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BrightonSEO 2019 - Mining the SERP for SEO, Content & Customer Insights

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Find out how you can use Python to analyse the language of the SERPs for valuable insights on what your customers want and how this can be applied to improve the performance of your SEO campaign.

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BrightonSEO 2019 - Mining the SERP for SEO, Content & Customer Insights

  1. 1. Mining the SERPs for SEO, Content & Customer Insights Rory Truesdale // Conductor http://cndr.co/brighton @RoryT11
  2. 2. About Me Rory Truesdale •SEO Strategist at Conductor •EMEA SEO lead for WeWork Get In Touch brightonseo@conductor.com @RoryT11 @RoryT11
  3. 3. Get The Slides @RoryT11 http://cndr.co/brighton
  4. 4. •SERPs are a great resource to learn what Google ‘thinks’ our customers want •Workflows that will help you understand the intent of the people you want to reach •How to use these insights to improve the quality of your on-page optimisation What To Expect @RoryT1
  5. 5. That’s how often Google rewrites the SERP displayed meta description
  6. 6. WHY?
  7. 7. To make SEOs sad?
  8. 8. Just for a laugh?
  9. 9. Nope…
  10. 10. It’s because Google thinks it is smarter than us
  11. 11. Intriguing… Can we use that to our advantage?
  12. 12. Yes, we can! (sorry, that was the last puppy pic)
  13. 13. How? @RoryT11
  14. 14. We can deconstruct & analyse the language in SERP displayed content to learn what Google thinks our customers are interested in @RoryT1
  15. 15. Curious? This is important because we are in the age of semantic search @RoryT1
  16. 16. Google isn’t ranking a page based on how it uses a keyword. Google provides accurate results based on intent, query context & word relationships. On-page Optimisation @RoryT1
  17. 17. • User intent • Query context •Topical relevance • Word relationships Target the keyword, but optimise for this. On-page Optimisation @RoryT1
  18. 18. Understand customer intent & desire to better tailor your messaging @RoryT1
  19. 19. Structure landing pages to help Google understand context & how it meets the needs of the searcher @RoryT1
  20. 20. Build more meaningful online experiences that better convert website visitors @RoryT1
  21. 21. Your Toolkit @RoryT11
  22. 22. You need SERP content There are three ways you can get this. @RoryT1
  23. 23. Scrape at scale with Screaming Frog Follow these instructions @RoryT1 Option A
  24. 24. Option B Get SERP content via an API
  25. 25. Option C Get SERP content using the Scraper Chrome extension Get Scraper
  26. 26. There are four ways you can get this. You need Jupyter Notebook What is that?
  27. 27. 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
  28. 28. Stumped? Me too… Here’s my definition
  29. 29. 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
  30. 30. Resources to get started… Jupyter Notebook – Getting Started Guide Robin Lord Find scripts Paul Shapiro JR Oakes Hamlet Batista Find scripts Find scripts
  31. 31. You’ll end up with… @RoryT11
  32. 32. Your SERP content in a CSV@RoryT1
  33. 33. Imported into Jupyter Notebook @RoryT1
  34. 34. You’re ready to use Python to analyse the SERPs! @RoryT1
  35. 35. There’s a treat for you.
  36. 36. I’ll share a link to a Dropbox with everything you need to get you started @RoryT11
  37. 37. Before we dive in…
  38. 38. Start by cleaning your SERP content @RoryT1
  39. 39. Lower case avoids duplication & punctuation adds no value to this analysis Lower Case & Remove Punctuation @RoryT1
  40. 40. Stop words are commonly occurring words that don’t improve our analysis Remove Stop Words @RoryT1
  41. 41. The process of chopping up a sentence into individual pieces, called ‘tokens’ Tokenization @RoryT1
  42. 42. The process of converting a word to its root (i.e. “playing” becomes “play”) Lemmatization (optional) @RoryT1
  43. 43. @RoryT11
  44. 44. @RoryT11
  45. 45. How many times does a word or combination of words appear in your SERP content? Co-occurrence @RoryT1
  46. 46. Co- occurrence Snapshot of phrases frequently occurring in the SERPs @RoryT1
  47. 47. Co- occurrence Demonstrates the topics competitors cover on landing pages @RoryT1
  48. 48. Co- occurrence Understand the types of phrases that Google sees as semantically relevant to a target keyword set
  49. 49. •Additional source of data for keyword research •Identify topical content gaps on landing pages •Optimise landing page content by incorporating semantically relevant phrases HOW CAN WE APPLY THIS? @RoryT1
  50. 50. 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
  51. 51. What are the most frequently occurring nouns, verbs & adjectives in a SERP? Part of Speech Tagging @RoryT1
  52. 52. PoS Tagging Uncover the phrases or topics you should include in your landing pages to rank for a term Nouns (people, place, thing) @RoryT1
  53. 53. PoS Tagging Get clues around how Google is interpreting the context and intent of a search Verbs (action or state) @RoryT1
  54. 54. PoS Tagging Understand the language and tone that might resonate with a searcher Adjectives (descriptive word) @RoryT1
  55. 55. PoS Tagging Credit Card Example – P1 Verbs Intent Clues: What is the specific motivation our searcher has?
  56. 56. PoS Tagging Credit Card Example - P1 Nouns Context Clues: Words that clarify meaning & help us understand what a searcher wants @RoryT1
  57. 57. PoS Tagging Credit Card Example - P1 Adjectives Context Clues: Words that clarify meaning & help us understand what a searcher wants @RoryT1
  58. 58. •Create landing pages that are aligned with the intent of a searcher •Help copywriters understand the language and desires of a target audience •Tactically incorporate more semantically relevant phrases into landing pages HOW CAN WE APPLY THIS? @RoryT1
  59. 59. Can we use NLP to uncover topical trends in the SERPs to help us with content ideation? Topic Modelling @RoryT1
  60. 60. 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
  61. 61. Topic Modelling OK, computer. Here’s some words. Group them. @RoryT1 RoryTruesdale Cheapening machine learning since 2019
  62. 62. Topic Modelling Each bubble represents a topic @RoryT1
  63. 63. Topic Modelling The bigger the bubble the more prominent the topic @RoryT1
  64. 64. Topic Modelling The further away the bubbles are, the more distinct those topic are
  65. 65. Topic Modelling Get a breakdown of the terms our topics consist of @RoryT1
  66. 66. Topic Modelling The output is an interactive visual on topical trends that can be easily shared with other teams @RoryT1
  67. 67. Topic Modelling Use Google’s algorithm to help us identify areas of interest for our audience
  68. 68. Topic Modelling Uncover topical trends hidden in the language of the SERPs that can inform content ideation @RoryT1
  69. 69. •Valuable data point to reference for content ideation •Inform internal linking and content recommendations across a website •Incorporate topically relevant phrases into existing pages to improve semantic relevance HOW CAN WE APPLY THIS? @RoryT1
  70. 70. How can we make our scripts work across other data sources to understand our customers? Other Useful Applications @RoryT1
  71. 71. Product Reviews @RoryT11
  72. 72. Product Reviews @RoryT11
  73. 73. GMB Reviews @RoryT11
  74. 74. GMB Reviews @RoryT11
  75. 75. Reddit @RoryT11
  76. 76. Reddit @RoryT11
  77. 77. YouTube Captions @RoryT11
  78. 78. YouTube Captions @RoryT11
  79. 79. Competitors & Top Ranking Pages @RoryT11
  80. 80. Competitors & Top Ranking Pages @RoryT11
  81. 81. With some minor tweaks we can make our scripts work across a huge corpus of user- centric content Pretty cool, right? @RoryT1
  82. 82. Potential to ramp up and apply sentiment analysis to these sources for useful visualisations @RoryT11
  83. 83. Deconstruct product reviews to find out what really matters to customers •Simple •Easy to use •Intuitive •Buggy •Slow @RoryT1
  84. 84. A lot to take in…what does it all mean? @RoryT11
  85. 85. SERPs give us amazing insight into what customers want @RoryT1
  86. 86. Python makes getting these insights at scale accessible @RoryT1
  87. 87. Use these insights to align landing pages with intent and semantic relevance @RoryT1
  88. 88. Scripts we create allow us to get these insights from lots of other user-centric sources beyond the SERPs @RoryT1
  89. 89. http://cndr.co/jupyter Python Dropbox Link @RoryT1
  90. 90. Get The Slides @RoryT11 http://cndr.co/brighton
  91. 91. • 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
  92. 92. Thanks For Listening! Conductor.com @RoryT11 brightonseo@conductor.com

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