scary serps (and keyword creep) #brightonseo
TRANSCRIPT
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Kelvin NewmanFounder - BrightonSEO
Scary SERPs (and keyword creep)
@kelvinnewman http://www.slideshare.net/kelvinnewman
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@kelvinnewman
So, who is this guy?
@kelvinnewman
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Founder of BrightonSEO
@kelvinnewman
http://www.slideshare.net/kelvinnewman
Kelvin Newman
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@kelvinnewman
Shhhhh, but I don’t live in Brighton
@kelvinnewman
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@kelvinnewman
Actually; I live in Worthing
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@kelvinnewman
Which is famous for one thing…
@kelvinnewman
Birdman Competition where people see how far they can jump of the pier
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@kelvinnewman
So what is this presentation about?
@kelvinnewman
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@kelvinnewman
Well, this presentation isn’t about…
@kelvinnewman
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@kelvinnewman
So, if you use the Google Keyword Planner you can find
some excellent keywords
““
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@kelvinnewman
It’s more about getting you to think differently about the future
of keywords.
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@kelvinnewman
One True AnswerPart 1
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@kelvinnewman
Start with a story
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@kelvinnewman
Peter A. Shulman
Historian of sci, tech, and American politics. Author of Coal & Empire.
Associate Professor of History
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Lecturing on the reemergence of the Ku Klux
Klan in the 1920s when a student asked an odd
question:
@kelvinnewman
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Was President Warren Harding a member of the KKK?
@kelvinnewman
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@kelvinnewman
He wasn’t
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@kelvinnewman
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@kelvinnewman
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@kelvinnewman
This isn’t an isolated occurrence
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https://theoutline.com/post/1192/google-s-featured-snippets-are-worse-than-fake-news
@kelvinnewman
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https://theoutline.com/post/1192/google-s-featured-snippets-are-worse-than-fake-news
@kelvinnewman
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http://searchengineland.com/googles-one-true-answer-problem-featured-snippets-270549
@kelvinnewman
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@kelvinnewman
Bad on desktop, terrible on voice
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https://theoutline.com/post/1192/google-s-featured-snippets-are-worse-than-fake-news
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@kelvinnewman
Easy to mock but what can we learn from this
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Preference for “one true answer”
http://searchengineland.com/googles-one-true-answer-problem-featured-snippets-270549
@kelvinnewman
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Risk for Google but opportunity for us
@kelvinnewman
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@kelvinnewman
End of Keyword PrecisionPart 2
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@kelvinnewman
Not-Bloody-Provided
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@kelvinnewman
Keyword Bloody Planner Estimates
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@kelvinnewman
Close-Bloody-Variants
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@kelvinnewman
Query Re-writing
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@kelvinnewman
It would seem Machine Learning is involved in Query Re-writing
@kelvinnewman
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@kelvinnewman
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@kelvinnewman
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@kelvinnewman
Machine learning is sexy
And Artificial Intelligence, Deep Learning and other sort of related things along those lines.
@kelvinnewman
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@kelvinnewman
I studied media studies at Uni not computer science or anything like it…
Now feels like a good time to share
@kelvinnewman
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@kelvinnewman
Machine Learning Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction
about something in the world.
So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using
large amounts of data and algorithms that give it the ability to learn how to perform the task.
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@kelvinnewman
Deep Learning A branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data by using a deep graph with multiple
processing layers, composed of multiple linear and non-linear transformations.
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Seeing the connections
below the surface
@kelvinnewman
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@kelvinnewman
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@kelvinnewman
So what are the actions?
@kelvinnewman
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@kelvinnewman
Use some off the shelf
options
@kelvinnewman
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https://algorithmia.com/algorithms/nlp/Word2Vec
@kelvinnewman
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@kelvinnewman
Tutorials on use Machine Learning on over 1M hotel
reviews finds interesting insights
https://blog.monkeylearn.com/machine-learning-1m-hotel-reviews-finds-interesting-insights
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Or wait for SEO tool suites to start doing this properly
@kelvinnewman
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Easiest solution is to know the space and know your
customers
@kelvinnewman
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But just writing for users is a lazy suggestion.
We can better understand our users needs if we can better understand how other websites are writing about a topic
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@kelvinnewman
To optimise a page now you need more than add keyphrases.
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@kelvinnewman
You need to have all the phrases and words they’d expect
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Is the search query on the page and does deserve to rank?
Old Model
@kelvinnewman
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Does it contain the search query and phrases used be other pages that rank for the term and does deserve to rank?
New Model
@kelvinnewman
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@kelvinnewman
Two hacky & clunky way of seeing those you’d need
include.
Method 1
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@kelvinnewman
Take the top ten results for your query and extract the text using
something like textise.net
Method 1
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@kelvinnewman
Bung the copy from all the pages into a Word Cloud
Tool
I like jasondavies.com/wordcloud/
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@kelvinnewman
Treat the most common words like bingo
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@kelvinnewman
Take the top 3 results for your query and extract the text using
something like textise.net
Method 2
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@kelvinnewman
Create a list of all the single words used on the page
using something like writewords.org.uk/
word_count.asp
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@kelvinnewman
Create a Venn Diagram of the overlap
Method 2
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@kelvinnewman
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@kelvinnewman
http://www.slideshare.net/kelvinnewman
Thanks