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BEGINNER-FRIENDLY
GUIDE TO ML-ENABLED
AUTOMATION IN
ORGANIC MARKETING
LAZARINA STOY.
SEO & Data Science Consultant
@lazarinastoy
www.smxl.it
@lazarinastoy
www.smxl.it @lazarinastoy
www.smxl.it
the
‘YOU NEED HELP AND HELP IS THERE FOR YOU’
argument
@lazarinastoy
www.smxl.it @lazarinastoy
www.smxl.it
the
‘AUTOMATION IS THE FUTURE’
argument
@lazarinastoy
www.smxl.it @lazarinastoy
www.smxl.it
the
‘SOMETHING IS HAPPENING AND IT’S MAKE
OR BREAK TIME’
argument
@lazarinastoy
www.smxl.it @lazarinastoy
www.smxl.it
the
‘TAKE BACK TIME’
argument
@lazarinastoy
www.smxl.it @lazarinastoy
www.smxl.it
the
‘CHALLENGE YOURSELF BY LEARNING
SOMETHING NEW’
argument
@lazarinastoy
www.smxl.it
I’ll let you choose, whichever
speaks the most to you
@lazarinastoy
www.smxl.it
Instead, I’ll start with this:
@lazarinastoy
www.smxl.it
There’s help in the face of task
automation for accelerating your
organic performance in most of these
categories.
@lazarinastoy
www.smxl.it
So, why not get ahead?
@lazarinastoy
www.smxl.it
(before we dive in)
@lazarinastoy
www.smxl.it
x This is a beginner-friendly talk,
meaning you can absolutely do
everything mentioned here, if you
are a true beginner
x No advanced coding needed but
consider it a starting point - more
advanced scripts will likely lead to
better, more tailored results
x During this talk you might forget
about chatGPT being the “ultimate
AI model” (because it is not a ‘one
size fits all’ type of field
@lazarinastoy
www.smxl.it
entities
let’s talk about
@lazarinastoy
www.smxl.it
@lazarinastoy
www.smxl.it
entity analysis is a must-have in today’s search
landscape
@lazarinastoy
www.smxl.it
What components
should you extract
and analyse the
entities of?
● your pages’ content
● your titles and meta descriptions
(SERP data) versus your
competitors’ titles and meta
descriptions
● your competitors’ content
★ first-party data
★ UGC
★ social mentions
@lazarinastoy
www.smxl.it
does it matter how you do it?
@lazarinastoy
www.smxl.it
yes - use a tailored model (not chatGPT)
@lazarinastoy
www.smxl.it
Google’s Natural Language API vs ChatGPT
53 16
@lazarinastoy
www.smxl.it
• Entity = recognised thing/concept
• Salience = importance
• Sentiment Score
• Sentiment Magnitude = strength
• # of mentions = entity prominence
@lazarinastoy
www.smxl.it
With Google’s Natural
Language API, you can also
do Syntax analysis
• Dependency
• Parse label
• Part of speech
• Lemma
• Morphology
@lazarinastoy
www.smxl.it
How important getting this right?
@lazarinastoy
www.smxl.it
Entity analysis work is central to multiple SEO projects
@lazarinastoy
www.smxl.it
(pt.1 - SERP analysis)
● Entity-driven SERP analysis:
○ Using your keyword universe as a
starting point, collect SERP data in bulk
via dataforSEO
○ Run entity extraction on the titles and
meta descriptions of results ranking on
positions 1-10
○ Analyze the data to inform the content
direction
@lazarinastoy
www.smxl.it
(pt.2 - keyword research):
● Entity-driven Keyword Research
○ Using your keyword data as a starting point, validate which keywords contain
entities
○ Create content maps, based on closely linked entities
○ Create lists of positive and negative secondary entities to discuss for content
writers
○ Create lists of entity attributes to address in content
○ As an additional step, validate which keywords contain entities that are part of the
knowledge graph
@lazarinastoy
www.smxl.it
(pt.3 - internal link analysis):
● Entity-driven Internal linking anchor text
analysis
○ Using your internal linking anchor text data as a
starting point, validate which anchors contain
entities
○ As an additional step, validate which keywords
contain entities that are part of the knowledge
graph 1: Analyze anchor text for entities
@lazarinastoy
www.smxl.it
(pt.3 - internal link analysis):
● Entity-driven content analysis for internal
link identification analysis
○ Using your website content (scrape the content
via crawling), combined with internal links and
traffic report, identify pages with entities
mentioned and relationship with internal links
and traffic.
@lazarinastoy
www.smxl.it
(pt.4 - content analysis, etc..):
● Entity-driven site content analysis
○ Starting with your website content (scraped), extract entities from the text, titles and meta descriptions
○ If an entity, appears more than X times on a page of Y words, highlight it as the prominent entity for the article
○ How many articles with prominent entities have them in the title/meta description
○ How many articles with prominent entities have internal links incoming from other articles, where the same
entities are mentioned → internal link opportunities
○ Highlight articles that don’t contain any entities, map with traffic → Content enhancement/ consolidation
opportunities
● Entity-driven competitor content analysis
○ Process is same as above, only with competitor website content
● Entity-driven social comments analysis, etc…
○ Scrape social comments from YouTube, TikTok, Twitter, Facebook
○ Analyse for entities
○ Map against site content
@lazarinastoy
www.smxl.it
the quality of your analysis will dictate the
quality of your content strategy
@lazarinastoy
www.smxl.it
(so, pretty important to get it right)
@lazarinastoy
www.smxl.it
let’s talk process
@lazarinastoy
www.smxl.it
your api key set-up guide
your sheets template
1. Copy the templates.
Get your API key.
Enter it in the AppScript tab.
✨ copy the Sheets templates ✨
@lazarinastoy
www.smxl.it
URLs Meta Descriptions
2. Input your data
(URL (yours or
competitors’)
+ page content,
paragraphs, titles, MDs,
etc.)
@lazarinastoy
www.smxl.it
3. Get the entities and
sentiment scores for each
of your analyzed data
points (pages content,
titles, feedback forms, …)
@lazarinastoy
www.smxl.it
4. Visualize in Looker
Studio and explore the
data
@lazarinastoy
www.smxl.it
@lazarinastoy
www.smxl.it
✨ get the Looker Studio template ✨
If you’re working with a multi-million page site, or massive
dataset, choose Python & Pandas library, not Google Sheets
Google releases handy code labs
(practice environments) with
sample code.
Tip: Prompt an LLM or code buddy
to rework the code so it works in:
● Google colab
● With an API key
● Takes input a pandas dataset
(csv)
Test, troubleshoot, iterate as
needed for your dataset.
@lazarinastoy
www.smxl.it
Then what?
● Audit if you’re stuffing your pages with unnecessary entities that are not
related to your main topic
● Audit if you’re providing enough context about the topics and entities
you’re discussing for Google to know what you’re talking about
● Audit the use of entities between you and other, better-performing
competitors
@lazarinastoy
www.smxl.it
a step further:
understanding language used within the
high-performing articles for your terms can be
beneficial for building content briefs.
@lazarinastoy
www.smxl.it
@lazarinastoy
www.smxl.it
@lazarinastoy
www.smxl.it
@lazarinastoy
www.smxl.it
Language use analysis can also be useful for scaling generation of
or identifying opportunities for Structured Data (schema) mark-up
@lazarinastoy
www.smxl.it
Here’s a demo with perhaps the simplest of solutions & schema types...
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [{
"@type": "Question",
"name": "What is the return policy?",
"acceptedAnswer": {
"@type": "Answer",
"text": "<p>Most unopened items in new condition and returned within <b>90 days</b> will
receive a refund or exchange. Some items have a modified return policy noted on the receipt or
packing slip. Items that are opened or damaged or do not have a receipt may be denied a refund
or exchange. Items purchased online or in-store may be returned to any store.</p><p>Online
purchases may be returned via a major parcel carrier. <a href=https://example.com/returns> Click
here </a> to initiate a return.</p>"
}
}, {
"@type": "Question",
"name": "How long does it take to process a refund?",
"acceptedAnswer": {
"@type": "Answer",
"text": "We will reimburse you for returned items in the same way you paid for them. For
example, any amounts deducted from a gift card will be credited back to a gift card. For returns by
mail, once we receive your return, we will process it within 4–5 business days. It may take up to 7
days after we process the return to reflect in your account, depending on your financial
institution's processing time."
}
}, { }
</script>
@lazarinastoy
www.smxl.it
How it works:
● You provide the content
(currently - copy-paste; of course,
you can edit the script so it reads
from a column from a pandas
dataset (csv) if working in bulk)
● Script tokenizes the text,
discovers the questions, and
pulls the answers
● Script organises these into a
schema dictionary, which is
saved as a JSON file
@lazarinastoy
www.smxl.it
Here’s an example blog post:
Run the cell, paste the content
Download the output JSON
@lazarinastoy
www.smxl.it
Review if the replies are accurate - typically
it might include more than one paragraph,
so you might want to shorten them. (you
can also edit the script so it only pulls the
first 1 or two sentences after the question)
TIP: If using for multiple content pieces,
crawl your site, export the content, and
change the method to list-based based on
the column where the content is exported,
as opposed to user input. (using python
pandas library)
Review & edit, if needed
@lazarinastoy
www.smxl.it
Upload.
What took minutes even with the most
straightforward methods (e.g. using a
schema markup generator, and
copy-pasting individual questions into it),
now took seconds.
With a bit of testing and script
manipulation, you can put this process into
production.
@lazarinastoy
www.smxl.it
This approach is great for websites and schema types
that are niche-specific
(eg. VehicleListing, PropertyListing, Recipe, etc.) where you:
○ You know the page format used for all pages and the elements you should pull schema
data into
○ you know the structure of the schema
○ AND if you have a website with hundreds or thousands of pages, you can also use
keyword identification to flag pages for structured data or migration to a particular
template
@lazarinastoy
www.smxl.it
clustering and classification
let’s talk about
@lazarinastoy
www.smxl.it
Classification vs Clustering - What’s the difference?
Classification sorts data into specific
categories using a labeled dataset.
Clustering is partitioning an unlabeled
dataset into groups of similar objects.
@lazarinastoy
www.smxl.it
How important is classification, really?
@lazarinastoy
www.smxl.it
Content understanding
quickly understand what topics the site is covering
with the content
Content Gap analysis
check whether the content topics on the site aligns
with the business direction desired
Competitor analysis
quickly understand the topics that competitors’
content talks about
@lazarinastoy
www.smxl.it
With Google’s Natural Language API, you can classify
documents in 700+ predefined categories
(out of the box, can be custom-trained, too with AutoML)
@lazarinastoy
www.smxl.it
With Open AI’s GPT-3 or with ChatGPT, you can do both,
but results are a hit or miss.
@lazarinastoy
www.smxl.it
Can you guess what can go great?
(and what - horribly wrong)
@lazarinastoy
www.smxl.it
✅Predictable categories
✅Controlled training of model
✅Accuracy indicated
✅Great for scale and beckmarking
✅Can map the information to a label or
assign a plausible such, provided it has this
information in its training set
✅Very adaptive
✅Great for small projects, one offs
@lazarinastoy
www.smxl.it
❌Can’t be used for uses outside of the
main task
❌Can’t be given custom lists (...unless)
❌Requires time and data for custom
training models with AutoML
❌Non-predictable results
❌Direction might not followed
❌Model not trained for task
❌Limited knowledge
❌Unsuitable for niche industries
@lazarinastoy
www.smxl.it
What about clustering?
@lazarinastoy
www.smxl.it
Topic understanding
quickly understand what topics the keyword
universe you have consists of
Keyword clustering
quickly understand how other parameters of
keyword research relate to the clusters identified
@lazarinastoy
www.smxl.it
the goal:
see your content and links
the way search engines see it
to understand how to improve it.
@lazarinastoy
www.smxl.it
Content clusters
@lazarinastoy
www.smxl.it
Topic Models
LDA emerged to:
● remove dependency on links and introduce the “things” concept and
topic/term understanding
● introduce computational understanding of topics and terms and their
importance
● highlight the assumption that each page will have multiple different
topics or subtopics addressed, which might be of value to different
people and should be understood and surfaced in results
Needless to say, the field has evolved a lot since then!
@lazarinastoy
www.smxl.it
Topicmodelingis
patternrecognition
inlarge,text-basedcorpusesofdata.
@lazarinastoy
www.smxl.it
Introduction to Probabilistic Topic Models (Blei, 2012)
@lazarinastoy
www.smxl.it
Introduction to Probabilistic Topic Models (Blei, 2012)
@lazarinastoy
www.smxl.it
WATCH the
details LATER
I’ve recorded a step-by-step tutorial
on doing topic modelling using a
no-code, publicly-available,
web-based app using LDA.
@lazarinastoy
www.smxl.it
Identifytopics
Crawl
Export the content
from the website
Upload+finetune
Upload the files to the
web app + finetune
Download
Download all files
Explore+Build
Explore the outcome and
build your deliverable
@lazarinastoy
www.smxl.it
Topic
models
Topic models
@lazarinastoy
www.smxl.it
Topic models
Page Info
@lazarinastoy
www.smxl.it
@lazarinastoy
www.smxl.it
for less than 30 minutes:
● baseline overview of topics and relationships
● understanding of main terms that define topics
● understanding of term and topic dominance per page
What you can use this for
(literally, you’re only limited by imagination here)
● Categorise/ discover patterns and topics on site content
○ → Identify opportunities for internal linking
○ → identify what your site is about and whether it aligns with business positioning
○ → Identify the topics that your competitor site tackles
● Categorise/ discover patterns and topics on YouTube titles or video catalogs
○ → Quickly understand competitive landscape in hundreds or thousands of videos in a
niche
● Categorise/ discover patterns and topics in first-party data (any kind of user
forms)
○ → Quickly see what topics your feedback is centred upon
@lazarinastoy
www.smxl.it
KEYWORDCLUSTERINGis
SUB-topicKeywordExtraction
intext-basedDOCUMENTS.
@lazarinastoy
www.smxl.it
@lazarinastoy
www.smxl.it
@lazarinastoy
www.smxl.it
great if you want to:
● do a competitor-informed content strategy
● quickly understand the content of a massive website
@lazarinastoy
www.smxl.it
let’s talk about
fuzzy matching
@lazarinastoy
www.smxl.it
A quick and dirty way
for calculating
similarity between two strings
@lazarinastoy
www.smxl.it
Assesssimilarity
Crawl+model
Export the content from
the website
Organize
Choose what you want to
assess for similarity -
queries, anchor text, titles,
paragraphs, URLs
assess
Human-check similarity
scores. Is the output
good?
ACT
Implement or
recommend
@lazarinastoy
www.smxl.it
WATCH the
details LATER
I’ve recorded a step-by-step
tutorial on using fuzzy matching for
things like:
● Identifying link opportunities
● String Similarity Analysis
● redirect mapping of URLs
@lazarinastoy
www.smxl.it
✨ copy the template ✨
@lazarinastoy
www.smxl.it
Content Transformation
let’s talk about something a bit more advanced (but still beginner-friendly)
@lazarinastoy
www.smxl.it
Omni-presence and
Accessibility
Both users and search engines want to see
multi-modal presence for high-value sites.
Meaning:
● Text to video
● Videos to text
● Text to audio
● Audio to text
● Text summaries for longer texts (e.g. FAQs,
headings)
@lazarinastoy
www.smxl.it
Shorten ✔
Lengthen ✔
@lazarinastoy
www.smxl.it
Answer ✔
(with some
limitations)
@lazarinastoy
www.smxl.it
Multi-modal Transformation ✔
@lazarinastoy
www.smxl.it
Needless to say scaling production here is pretty
significant for organic growth.
@lazarinastoy
www.smxl.it
You have a library of videos on
YouTube but no blog?
→ Scale it’s launch quickly
through transcription.
@lazarinastoy
www.smxl.it
You have a library of
high-performing blog posts
but no presence on
YouTube/TikTok?
→Scale production with
text to speech.
@lazarinastoy
www.smxl.it
Want to optimize for
high-intent visitors or improve
accessibility of content?
→ Provide summaries of
sections, improve
headings, add FAQs, at
scale.
(...)
@lazarinastoy
www.smxl.it
half of the people in the
room might be thinking:
@lazarinastoy
www.smxl.it
“ I got this.”
@lazarinastoy
www.smxl.it
the next part of the
presentation is for those
that are thinking:
@lazarinastoy
www.smxl.it
“I could never.”
@lazarinastoy
www.smxl.it
“I am not technical
enough”
@lazarinastoy
www.smxl.it
“I don’t know enough about
ML to do these things”
@lazarinastoy
www.smxl.it
“I sucked at math in high
school, so this just goes
above my head”
@lazarinastoy
www.smxl.it
“I simply don’t have the
time to do these things”
@lazarinastoy
www.smxl.it
Recognise any of these?
Your limiting beliefs about what it takes to
implement automation and ML in your tasks
and processes might be holding you back.
@lazarinastoy
www.smxl.it
Waiting To Get Started?
Search machine learning
in 10 minutes. Follow
along.
Start small but do
something today.
Awaiting Perfect Conditions? Struggling or Tried and Failed?
Build a habit and track your
progress.
Start small and remain
consistent.
Cut scope or change direction.
Start small to get back into it.
@lazarinastoy
www.smxl.it
Or, maybe you lack
context?
@lazarinastoy
www.smxl.it
Like hearing:
Just think about things to automate.
”
@lazarinastoy
www.smxl.it
and thinking:
I don’t even
know what’s
possible
”
@lazarinastoy
www.smxl.it
@lazarinastoy
www.smxl.it
Often, there is no need to reinvent the
wheel, especially as a beginner.
@lazarinastoy
www.smxl.it
You don’t need to
know a ton of
theory.
@lazarinastoy
www.smxl.it
You don’t need
end-to-end, custom-built,
automated solutions.
@lazarinastoy
www.smxl.it
You don’t need to spend
a ton of time.
@lazarinastoy
www.smxl.it
Aim to drive value and have fun with the process.
@lazarinastoy
www.smxl.it
Knowing
● what model to use
● how to find and implement it quickly
● how to drive value via ML
is the perfect way to start.
@lazarinastoy
www.smxl.it
Let’s start with the basics.
For each potential project
consider three aspects.
@lazarinastoy
www.smxl.it
Task characteristics
@lazarinastoy
www.smxl.it
ML
Supervised
Unsupervised
Clustering
Dimensionality reduction
Regression
Classification
@lazarinastoy
www.smxl.it
ML
You have labelled data
to validate results
You don’t have a way
to validate results
Find patterns and group based on similarity
Simplify or transform your
data
Make predictions
Split into groups, based on existing classes
@lazarinastoy
www.smxl.it
@lazarinastoy
www.smxl.it
Data characteristics
@lazarinastoy
www.smxl.it
Is your input data:
Textual Numeric Image-based
@lazarinastoy
www.smxl.it
Solution characteristics
@lazarinastoy
www.smxl.it
Is this task mission
critical?
✅
❌
Don’t rely on
AI.
(seriously)
@lazarinastoy
www.smxl.it
Must the results remain
consistent every time?
✅
❌
Don’t rely on
AI.
(yeah, really)
@lazarinastoy
www.smxl.it
Must the results remain
easy to understand and
relay to the stakeholder?
✅
❌
Skip deep
learning.
@lazarinastoy
www.smxl.it
Is it okay that simply on
average the output
outperforms existing
methods?
✅
Okay, then.
Take a look at
ML options.
@lazarinastoy
www.smxl.it
Assess using multiple factors:
● insights
● complexity
● accuracy
● scalability
● assets (data in = data out)
● Resources
● bottom line
@lazarinastoy
www.smxl.it
Let’s put this into context…
@lazarinastoy
www.smxl.it
How to find suitable ML buddies (scripts,
models, tools)?
@lazarinastoy
www.smxl.it
Keep queries specific to data, task, and solution.
@lazarinastoy
www.smxl.it
A typical on-page
optimisation project
might include
mini-projects like…
@lazarinastoy
www.smxl.it
1. Writing Meta Descriptions
• Input data? Textual (page content).
• Supervised or unsupervised? Unsupervised
• It is transformational (Page content to Page Summary in less than 160
characters)
• It can also be generative (write them from scratch)
• Is it mission critical? No.
• Different results okay? Yes.
• Explanation of process needed? Not really.
• Outperforms average methods? Yes, much faster.
@lazarinastoy
www.smxl.it
Python script meta descriptions
@lazarinastoy
www.smxl.it
Python script unsupervised machine learning text summarisation
@lazarinastoy
www.smxl.it
2.Title / H1 Optimisations
• Input data? Textual (page content).
• Supervised or unsupervised? Unsupervised
• It is transformational (Page content to Page Summary in less than 60
characters)
• It can also be generative (write them from scratch)
• Is it mission critical? Hm, debatable (critical for YMYL)
• Different results okay? Again, debatable (critical for certain industries)
• Explanation of process needed? Kind of.
• Outperforms average methods? Yes, much faster. Not better.
@lazarinastoy
www.smxl.it
Python script python script title optimisation
@lazarinastoy
www.smxl.it
3. Image Alt tag Generation
• Input data? Image
• Supervised or unsupervised? Unsupervised
• Generative AI / Image recognition
• Is it mission critical? No
• Different results okay? Yes.
• Explanation of process needed? Not really.
• Outperforms average methods? Yes, much faster.
@lazarinastoy
www.smxl.it
Python script image alt text caption generation machine learning
@lazarinastoy
www.smxl.it
But might also involve…
• Predicting traffic / revenue based on presence of keyword
in the title/ h1 to get buy in on proposed changes
• Updating internal links
• Researching keywords for new content updates
• Schema implementation
@lazarinastoy
www.smxl.it
“Surely a script can’t do all that?!”
@lazarinastoy
www.smxl.it
Not yet, anyway.
@lazarinastoy
www.smxl.it
But a few can.
@lazarinastoy
www.smxl.it
@lazarinastoy
www.smxl.it
Adding value doesn’t necessarily mean a
fully-automated, autonomous solution.
@lazarinastoy
www.smxl.it
Incremental improvements can
lead to a compounding effect.
@lazarinastoy
www.smxl.it
@lazarinastoy
www.smxl.it
Thank you for listening.

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