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Ash Nallawalla Presentation at Pubcon 2015
- 1. #pubcon http://ash.nallawalla.com @ashnallawalla Lateral Keywords for Writers (When the Google Keyword Planner isn’t enough) Presented by: Ash Nallawalla SEO Strategist, Suncorp Insurance
- 2. #pubcon http://ash.nallawalla.com @ashnallawalla 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 2
- 4. #pubcon http://ash.nallawalla.com @ashnallawalla 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. 4
- 5. #pubcon http://ash.nallawalla.com @ashnallawalla Search volume alone isn’t enough • But we need a starting point. 5
- 6. #pubcon http://ash.nallawalla.com @ashnallawalla Give researched keywords to writers • A keyword matrix ensures a good spread of keywords across the site and saves the writer from guessing keywords. 6
- 7. #pubcon http://ash.nallawalla.com @ashnallawalla Search intent is important • Intent can be Navigational, Informational, Commercial, Transactional. • Yes, check out some tools. 7
- 8. #pubcon http://ash.nallawalla.com @ashnallawalla CHECK OUT THE COMPETITION So who is winning in your niche?
- 9. #pubcon http://ash.nallawalla.com @ashnallawalla 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. 9
- 10. #pubcon http://ash.nallawalla.com @ashnallawalla Count ranking keywords • First get the count of keywords that rank. 10
- 11. #pubcon http://ash.nallawalla.com @ashnallawalla Derive the mean position • Get the “average” position for each company. 11
- 12. #pubcon http://ash.nallawalla.com @ashnallawalla Invert the mean position • “Inverting” means deducting the rank from 10, so that a higher number denotes a higher rank. 12
- 13. #pubcon http://ash.nallawalla.com @ashnallawalla Derive a “score” • Score (say Allianz) = (C3*C5)+(D3*D5) • Score = (6.1x21)+(4.0x3) = 141 13
- 14. #pubcon http://ash.nallawalla.com @ashnallawalla Ranking spreadsheet • “Visibility” is important, but what is your way to measure it? • Which competitor is more visible? 14
- 15. #pubcon http://ash.nallawalla.com @ashnallawalla 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!) 15
- 16. #pubcon http://ash.nallawalla.com @ashnallawalla DEEP DIVE – TERM FREQUENCY Looking for that lightbulb moment?
- 17. #pubcon http://ash.nallawalla.com @ashnallawalla 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) 17
- 18. #pubcon http://ash.nallawalla.com @ashnallawalla 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. 18
- 19. #pubcon http://ash.nallawalla.com @ashnallawalla 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 19
- 20. #pubcon http://ash.nallawalla.com @ashnallawalla 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 20
- 21. #pubcon http://ash.nallawalla.com @ashnallawalla Getting back to Term Frequency… 21 • 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).
- 22. #pubcon http://ash.nallawalla.com @ashnallawalla 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. 22
- 23. #pubcon http://ash.nallawalla.com @ashnallawalla TF – one worksheet per keyword • Six sets of n-grams on the left and de-duped list in grey zone. 23 Eric’s spreadsheet
- 24. #pubcon http://ash.nallawalla.com @ashnallawalla TF – close-up • Get a count of each word or phrase used by the top five pages and by yours 24
- 25. #pubcon http://ash.nallawalla.com @ashnallawalla 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 25
- 26. #pubcon http://ash.nallawalla.com @ashnallawalla 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. 26
- 27. #pubcon http://ash.nallawalla.com @ashnallawalla The extracted words • The pages I was analysing did not contain some “obvious” words – this is the beauty of this technique. 27
- 28. #pubcon http://ash.nallawalla.com @ashnallawalla The future? • Working on a web version • Takes minutes, not hours. 28 Sliders Gems Beta: http://www.lateralkeywords.com
- 29. #pubcon http://ash.nallawalla.com @ashnallawalla 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 29 Ash Nallawalla • Twitter: @ashnallawalla • Email: ash@nallawalla.com • Web: http://ash.nallawalla.com