Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Mining the SERP for SEO, Content & Customer Insights

6b68737f9aacb94936b269f2592da891?s=47 DDavydoff
September 20, 2019

Mining the SERP for SEO, Content & Customer Insights



September 20, 2019


  1. Mining the SERPs for SEO, Content & Customer Insights Rory

    Truesdale // Conductor http://cndr.co/brighton @RoryT11
  2. About Me Rory Truesdale •SEO Strategist at Conductor •EMEA SEO

    lead for WeWork Get In Touch brightonseo@conductor.com @RoryT11 @RoryT11
  3. Get The Slides @RoryT11 http://cndr.co/brighton

  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. That’s how often Google rewrites the SERP displayed meta description

  6. WHY?

  7. To make SEOs sad?

  8. Just for a laugh?

  9. Nope…

  10. It’s because Google thinks it is smarter than us

  11. Intriguing… Can we use that to our advantage?

  12. Yes, we can! (sorry, that was the last puppy pic)

  13. How? @RoryT11

  14. We can deconstruct & analyse the language in SERP displayed

    content to learn what Google thinks our customers are interested in @RoryT1
  15. Curious? This is important because we are in the age

    of semantic search @RoryT1
  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. • User intent • Query context •Topical relevance • Word

    relationships Target the keyword, but optimise for this. On-page Optimisation @RoryT1
  18. Understand customer intent & desire to better tailor your messaging

  19. Structure landing pages to help Google understand context & how

    it meets the needs of the searcher @RoryT1
  20. Build more meaningful online experiences that better convert website visitors

  21. Your Toolkit @RoryT11

  22. You need SERP content There are three ways you can

    get this. @RoryT1
  23. Scrape at scale with Screaming Frog Follow these instructions @RoryT1

    Option A
  24. Option B Get SERP content via an API

  25. Option C Get SERP content using the Scraper Chrome extension

    Get Scraper
  26. There are four ways you can get this. You need

    Jupyter Notebook What is that?
  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. Stumped? Me too… Here’s my definition

  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. Rory Truesdale Python Charlatan @RoryT1
  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. You’ll end up with… @RoryT11

  32. Your SERP content in a CSV @RoryT1

  33. Imported into Jupyter Notebook @RoryT1

  34. You’re ready to use Python to analyse the SERPs! @RoryT1

  35. There’s a treat for you.

  36. I’ll share a link to a Dropbox with everything you

    need to get you started @RoryT11
  37. Before we dive in…

  38. Start by cleaning your SERP content @RoryT1

  39. Lower case avoids duplication & punctuation adds no value to

    this analysis Lower Case & Remove Punctuation @RoryT1
  40. Stop words are commonly occurring words that don’t improve our

    analysis Remove Stop Words @RoryT1
  41. The process of chopping up a sentence into individual pieces,

    called ‘tokens’ Tokenization @RoryT1
  42. The process of converting a word to its root (i.e.

    “playing” becomes “play”) Lemmatization (optional) @RoryT1
  43. @RoryT11

  44. @RoryT11

  45. How many times does a word or combination of words

    appear in your SERP content? Co-occurrence @RoryT1
  46. Co- occurrence Snapshot of phrases frequently occurring in the SERPs

  47. Co- occurrence Demonstrates the topics competitors cover on landing pages

  48. Co- occurrence Understand the types of phrases that Google sees

    as semantically relevant to a target keyword set
  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. 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. What are the most frequently occurring nouns, verbs & adjectives

    in a SERP? Part of Speech Tagging @RoryT1
  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. PoS Tagging Get clues around how Google is interpreting the

    context and intent of a search Verbs (action or state) @RoryT1
  54. PoS Tagging Understand the language and tone that might resonate

    with a searcher Adjectives (descriptive word) @RoryT1
  55. PoS Tagging Credit Card Example – P1 Verbs Intent Clues:

    What is the specific motivation our searcher has?
  56. PoS Tagging Credit Card Example - P1 Nouns Context Clues:

    Words that clarify meaning & help us understand what a searcher wants @RoryT1
  57. PoS Tagging Credit Card Example - P1 Adjectives Context Clues:

    Words that clarify meaning & help us understand what a searcher wants @RoryT1
  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. Can we use NLP to uncover topical trends in the

    SERPs to help us with content ideation? Topic Modelling @RoryT1
  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. Topic Modelling OK, computer. Here’s some words. Group them. @RoryT1

    Rory Truesdale Cheapening machine learning since 2019
  62. Topic Modelling Each bubble represents a topic @RoryT1

  63. Topic Modelling The bigger the bubble the more prominent the

    topic @RoryT1
  64. Topic Modelling The further away the bubbles are, the more

    distinct those topic are
  65. Topic Modelling Get a breakdown of the terms our topics

    consist of @RoryT1
  66. Topic Modelling The output is an interactive visual on topical

    trends that can be easily shared with other teams @RoryT1
  67. Topic Modelling Use Google’s algorithm to help us identify areas

    of interest for our audience
  68. Topic Modelling Uncover topical trends hidden in the language of

    the SERPs that can inform content ideation @RoryT1
  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. How can we make our scripts work across other data

    sources to understand our customers? Other Useful Applications @RoryT1
  71. Product Reviews @RoryT11

  72. Product Reviews @RoryT11

  73. GMB Reviews @RoryT11

  74. GMB Reviews @RoryT11

  75. Reddit @RoryT11

  76. Reddit @RoryT11

  77. YouTube Captions @RoryT11

  78. YouTube Captions @RoryT11

  79. Competitors & Top Ranking Pages @RoryT11

  80. Competitors & Top Ranking Pages @RoryT11

  81. With some minor tweaks we can make our scripts work

    across a huge corpus of user- centric content Pretty cool, right? @RoryT1
  82. Potential to ramp up and apply sentiment analysis to these

    sources for useful visualisations @RoryT11
  83. Deconstruct product reviews to find out what really matters to

    customers •Simple •Easy to use •Intuitive •Buggy •Slow @RoryT1
  84. A lot to take in…what does it all mean? @RoryT11

  85. SERPs give us amazing insight into what customers want @RoryT1

  86. Python makes getting these insights at scale accessible @RoryT1

  87. Use these insights to align landing pages with intent and

    semantic relevance @RoryT1
  88. Scripts we create allow us to get these insights from

    lots of other user-centric sources beyond the SERPs @RoryT1
  89. http://cndr.co/jupyter Python Dropbox Link @RoryT1

  90. Get The Slides @RoryT11 http://cndr.co/brighton

  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. Thanks For Listening! Conductor.com @RoryT11 brightonseo@conductor.com