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

3. #pubcon http://ash.nallawalla.com @ashnallawalla KEYWORD RESEARCH BASICS No longer enough

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 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 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 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 http://www.trainsem.com/pubcon 29 Ash Nallawalla Twitter: @ashnallawalla Email: ash@nallawalla.com Web: http://ash.nallawalla.comhttp://ash.nallawalla.com