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Mining the SERPs for SEO,
Content & Customer
Insights
Rory Truesdale // Conductor
http://cndr.co/brighton
@RoryT11
About Me
Rory Truesdale
•SEO Strategist at Conductor
•EMEA SEO lead for WeWork
Get In Touch
brightonseo@conductor.com
@Ror...
Get The Slides
@RoryT11
http://cndr.co/brighton
•SERPs are a great resource to learn what Google
‘thinks’ our customers want
•Workflows that will help you understand the ...
That’s how often Google rewrites the SERP
displayed meta description
WHY?
To make
SEOs sad?
Just for a
laugh?
Nope…
It’s because Google thinks
it is smarter than us
Intriguing…
Can we use that to our advantage?
Yes, we
can!
(sorry, that was
the last puppy pic)
How?
@RoryT11
We can deconstruct &
analyse the language in
SERP displayed content
to learn what Google
thinks our customers
are interest...
Curious?
This is important
because we are
in the age of
semantic search
@RoryT1
Google isn’t ranking a page based on how
it uses a keyword.
Google provides accurate results based on
intent, query contex...
• User intent
• Query context
•Topical relevance
• Word relationships
Target the keyword, but optimise for this.
On-page O...
Understand
customer intent
& desire to better
tailor your
messaging
@RoryT1
Structure landing
pages to help
Google understand
context & how it
meets the needs
of the searcher
@RoryT1
Build more
meaningful
online
experiences that
better convert
website visitors
@RoryT1
Your
Toolkit
@RoryT11
You need
SERP content
There are
three ways
you can get
this.
@RoryT1
Scrape at scale with
Screaming Frog
Follow these instructions
@RoryT1
Option
A
Option
B
Get SERP content via an
API
Option
C
Get SERP content using the
Scraper Chrome extension
Get
Scraper
There are four
ways you can
get this.
You need
Jupyter Notebook
What is
that?
The Jupyter Notebook is an open-source web
application that allows you to create and share
documents that contain live cod...
Stumped?
Me too…
Here’s my definition
Jupyter Notebook is an environment on my laptop
where I can learn Python by copying scripts created
by people significantl...
Resources to get started…
Jupyter
Notebook –
Getting
Started Guide
Robin Lord
Find
scripts
Paul Shapiro JR Oakes Hamlet
Ba...
You’ll end up
with…
@RoryT11
Your SERP content in a CSV@RoryT1
Imported into Jupyter Notebook
@RoryT1
You’re
ready to
use Python
to analyse
the SERPs!
@RoryT1
There’s a
treat for you.
I’ll share a link to a Dropbox
with everything you need to get
you started
@RoryT11
Before we
dive in…
Start by
cleaning
your SERP
content
@RoryT1
Lower case avoids
duplication &
punctuation adds
no value to this
analysis
Lower Case &
Remove
Punctuation
@RoryT1
Stop words are
commonly
occurring words
that don’t improve
our analysis
Remove
Stop
Words
@RoryT1
The process of
chopping up a
sentence into
individual pieces,
called ‘tokens’
Tokenization
@RoryT1
The process of
converting a word
to its root (i.e.
“playing” becomes
“play”)
Lemmatization
(optional)
@RoryT1
@RoryT11
@RoryT11
How many times
does a word or
combination of
words appear in
your SERP
content?
Co-occurrence
@RoryT1
Co-
occurrence
Snapshot of
phrases
frequently
occurring in
the SERPs
@RoryT1
Co-
occurrence
Demonstrates
the topics
competitors
cover on landing
pages
@RoryT1
Co-
occurrence
Understand the
types of phrases
that Google sees
as semantically
relevant to a target
keyword set
•Additional source of data for keyword research
•Identify topical content gaps on landing pages
•Optimise landing page con...
Cost:
Range:
Time to Charge:
Battery Size/Capacity:
All Wheel Drive:
Towing Capacity:
Semi-Conductor SERP XLT: Product Pag...
What are the most
frequently
occurring nouns,
verbs &
adjectives in a
SERP?
Part of Speech
Tagging
@RoryT1
PoS
Tagging
Uncover the
phrases or topics
you should include
in your landing
pages to rank for a
term
Nouns (people, place...
PoS
Tagging
Get clues around
how Google is
interpreting the
context and intent
of a search
Verbs (action or state)
@RoryT1
PoS
Tagging
Understand the
language and
tone that might
resonate with a
searcher
Adjectives (descriptive word)
@RoryT1
PoS
Tagging
Credit Card Example – P1 Verbs
Intent Clues: What is the specific motivation
our searcher has?
PoS
Tagging
Credit Card Example - P1 Nouns
Context Clues: Words that clarify meaning &
help us understand what a searcher ...
PoS
Tagging
Credit Card Example - P1 Adjectives
Context Clues: Words that clarify meaning &
help us understand what a sear...
•Create landing pages that are aligned with the
intent of a searcher
•Help copywriters understand the language and
desires...
Can we use NLP
to uncover topical
trends in the
SERPs to help us
with content
ideation?
Topic
Modelling
@RoryT1
Topic
Modelling
Topic modelling is an NLP method that assumes a
corpus contains a mixture of topics. It looks at how
words...
Topic
Modelling
OK, computer. Here’s some words. Group them.
@RoryT1
RoryTruesdale
Cheapening machine
learning since 2019
Topic
Modelling
Each bubble
represents a
topic
@RoryT1
Topic
Modelling
The bigger
the bubble
the more
prominent
the topic
@RoryT1
Topic
Modelling
The further
away the
bubbles are,
the more
distinct those
topic are
Topic
Modelling
Get a
breakdown of
the terms our
topics consist
of
@RoryT1
Topic
Modelling
The output is an
interactive visual
on topical trends
that can be easily
shared with other
teams
@RoryT1
Topic
Modelling
Use Google’s
algorithm to help
us identify areas
of interest for our
audience
Topic
Modelling
Uncover topical
trends hidden in
the language of
the SERPs that
can inform
content ideation
@RoryT1
•Valuable data point to reference for content
ideation
•Inform internal linking and content
recommendations across a websi...
How can we make
our scripts work
across other data
sources to
understand our
customers?
Other
Useful
Applications
@RoryT1
Product
Reviews
@RoryT11
Product Reviews
@RoryT11
GMB
Reviews
@RoryT11
GMB Reviews
@RoryT11
Reddit
@RoryT11
Reddit
@RoryT11
YouTube
Captions
@RoryT11
YouTube Captions
@RoryT11
Competitors & Top
Ranking Pages
@RoryT11
Competitors & Top Ranking Pages
@RoryT11
With some minor
tweaks we can
make our scripts
work across a huge
corpus of user-
centric content
Pretty cool, right?
@Ror...
Potential to ramp up and apply sentiment analysis
to these sources for useful visualisations
@RoryT11
Deconstruct product reviews to find out what really
matters to customers
•Simple
•Easy to use
•Intuitive
•Buggy
•Slow
@Ror...
A lot to
take
in…what
does it all
mean?
@RoryT11
SERPs give us
amazing insight
into what
customers want
@RoryT1
Python makes
getting these
insights at scale
accessible
@RoryT1
Use these insights
to align landing
pages with intent
and semantic
relevance
@RoryT1
Scripts we create
allow us to get these
insights from lots of
other user-centric
sources beyond the
SERPs
@RoryT1
http://cndr.co/jupyter
Python Dropbox Link
@RoryT1
Get The Slides
@RoryT11
http://cndr.co/brighton
• https://www.searchenginejournal.com/scrape-google-serp-custom-extractions/267211/
• https://www.searchenginejournal.com/...
Thanks
For
Listening!
Conductor.com
@RoryT11
brightonseo@conductor.com
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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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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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