Mining the SERP for SEO, Content & Customer Insights

Transcript

1. Mining the SERPs for SEO,
   Content & Customer
   Insights
   Rory Truesdale // Conductor
   http://cndr.co/brighton
   @RoryT11
   

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2. About Me
   Rory Truesdale
   •SEO Strategist at Conductor
   •EMEA SEO lead for WeWork
   Get In Touch
   brightonseo@conductor.com
   @RoryT11
   @RoryT11
   

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

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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
   

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

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6. WHY?
   

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7. To make
   SEOs sad?
   

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8. Just for a
   laugh?
   

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9. Nope…
   

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

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

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

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13. How?
    @RoryT11
    

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14. We can deconstruct &
    analyse the language in
    SERP displayed content
    to learn what Google
    thinks our customers
    are interested in
    @RoryT1
    

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15. Curious?
    This is important
    because we are
    in the age of
    semantic search
    @RoryT1
    

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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
    

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17. • User intent
    • Query context
    •Topical relevance
    • Word relationships
    Target the keyword, but optimise for this.
    On-page Optimisation
    @RoryT1
    

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18. Understand
    customer intent
    & desire to better
    tailor your
    messaging
    @RoryT1
    

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19. Structure landing
    pages to help
    Google understand
    context & how it
    meets the needs
    of the searcher
    @RoryT1
    

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20. Build more
    meaningful
    online
    experiences that
    better convert
    website visitors
    @RoryT1
    

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21. Your
    Toolkit
    @RoryT11
    

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22. You need
    SERP content
    There are
    three ways
    you can get
    this.
    @RoryT1
    

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23. Scrape at scale with
    Screaming Frog
    Follow these instructions
    @RoryT1
    Option
    A
    

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24. Option
    B
    Get SERP content via an
    API
    

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

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26. There are four
    ways you can
    get this.
    You need
    Jupyter Notebook
    What is
    that?
    

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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
    

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28. Stumped?
    Me too…
    Here’s my definition
    

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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
    

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30. Resources to get started…
    Jupyter
    Notebook –
    Getting
    Started Guide
    Robin Lord
    Find
    scripts
    Paul Shapiro JR Oakes Hamlet
    Batista
    Find
    scripts
    Find
    scripts
    

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31. You’ll end up
    with…
    @RoryT11
    

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32. Your SERP content in a CSV
    @RoryT1
    

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33. Imported into Jupyter Notebook
    @RoryT1
    

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

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35. There’s a
    treat for you.
    

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36. I’ll share a link to a Dropbox
    with everything you need to get
    you started
    @RoryT11
    

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37. Before we
    dive in…
    

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38. Start by
    cleaning
    your SERP
    content
    @RoryT1
    

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39. Lower case avoids
    duplication &
    punctuation adds
    no value to this
    analysis
    Lower Case &
    Remove
    Punctuation
    @RoryT1
    

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40. Stop words are
    commonly
    occurring words
    that don’t improve
    our analysis
    Remove
    Stop
    Words
    @RoryT1
    

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41. The process of
    chopping up a
    sentence into
    individual pieces,
    called ‘tokens’
    Tokenization
    @RoryT1
    

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42. The process of
    converting a word
    to its root (i.e.
    “playing” becomes
    “play”)
    Lemmatization
    (optional)
    @RoryT1
    

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43. @RoryT11
    

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44. @RoryT11
    

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45. How many times
    does a word or
    combination of
    words appear in
    your SERP
    content?
    Co-occurrence
    @RoryT1
    

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46. Co-
    occurrence
    Snapshot of
    phrases
    frequently
    occurring in
    the SERPs
    @RoryT1
    

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47. Co-
    occurrence
    Demonstrates
    the topics
    competitors
    cover on landing
    pages
    @RoryT1
    

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48. Co-
    occurrence
    Understand the
    types of phrases
    that Google sees
    as semantically
    relevant to a target
    keyword set
    

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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
    

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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
    

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51. What are the most
    frequently
    occurring nouns,
    verbs &
    adjectives in a
    SERP?
    Part of Speech
    Tagging
    @RoryT1
    

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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
    

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53. PoS
    Tagging
    Get clues around
    how Google is
    interpreting the
    context and intent
    of a search
    Verbs (action or state)
    @RoryT1
    

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54. PoS
    Tagging
    Understand the
    language and
    tone that might
    resonate with a
    searcher
    Adjectives (descriptive word)
    @RoryT1
    

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55. PoS
    Tagging
    Credit Card Example – P1 Verbs
    Intent Clues: What is the specific motivation
    our searcher has?
    

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

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

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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
    

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59. Can we use NLP
    to uncover topical
    trends in the
    SERPs to help us
    with content
    ideation?
    Topic
    Modelling
    @RoryT1
    

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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
    

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61. Topic
    Modelling
    OK, computer. Here’s some words. Group them.
    @RoryT1
    Rory Truesdale
    Cheapening machine
    learning since 2019
    

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62. Topic
    Modelling
    Each bubble
    represents a
    topic
    @RoryT1
    

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63. Topic
    Modelling
    The bigger
    the bubble
    the more
    prominent
    the topic
    @RoryT1
    

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64. Topic
    Modelling
    The further
    away the
    bubbles are,
    the more
    distinct those
    topic are
    

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65. Topic
    Modelling
    Get a
    breakdown of
    the terms our
    topics consist
    of
    @RoryT1
    

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66. Topic
    Modelling
    The output is an
    interactive visual
    on topical trends
    that can be easily
    shared with other
    teams
    @RoryT1
    

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67. Topic
    Modelling
    Use Google’s
    algorithm to help
    us identify areas
    of interest for our
    audience
    

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68. Topic
    Modelling
    Uncover topical
    trends hidden in
    the language of
    the SERPs that
    can inform
    content ideation
    @RoryT1
    

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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
    

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70. How can we make
    our scripts work
    across other data
    sources to
    understand our
    customers?
    Other
    Useful
    Applications
    @RoryT1
    

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71. Product
    Reviews
    @RoryT11
    

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72. Product Reviews
    @RoryT11
    

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73. GMB
    Reviews
    @RoryT11
    

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74. GMB Reviews
    @RoryT11
    

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75. Reddit
    @RoryT11
    

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76. Reddit
    @RoryT11
    

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77. YouTube
    Captions
    @RoryT11
    

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78. YouTube Captions
    @RoryT11
    

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79. Competitors & Top
    Ranking Pages
    @RoryT11
    

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80. Competitors & Top Ranking Pages
    @RoryT11
    

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81. With some minor
    tweaks we can
    make our scripts
    work across a huge
    corpus of user-
    centric content
    Pretty cool, right?
    @RoryT1
    

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82. Potential to ramp up and apply sentiment analysis
    to these sources for useful visualisations
    @RoryT11
    

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83. Deconstruct product reviews to find out what really
    matters to customers
    •Simple
    •Easy to use
    •Intuitive
    •Buggy
    •Slow
    @RoryT1
    

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84. A lot to
    take
    in…what
    does it all
    mean?
    @RoryT11
    

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

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

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87. Use these insights
    to align landing
    pages with intent
    and semantic
    relevance
    @RoryT1
    

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88. Scripts we create
    allow us to get these
    insights from lots of
    other user-centric
    sources beyond the
    SERPs
    @RoryT1
    

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89. http://cndr.co/jupyter
    Python Dropbox Link
    @RoryT1
    

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

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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
    

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

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