od
  63. 63. And if someone tells you that SEO is dead…
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