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.

1. Analysing the SERPS
For SEO, Content &
Customer Insights

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

3. WHY?

4. To make
SEOs sad?

5. Just for a
laugh?

6. Nope…

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

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

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

10. How?
@RoryT11

11. Deconstruct &
analyse the
language of the
SERPs
@RoryT1

12. Curious?
We are in the age of
semantic search
@RoryT1

13. Google isn’t ranking a page based on how it
uses a keyword
On-page Optimisation
@RoryT1

14. • User intent
• Query context
•Topical relevance
• Word relationships
Target the keyword, but optimise for this.
How does Google provide accurate results?
@RoryT1

15. Understand
customer
intent to better
tailor your
messaging
@RoryT1

16. Structure
landing pages
to help Google
understand
context
@RoryT1

17. Create more
impactful
online
experiences
@RoryT1

18. Your
Toolkit
@RoryT11

19. You need
SERP content
There are
four ways
you can get
this.
@RoryT1

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

21. Option
B
Get SERP content via an
API

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

23. Option
D
Use ‘Thruuu’ – a free
SERP analysis tool
Use
Thruuu
By Samuel Schmitt

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

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. Stumped.

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

29. You’ll end up
with…
@RoryT11

30. Your SERP content in a CSV@RoryT1

31. Imported into Jupyter Notebook
@RoryT1

32. You’re (nearly) ready to use
Python to analyse the SERPs!
@RoryT1

33. There’s a
treat for you.

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

35. Before we
dive in…

36. Start by
cleaning
your SERP
content
@RoryT1

37. Avoids
duplication &
punctuation
adds no value
Lower case &
remove
punctuation
@RoryT1

38. Get rid of words like:
“Do”
“Of”
“Am”
“If”
“But”
Remove
stop
words
@RoryT1

39. Chop up a
sentence into
individual pieces
Tokenization
@RoryT1

40. Convert a word to
its root:
-‘Playing’ > ‘Play’
-‘Crawling’ > ‘Crawl’
Lemmatization
(optional)
@RoryT1

41. @RoryT11

42. @RoryT11

43. How many times
a combination of
words appear in
your SERP
content?
Co-occurrence
@RoryT1

44. Co-
occurrence
@RoryT1

45. Co-
occurrence
Shows the topics
competitors
cover on landing
pages
@RoryT1

46. Co-
occurrence
What does
Google see as
semantically
relevant?
@RoryT1

47. •Additional keyword research
•Topical content gaps
•Use semantically relevant phrases on landing pages
HOW CAN WE APPLY THIS?
@RoryT1

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. What are the most
frequently
occurring nouns,
verbs &
adjectives in a
SERP?
Part of Speech
(PoS)
tagging
@RoryT1

50. PoS
tagging
What
‘things’ are
competitors
writing
about?
Nouns (people, place, thing)
@RoryT1

51. PoS
tagging
How is Google
interpreting
the context
and intent of a
search
Verbs (action or state)
@RoryT1

52. PoS
tagging
The language
& tone that
will resonate
with a
searcher
Adjectives (descriptive word)
@RoryT1

53. PoS
tagging
Credit Card Example
Verbs = Intent Clues

54. PoS
tagging
Credit Card Example
Nouns = Context Clues
@RoryT1

55. PoS
tagging
Credit Card Example
Adjectives = Context Clues
@RoryT1

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. Can we use NLP
to uncover
topical trends
in the SERPs?
Topic
modelling
@RoryT1

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. Topic
modelling
OK, computer. Here are some words. Group them.
@RoryT1
RoryTruesdale
Cheapening machine
learning since 2019

60. Topic
modelling
Each bubble
represents a
topic
@RoryT1

61. Topic
modelling
The bigger
the bubble
the more
prominent
the topic
@RoryT1

62. Topic
modelling
The further
away the
bubbles are,
the more
distinct those
topic are

63. Topic
modelling
Get a
breakdown of
the terms our
topics consist
of
@RoryT1

64. Topic
modelling
@RoryT1

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

66. •Reference for content ideation
•Internal linking and content recommendations
•Optimise effectively for semantic relevance
HOW CAN WE APPLY THIS?
@RoryT1

67. Can we make our
scripts work
across other data
sources to
understand our
customers?
Other
useful
applications
@RoryT1

68. Product
Reviews
@RoryT11

69. GMB
Reviews
@RoryT11

70. Reddit
@RoryT11

71. YouTube
Captions
@RoryT11

72. Competitors & Top
Ranking Pages
@RoryT11

73. You can
scrape
it all!
@RoryT11

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

75. Visualise sentiment across GMB reviews
@RoryT11

76. Positive:
• Simple
• Easy to use
• Intuitive
Negative:
• Buggy
• Broken exports
• Crashes
@RoryT1
Identify recurring themes in product reviews

77. Create networks based on word relationships
@RoryT11

78. What does
it all
mean?
@RoryT11

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

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

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

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

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

84. Get the slides link
@RoryT11
http://cndr.co/searchlove

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. Thanks
For
Listening!
Conductor.com
@RoryT11