Taming the Hummingbird - #PMIEUR Berlin

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Jan, who hails from a background in physics, has been able to successfully prove a number of his own personal theories about the inner workings of Google's Hummingbird algorithm and how its has evolved in the months since its launch.

During these 45 minutes you will be informed how you can build a better website in the eyes of the search engine's most recent major update. Patents, publications and announcements over the past five years can all be used to your advantage.

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Taming the Hummingbird - #PMIEUR Berlin

  1. 1. Taming the Hummingbird Performance Marketing Insights Berlin, 2014 Jan-Willem Bobbink - @jbobbink #PMIEUR http://bit.ly/pmi-hummingbird
  2. 2. WHO SAW IT COMING?
  3. 3. ALGORITHM UPDATE
  4. 4. “ACCORDING TO GOOGLE, THIS NEW ALGORITHM IMPACTS 90% OF ALL QUERIES” 90% OF ALL SEARCH QUERIES!
  5. 5. GOOGLE STARTED USING HUMMINGBIRD ABOUT A MONTH AGO, IT SAID. GOOGLE ONLY ANNOUNCED TODAY. ADJUSTMENTS WERE ALREADY LIVE FOR MULTIPLE WEEKS
  6. 6. Performance Marketing Insights Berlin, 2014
  7. 7. Director of SEO @ Acronym Media – Blogging at www.notprovided.eu
  8. 8. WHAT CHANGED?
  9. 9. WHY SO MUCH CONFUSION?
  10. 10. Back to the basics
  11. 11. CONVERSATIONAL SEARCH EVEN MORE AWESOME!
  12. 12. That’s the knowledge graph!
  13. 13. "Hummingbird is focused more on ranking information based on a more intelligent understanding of search requests, unlike its predecessor, Caffeine, which was targeted at better indexing of websites." GOOGLE: “TRY TO UNDERSTAND” RELATIONS
  14. 14. WHAT IS THE DIFFERENCE?
  15. 15. REWRITING THE QUERIES
  16. 16. HOW ABOUT ORGANIC RESULTS? [how old is the president of the United States, Barack Obama] Biography is not in the query!
  17. 17. HOW ABOUT ORGANIC RESULTS? [age obama]
  18. 18. Keyword based!? [age obama] RESULTING IN MORE RELEVANT SERPS Semantically, related phrases will be those that are commonly used to discuss or describe a given topic or concept, such as “President of the United States” and “White House.”
  19. 19. MONITOR SEO PATENTS
  20. 20. FIFA, ARE YOU WATCHING?
  21. 21. SEO PATENTS, NOT ONLY FOR THE NERDS! - Learn from the past - Predict future changes - Get an idea about the inner working of search engines - Get an idea what certain features take to exist WHAT CAN YOU LEARN FROM PATENTS?
  22. 22. PREDECESSOR - 2004 PHRASE-BASED VS SINGLE KEYWORDS
  23. 23. KEYWORD BASED SCORING “A document is retrieved in response to a query containing a number of query terms, typically based on having some number of query terms present in the document.”
  24. 24. Google research has shown that on more difficult queries, people start to type their searches as natural language questions. They also searched longer queries on average. This study also stated that, at the time of the study (2010), most of the time the question queries failed to give users the information they were looking for and they would revert back to keyword queries. WHY DEVELOP HUMMINGBIRD?
  25. 25. DISTRIBUTION OF WEB SEARCH QUERIES [Lin et al. 2011]
  26. 26. THE HUMMINGBIRD PATENT? REVISING SEARCH QUERIES http://www.google.com/patents/US8538984
  27. 27. IT’S ALL ABOUT CONCEPTS “The goal is that pages matching the meaning do better, rather than pages matching just a few words.”
  28. 28. CAR VERSUS AUTO
  29. 29. CAR VERSUS AUTO
  30. 30. FULL QUESTION NOT NEEDED
  31. 31. Already filed by Google in 2005 DETERMINING QUERY TERM SYNONYMS WITHIN QUERY CONTEXT http://www.google.com/patents/US7636714
  32. 32. HOW IS THE KNOWLEDGE GRAPH WORKING?
  33. 33. HOW ABOUT SCALE? -YAGO: 10 million entities and 120 million facts -Freebase: 37 million topics, 1,998 types, and more than 30,000 properties - DBpedia: 3.77 million things, 2.35 million classified in Ontology, including: - 764,000 persons, 573,000 places, - 333,000 creative works, 192,000 organizations, - 202,000 species and 5,500 diseases. -111 languages, together 20.8 million things Source: WSDM’14 conference, http://ejmeij.github.io/entity-linking-and-retrieval-tutorial/
  34. 34. INTERNATIONAL DIFFERENCES CURRENT STATUS
  35. 35. GOOGLE.DE (GERMAN)
  36. 36. GOOGLE.ES (SPANISH)
  37. 37. GOOGLE.NL (DUTCH)
  38. 38. KNOWLEDGE GRAPH LOCALISED? German Spanish Dutch
  39. 39. GOOGLE.DE (GERMAN)
  40. 40. GOOGLE.NL (DUTCH)
  41. 41. PRERENDERED QUERIES?
  42. 42. HOW CAN YOU DEAL WITH HUMMINGBIRD? AS AN AFFILIATE?
  43. 43. HUMMINGBIRD MYTHS
  44. 44. GOOGLE KNOWS WHAT QUALITY IS
  45. 45. ADD MORE TEXT TO YOUR PAGES
  46. 46. LITERALLY ADD MORE QUESTIONS & ANSWERS
  47. 47. TEXTS IN THE FORM OF QUESTIONS
  48. 48. PUT MORE FOCUS ON LONGTAIL!
  49. 49. OK, LETS TURN IT AROUND
  50. 50. SRC: Searchmetrics.com 2014 US Ranking factors study
  51. 51. DETERMINE TARGET AUDIENCE
  52. 52. DEFINE INTENTION PER PERSONA
  53. 53. BUILD ENTITY SPECIFIC PAGES Using natural and semantically rich language: Use entities in copy: 5 facts you have to know about Jan-Willem Bobbink being in Berlin at Performance marketing Insights Entity attributes: 1987, Utrecht, SEO, Acronym, Physics etc. So start with attribute stuffing instead of keyword stuffing
  54. 54. ORGANISE YOUR PAGES TOPICALLY
  55. 55. ENTITY BASED KEYWORD RESEARCH
  56. 56. GENERATE SYNONYM LISTS
  57. 57. http://www.performancemarketinginsights.com/14/europe/agenda/2/ SRC: http://www.alchemyapi.com/
  58. 58. Use Google’s Freebase API https://developers.google.com/freebase/
  59. 59. RELATE CONTENT TO ENTITY SRC: http://www.blindfiveyearold.com/knowledge-graph-optimization
  60. 60. sameAS EXAMPLE
  61. 61. GET LINKS FROM SEMANTICALLY RELEVANT SOURCES http://semantic-link.com/#/berlin
  62. 62. FILL FREEBASE WITH RELEVANT INFORMATION
  63. 63. Google may present better SERPs also in terms of better ads FROM ADVERTISING PERSPECTIVE
  64. 64. How to get in their?
  65. 65. HOW DOES GOOGLE DECIDE ON SOURCE? SRC: Bill Slawski http://www.seobythesea.com/2013/05/google-knowledge-graph-results/
  66. 66. HOW TO DEAL WITH KNOWLEDGE GRAPH?
  67. 67. NO PATTERN FOUND YET
  68. 68. GOOGLE GETTING YOUR TRAFFIC?
  69. 69. https://class.coursera.org/nlangp-001 https://www.coursera.org/course/nlp
  70. 70. PDF: HTTP://NLP.STANFORD.EDU/IR- BOOK/PDF/IRBOOKPRINT.PDF If you are interested in Natural language processing, read it: AN INTRODUCTION TO INFORMATION RETRIEVAL
  71. 71. WANT TO KNOW MORE ABOUT ENTITY RETRIEVAL AND LINKING? http://ejmeij.github.io/entity-linking-and-retrieval-tutorial/
  72. 72. Questions? Don’t hesitate to ask! Or find me at the bar  http://bit.ly/pmi-hummingbird & @jbobbink
  73. 73. Image Credits Thanks for the images! http://community.qlik.com/blogs/theqlikviewblog/2013/02/21/visualizations-the-tip-of-the-iceberg-of- understanding http://asset5.instanthumour.com/wp-content/uploads/2013/11/is-google-boy-or-girl-2.jpg https://encrypted- tbn2.gstatic.com/images?q=tbn:ANd9GcQovn4u6HtBbDB6uceJQLk7WyBNvRFKQLt2lYxF3gy94HRzosYG Images: http://img3.wikia.nocookie.net/__cb20130815124007/transformers-legends/images/c/c4/Triple- facepalm.jpg http://www.verticalresponse.com/blog/how-to-find-your-target-market/ http://footage.shutterstock.com/clip-2399522-stock-footage-reading-a-book-or-bible.html http://www.searchengineguide.com/matt-bailey/keyword-strategies-the-long-tail.php Europe: http://www.ezilon.com/maps/images/Europe-physical-map.gif Devices: http://www.google.com/insidesearch/features/search/assets/img/devices-preview.png Postcard: https://plus.google.com/u/0/+AmitSinghal/posts/AtndBA1pzNg Entity: http://www.entitythemovie.com/gallery Cutts meme: http://www.jacobking.com/affiliate-link-cloaking

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