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SearchLove London - Analysing the SERPs for SEO, Content & Customer Insights

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Find out how you can analyse the language of the SERPs to get actionable insights on how you can improve the performance of your SEO campaign.

Published in: Marketing
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SearchLove London - Analysing the SERPs for SEO, Content & Customer Insights

  1. 1. Analysing the SERPS For SEO, Content & Customer Insights
  2. 2. That’s how often Google rewrites the SERP displayed meta description
  3. 3. WHY?
  4. 4. To make SEOs sad?
  5. 5. Just for a laugh?
  6. 6. Nope…
  7. 7. It’s because Google thinks it is smarter than us
  8. 8. Intriguing… Can we use that to our advantage?
  9. 9. Yes, we can! (sorry, that was the last puppy pic)
  10. 10. How? @RoryT11
  11. 11. Deconstruct & analyse the language of the SERPs @RoryT1
  12. 12. Curious? We are in the age of semantic search @RoryT1
  13. 13. Google isn’t ranking a page based on how it uses a keyword On-page Optimisation @RoryT1
  14. 14. • User intent • Query context •Topical relevance • Word relationships Target the keyword, but optimise for this. How does Google provide accurate results? @RoryT1
  15. 15. Understand customer intent to better tailor your messaging @RoryT1
  16. 16. Structure landing pages to help Google understand context @RoryT1
  17. 17. Create more impactful online experiences @RoryT1
  18. 18. Your Toolkit @RoryT11
  19. 19. You need SERP content There are four ways you can get this. @RoryT1
  20. 20. Scrape at scale with Screaming Frog Follow these instructions @RoryT1 Option A
  21. 21. Option B Get SERP content via an API
  22. 22. Option C Get SERP content using the Scraper Chrome extension Get Scraper
  23. 23. Option D Use ‘Thruuu’ – a free SERP analysis tool Use Thruuu By Samuel Schmitt
  24. 24. There are four ways you can get this. You need Jupyter Notebook What is that?
  25. 25. The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. Jupyter.org @RoryT1
  26. 26. Stumped.
  27. 27. Jupyter Notebook is an environment on my laptop where I can learn Python by copying scripts created by people significantly smarter than me and breaking them or making them do something slightly different. RoryTruesdale Python Charlatan @RoryT1
  28. 28. Resources to get started… Jupyter Notebook – Getting Started Guide Robin Lord Find scripts Paul Shapiro JR Oakes Hamlet Batista Find scripts Find scripts
  29. 29. You’ll end up with… @RoryT11
  30. 30. Your SERP content in a CSV@RoryT1
  31. 31. Imported into Jupyter Notebook @RoryT1
  32. 32. You’re (nearly) ready to use Python to analyse the SERPs! @RoryT1
  33. 33. There’s a treat for you.
  34. 34. I’ll share a link to a Dropbox with everything you need to get you started @RoryT11
  35. 35. Before we dive in…
  36. 36. Start by cleaning your SERP content @RoryT1
  37. 37. Avoids duplication & punctuation adds no value Lower case & remove punctuation @RoryT1
  38. 38. Get rid of words like: “Do” “Of” “Am” “If” “But” Remove stop words @RoryT1
  39. 39. Chop up a sentence into individual pieces Tokenization @RoryT1
  40. 40. Convert a word to its root: -‘Playing’ > ‘Play’ -‘Crawling’ > ‘Crawl’ Lemmatization (optional) @RoryT1
  41. 41. @RoryT11
  42. 42. @RoryT11
  43. 43. How many times a combination of words appear in your SERP content? Co-occurrence @RoryT1
  44. 44. Co- occurrence @RoryT1
  45. 45. Co- occurrence Shows the topics competitors cover on landing pages @RoryT1
  46. 46. Co- occurrence What does Google see as semantically relevant? @RoryT1
  47. 47. •Additional keyword research •Topical content gaps •Use semantically relevant phrases on landing pages HOW CAN WE APPLY THIS? @RoryT1
  48. 48. Cost: Range: Time to Charge: Battery Size/Capacity: All Wheel Drive: Towing Capacity: Semi-Conductor SERP XLT: Product Page £ 44,360 MSV 9,620 MSV 7,470 MSV 380 MSV 3,040 MSV 180 MSV
  49. 49. What are the most frequently occurring nouns, verbs & adjectives in a SERP? Part of Speech (PoS) tagging @RoryT1
  50. 50. PoS tagging What ‘things’ are competitors writing about? Nouns (people, place, thing) @RoryT1
  51. 51. PoS tagging How is Google interpreting the context and intent of a search Verbs (action or state) @RoryT1
  52. 52. PoS tagging The language & tone that will resonate with a searcher Adjectives (descriptive word) @RoryT1
  53. 53. PoS tagging Credit Card Example Verbs = Intent Clues
  54. 54. PoS tagging Credit Card Example Nouns = Context Clues @RoryT1
  55. 55. PoS tagging Credit Card Example Adjectives = Context Clues @RoryT1
  56. 56. •Align pages with the motivations of a searcher •What language will resonate with your target audience •Use to improve on page optimisation HOW CAN WE APPLY THIS? @RoryT1
  57. 57. Can we use NLP to uncover topical trends in the SERPs? Topic modelling @RoryT1
  58. 58. Topic modelling Topic modelling is an NLP method that assumes a corpus contains a mixture of topics. It looks at how words and phrases co-occur in a corpus and attempts to group them in coherent themes or topics. @RoryT1
  59. 59. Topic modelling OK, computer. Here are some words. Group them. @RoryT1 RoryTruesdale Cheapening machine learning since 2019
  60. 60. Topic modelling Each bubble represents a topic @RoryT1
  61. 61. Topic modelling The bigger the bubble the more prominent the topic @RoryT1
  62. 62. Topic modelling The further away the bubbles are, the more distinct those topic are
  63. 63. Topic modelling Get a breakdown of the terms our topics consist of @RoryT1
  64. 64. Topic modelling @RoryT1
  65. 65. Topic modelling Use Google’s algorithm to help us identify areas of interest for our audience
  66. 66. •Reference for content ideation •Internal linking and content recommendations •Optimise effectively for semantic relevance HOW CAN WE APPLY THIS? @RoryT1
  67. 67. Can we make our scripts work across other data sources to understand our customers? Other useful applications @RoryT1
  68. 68. Product Reviews @RoryT11
  69. 69. GMB Reviews @RoryT11
  70. 70. Reddit @RoryT11
  71. 71. YouTube Captions @RoryT11
  72. 72. Competitors & Top Ranking Pages @RoryT11
  73. 73. You can scrape it all! @RoryT11
  74. 74. With some minor tweaks we can make our scripts work across a huge range of user- centric content Pretty cool, right? @RoryT1
  75. 75. Visualise sentiment across GMB reviews @RoryT11
  76. 76. Positive: • Simple • Easy to use • Intuitive Negative: • Buggy • Broken exports • Crashes @RoryT1 Identify recurring themes in product reviews
  77. 77. Create networks based on word relationships @RoryT11
  78. 78. What does it all mean? @RoryT11
  79. 79. SERPs give us amazing insight into what customers want @RoryT1
  80. 80. Python makes getting these insights at scale accessible @RoryT1
  81. 81. Use these insights to align landing pages with intent and semantic relevance @RoryT1
  82. 82. Scripts we create allow us to get these insights from lots of other user-centric sources @RoryT1
  83. 83. http://cndr.co/jupyter Python Dropbox Link @RoryT1
  84. 84. Get the slides link @RoryT11 http://cndr.co/searchlove
  85. 85. • https://www.searchenginejournal.com/scrape-google-serp-custom-extractions/267211/ • https://www.searchenginejournal.com/mine-serps-seo-content-customer-insights/311137/ • https://www.seerinteractive.com/blog/user-testing-serps-an-audience-first-approach-to-seo/ • https://www.dropbox.com/sh/vl5miyt6sgbvmkl/AAC5365YcWTun_EzkQLtixe1a?dl=0 (Jupyter Notebook tutorial) • http://www.blindfiveyearold.com/algorithm-analysis-in-the-age-of-embeddings • https://www.searchenginejournal.com/semantic-search-seo/264037/#close • https://www.slideshare.net/DawnFitton/natural-language-processing-and-search-intent- understanding-c3-conductor-2019-dawn-anderson • https://moz.com/blog/what-is-semantic-search • https://www.slideshare.net/paulshapiro/redefining-technical-seo-mozcon-2019-by-paul-shapiro Useful Resources @RoryT1
  86. 86. Thanks For Listening! Conductor.com @RoryT11

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