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Scary SERPs (and keyword creep) #brightonSEO

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My talk from brightonSEO on two big trends around keywords and a couple of practical ways to respond to them.

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Scary SERPs (and keyword creep) #brightonSEO

  1. 1. Kelvin Newman Founder - BrightonSEO Scary SERPs (and keyword creep) @kelvinnewman http://www.slideshare.net/kelvinnewman
  2. 2. @kelvinnewman So, who is this guy? @kelvinnewman
  3. 3. Founder of BrightonSEO @kelvinnewman kelvin@brightonseo.com http://www.slideshare.net/kelvinnewman Kelvin Newman
  4. 4. @kelvinnewman Shhhhh, but I don’t live in Brighton @kelvinnewman
  5. 5. @kelvinnewman Actually; I live in Worthing
  6. 6. @kelvinnewman Which is famous for one thing… @kelvinnewman Birdman Competition where people see how far they can jump of the pier
  7. 7. @kelvinnewman So what is this presentation about? @kelvinnewman
  8. 8. @kelvinnewman Well, this presentation isn’t about… @kelvinnewman
  9. 9. @kelvinnewman So, if you use the Google Keyword Planner you can find some excellent keywords “ “
  10. 10. @kelvinnewman It’s more about getting you to think differently about the future of keywords.
  11. 11. @kelvinnewman One True Answer Part 1
  12. 12. @kelvinnewman Start with a story
  13. 13. @kelvinnewman Peter A. Shulman Historian of sci, tech, and American politics. Author of Coal & Empire. Associate Professor of History 
  14. 14. Lecturing on the reemergence of the Ku Klux Klan in the 1920s when a student asked an odd question: @kelvinnewman
  15. 15. Was President Warren Harding a member of the KKK? @kelvinnewman
  16. 16. @kelvinnewman He wasn’t
  17. 17. @kelvinnewman
  18. 18. @kelvinnewman
  19. 19. @kelvinnewman This isn’t an isolated occurrence
  20. 20. https://theoutline.com/post/1192/google-s-featured-snippets-are-worse-than-fake-news @kelvinnewman
  21. 21. https://theoutline.com/post/1192/google-s-featured-snippets-are-worse-than-fake-news @kelvinnewman
  22. 22. http://searchengineland.com/googles-one-true-answer-problem-featured-snippets-270549 @kelvinnewman
  23. 23. @kelvinnewman Bad on desktop, terrible on voice
  24. 24. https://theoutline.com/post/1192/google-s-featured-snippets-are-worse-than-fake-news
  25. 25. @kelvinnewman Easy to mock but what can we learn from this
  26. 26. Preference for “one true answer” http://searchengineland.com/googles-one-true-answer-problem-featured-snippets-270549 @kelvinnewman
  27. 27. Risk for Google but opportunity for us @kelvinnewman
  28. 28. @kelvinnewman End of Keyword Precision Part 2
  29. 29. @kelvinnewman Not-Bloody-Provided
  30. 30. @kelvinnewman Keyword Bloody Planner Estimates
  31. 31. @kelvinnewman Close-Bloody-Variants
  32. 32. @kelvinnewman Query Re-writing
  33. 33. @kelvinnewman It would seem Machine Learning is involved in Query Re-writing @kelvinnewman
  34. 34. @kelvinnewman
  35. 35. @kelvinnewman
  36. 36. @kelvinnewman Machine learning is sexy And Artificial Intelligence, Deep Learning and other sort of related things along those lines. @kelvinnewman
  37. 37. @kelvinnewman I studied media studies at Uni not computer science or anything like it… Now feels like a good time to share @kelvinnewman
  38. 38. @kelvinnewman Machine Learning Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task.
  39. 39. @kelvinnewman Deep Learning A branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and non-linear transformations.
  40. 40. Seeing the connections below the surface @kelvinnewman
  41. 41. @kelvinnewman
  42. 42. @kelvinnewman So what are the actions? @kelvinnewman
  43. 43. @kelvinnewman Use some off the shelf options @kelvinnewman
  44. 44. https://algorithmia.com/ algorithms/nlp/Word2Vec @kelvinnewman
  45. 45. @kelvinnewman Tutorials on use Machine Learning on over 1M hotel reviews finds interesting insights https://blog.monkeylearn.com/machine-learning-1m-hotel-reviews-finds-interesting-insights
  46. 46. Or wait for SEO tool suites to start doing this properly @kelvinnewman
  47. 47. Easiest solution is to know the space and know your customers @kelvinnewman
  48. 48. But just writing for users is a lazy suggestion. We can better understand our users needs if we can better understand how other websites are writing about a topic
  49. 49. @kelvinnewman To optimise a page now you need more than add keyphrases.
  50. 50. @kelvinnewman You need to have all the phrases and words they’d expect
  51. 51. Is the search query on the page and does deserve to rank? Old Model @kelvinnewman
  52. 52. Does it contain the search query and phrases used be other pages that rank for the term and does deserve to rank? New Model @kelvinnewman
  53. 53. @kelvinnewman Two hacky & clunky way of seeing those you’d need include. Method 1
  54. 54. @kelvinnewman Take the top ten results for your query and extract the text using something like textise.net Method 1
  55. 55. @kelvinnewman Bung the copy from all the pages into a Word Cloud Tool I like jasondavies.com/ wordcloud/
  56. 56. @kelvinnewman Treat the most common words like bingo
  57. 57. @kelvinnewman Take the top 3 results for your query and extract the text using something like textise.net Method 2
  58. 58. @kelvinnewman Create a list of all the single words used on the page using something like writewords.org.uk/ word_count.asp
  59. 59. @kelvinnewman Create a Venn Diagram of the overlap Method 2
  60. 60. @kelvinnewman
  61. 61. @kelvinnewman kelvin@brightonseo.com http://www.slideshare.net/kelvinnewman Thanks

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