This document discusses lateral keyword research techniques for content writers. It recommends going beyond basic keyword search volume data and analyzing how competitors are ranking for keywords. The document teaches analyzing a topic's top ranking pages to see which keywords they use most frequently using term frequency analysis and inverse document frequency. This reveals underutilized keywords a writer could consider to make content more visible to search engines.

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Lateral Keywords for Writers
(When the Google Keyword Planner isn’t enough)
Presented by: Ash Nallawalla
SEO Strategist, Suncorp Insurance

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About Ash
• SEO consultant, currently at
Suncorp Insurance (eight
brands)
• Moderator at
Webmasterworld forums
• Previously in enterprise SEO
roles, notably NAB, ANZ
Bank, Ubank, Optus and
Yellow Pages
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KEYWORD RESEARCH BASICS
No longer enough

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Everybody’s doing it
• Most of us use the Google Keyword Planner to get a
feel for the most searched terms.
• Our competitors do that too.
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Search volume alone isn’t enough
• But we need a starting point.
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Give researched keywords to writers
• A keyword matrix ensures a good spread of
keywords across the site and saves the writer from
guessing keywords.
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Search intent is important
• Intent can be Navigational, Informational,
Commercial, Transactional.
• Yes, check out some tools.
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CHECK OUT THE COMPETITION
So who is winning in your niche?

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Start with a ranking check
• Use your preferred rank-checking tool to see who is
ranking for each keyword.
• We want to check which company’s content is
consistently coming up on Page 1 for a number of
similar keywords.
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Count ranking keywords
• First get the count of keywords that rank.
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Derive the mean position
• Get the “average” position for each company.
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Invert the mean position
• “Inverting” means deducting the rank from 10, so that
a higher number denotes a higher rank.
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Derive a “score”
• Score (say Allianz) = (C3*C5)+(D3*D5)
• Score = (6.1x21)+(4.0x3) = 141
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Ranking spreadsheet
• “Visibility” is important, but what is your way to
measure it?
• Which competitor is more visible?
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The content writer’s dilemma
• The spreadsheet shows the
“winners”, not the “losers”. We
can see who is using the most
searched phrases.
• Others are using the same tools.
• So what content are they using
that you are not using?
• (Note: Ranking involves many other factors
and this is also about Selling!)
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DEEP DIVE – TERM FREQUENCY
Looking for that lightbulb moment?

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Hat Tip to Eric Enge
• See his articles in Moz:
– Just Google “Eric Enge TF-IDF” for the URLs.
(click the image below if you have the PPT)
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Inverse Document Frequency
• Inverse Document Frequency – a measure of the
“rareness” of a term, so we weigh down the stop
words and scale up the rare ones.
𝐼𝐷𝐹(𝑡) = log[
𝐶𝑜𝑢𝑛𝑡 𝑜𝑓 𝑡𝑒𝑟𝑚 𝑡 𝑜𝑛 𝑎 𝑝𝑎𝑔𝑒
𝑇𝑜𝑡𝑎𝑙 𝑐𝑜𝑢𝑛𝑡 𝑜𝑓 𝑡𝑒𝑟𝑚𝑠 𝑜𝑛 𝑎 𝑝𝑎𝑔𝑒
]
• Refer to Eric’s second article for more details.
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TF-IDF example
• Say, a document with 100 words contains the term
“cat” 3 times.
• The TF is 3/100 x 0.5 + 0.5 = 0.515
• Google has, say, 30 trillion pages and the word “cat”
appears in 1.7 billion pages.
• The IDF is log(30,000,000,000,000 /1,700,000,000)
or log(730,2.718281828) = 6.593044535
• The TF-IDF (or TF*IDF) weight is the product of:
0.515 x 6.593044535 = 3.395417935
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Term Frequency – Two ways to measure
• Term Frequency – how frequently the term appears
in a document (incl. stop words)
𝑻𝑭(𝒕) = 𝟎. 𝟓 + 𝟎. 𝟓 ∗
𝑹𝒂𝒘 𝑻𝒆𝒓𝒎 𝑪𝒐𝒖𝒏𝒕
𝑪𝒐𝒖𝒏𝒕 𝒇𝒐𝒓 𝑴𝒐𝒔𝒕 𝑭𝒓𝒆𝒒𝒖𝒆𝒏𝒕 𝑻𝒆𝒓𝒎 𝒐𝒏 𝑷𝒂𝒈𝒆
Or
If Raw Term Count > 0, TF = 1+log10(Raw Term Count)
If Raw Term Count = 0, TF = 0
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Getting back to Term Frequency…
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• Search for your keyword.
• Visit the page/s of the highest
ranking company and the next
four top rankers.
• Note their URLs.
• Note the URL of your own page.
• Do a Term Frequency analysis
and, perhaps
Inverse Document Frequency
analysis (TF-IDF).

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Get n-grams
• Use one of the old “keyword density” tools to get 1-
word, 2-word, and 3-word pairs from your site and the
five competitors.
• Collate, de-dupe n-grams and place in Eric’s
spreadsheet.
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TF – one worksheet per keyword
• Six sets of n-grams on the left and de-duped list in
grey zone.
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Eric’s spreadsheet

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TF – close-up
• Get a count of each word or phrase used by the top
five pages and by yours
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TF – close-up
• Next, do the TF number crunching, i.e.
𝑇𝐹(𝑡) = 0.5 + 0.5 ∗
𝑅𝑎𝑤 𝑇𝑒𝑟𝑚 𝐶𝑜𝑢𝑛𝑡
𝐶𝑜𝑢𝑛𝑡 𝑓𝑜𝑟 𝑀𝑜𝑠𝑡 𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑡 𝑇𝑒𝑟𝑚 𝑜𝑛 𝑃𝑎𝑔𝑒
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TF – close-up
• Use conditional formatting to pick a range of TF values
and compare your TF column with the average TF of
the competitors.
• You will now see “significant” words to consider.
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The extracted words
• The pages I was analysing did not contain some
“obvious” words – this is the beauty of this technique.
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The future?
• Working on a web
version
• Takes minutes, not
hours.
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Sliders
Gems
Beta: http://www.lateralkeywords.com

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Summary
• Keyword research requires more than the Google
tool. Do lateral keyword research.
• Do consider Term Frequency at least. Also look into
Inverse Document Frequency.
• Download full PPT from:
http://www.trainsem.com/pubcon
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Ash Nallawalla
• Twitter: @ashnallawalla
• Email: ash@nallawalla.com
• Web: http://ash.nallawalla.com