Taming the Hummingbird - #PMIEUR Berlin

1. Taming the Hummingbird Performance Marketing Insights Berlin, 2014 Jan-Willem Bobbink - @jbobbink #PMIEUR http://bit.ly/pmi-hummingbird

2. WHO SAW IT COMING?

3. ALGORITHM UPDATE

4. “ACCORDING TO GOOGLE, THIS NEW ALGORITHM IMPACTS 90% OF ALL QUERIES” 90% OF ALL SEARCH QUERIES!

5. GOOGLE STARTED USING HUMMINGBIRD ABOUT A MONTH AGO, IT SAID. GOOGLE ONLY ANNOUNCED TODAY. ADJUSTMENTS WERE ALREADY LIVE FOR MULTIPLE WEEKS

6. Performance Marketing Insights Berlin, 2014

7. Director of SEO @ Acronym Media – Blogging at www.notprovided.eu

8. WHAT CHANGED?

9. WHY SO MUCH CONFUSION?

10. Back to the basics

11. CONVERSATIONAL SEARCH EVEN MORE AWESOME!

12. That’s the knowledge graph!

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. WHAT IS THE DIFFERENCE?

15. REWRITING THE QUERIES

16. HOW ABOUT ORGANIC RESULTS? [how old is the president of the United States, Barack Obama] Biography is not in the query!

17. HOW ABOUT ORGANIC RESULTS? [age obama]

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. MONITOR SEO PATENTS

20. FIFA, ARE YOU WATCHING?

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. PREDECESSOR - 2004 PHRASE-BASED VS SINGLE KEYWORDS

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. 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. DISTRIBUTION OF WEB SEARCH QUERIES [Lin et al. 2011]

26. THE HUMMINGBIRD PATENT? REVISING SEARCH QUERIES http://www.google.com/patents/US8538984

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. CAR VERSUS AUTO

29. CAR VERSUS AUTO

30. FULL QUESTION NOT NEEDED

31. Already filed by Google in 2005 DETERMINING QUERY TERM SYNONYMS WITHIN QUERY CONTEXT http://www.google.com/patents/US7636714

32. HOW IS THE KNOWLEDGE GRAPH WORKING?

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. INTERNATIONAL DIFFERENCES CURRENT STATUS

35. GOOGLE.DE (GERMAN)

36. GOOGLE.ES (SPANISH)

37. GOOGLE.NL (DUTCH)

38. KNOWLEDGE GRAPH LOCALISED? German Spanish Dutch

39. GOOGLE.DE (GERMAN)

40. GOOGLE.NL (DUTCH)

41. PRERENDERED QUERIES?

42. HOW CAN YOU DEAL WITH HUMMINGBIRD? AS AN AFFILIATE?

43. HUMMINGBIRD MYTHS

44. GOOGLE KNOWS WHAT QUALITY IS

45. ADD MORE TEXT TO YOUR PAGES

46. LITERALLY ADD MORE QUESTIONS & ANSWERS

47. TEXTS IN THE FORM OF QUESTIONS

48. PUT MORE FOCUS ON LONGTAIL!

49. OK, LETS TURN IT AROUND

50. SRC: Searchmetrics.com 2014 US Ranking factors study

51. DETERMINE TARGET AUDIENCE

52. DEFINE INTENTION PER PERSONA

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. ORGANISE YOUR PAGES TOPICALLY

55. ENTITY BASED KEYWORD RESEARCH

56. GENERATE SYNONYM LISTS

57. http://www.performancemarketinginsights.com/14/europe/agenda/2/ SRC: http://www.alchemyapi.com/

58. Use Google’s Freebase API https://developers.google.com/freebase/

59. RELATE CONTENT TO ENTITY SRC: http://www.blindfiveyearold.com/knowledge-graph-optimization

60. sameAS EXAMPLE

61. GET LINKS FROM SEMANTICALLY RELEVANT SOURCES http://semantic-link.com/#/berlin

62. FILL FREEBASE WITH RELEVANT INFORMATION

63. Google may present better SERPs also in terms of better ads FROM ADVERTISING PERSPECTIVE

64. How to get in their?

65. HOW DOES GOOGLE DECIDE ON SOURCE? SRC: Bill Slawski http://www.seobythesea.com/2013/05/google-knowledge-graph-results/

66. HOW TO DEAL WITH KNOWLEDGE GRAPH?

67. NO PATTERN FOUND YET

68. GOOGLE GETTING YOUR TRAFFIC?

69. https://class.coursera.org/nlangp-001 https://www.coursera.org/course/nlp

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. WANT TO KNOW MORE ABOUT ENTITY RETRIEVAL AND LINKING? http://ejmeij.github.io/entity-linking-and-retrieval-tutorial/

72. Questions? Don’t hesitate to ask! Or find me at the bar  http://bit.ly/pmi-hummingbird & @jbobbink

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

Taming the Hummingbird - #PMIEUR Berlin

Jan-Willem Bobbink - Freelance SEO Consultant

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