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# 2011 12 ECMOD360 Writing for Readers and Search Bots

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Writing for Readers and Search Bots: What to Write and How to Write to It

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• We’re the world’s most popular provider of search marketing software, covering everything from technical SEO issues to social media monitoring, We serve SMBs to Fortune 100 enterprises with actionable information, community support, tools and monitoring platform to make search marketing faster, easier, and more effective. We don’t do any consulting.
• The search engine can use TF*IDF to determine that “ Wiggum ” is a much less common word than “ chief ” and thus, Content A is more relevant to the query than Content B. NOTE: This example also does a good job of showing the inherent weakness of a metric like keyword density.
• As humans reading both sentences, we can infer that Content B is obviously about the musical instrument – a piano – and the woman playing it. But a search engine armed with only the methods we described above will struggle since both sentences use the words “keys” and “notes”, some of the few clues to the puzzle.
• Term vector spaces, topic modeling and cosine similarity sound like a tough concepts. However, Ben (along with Rand Fishkin and Will Critchlow , whose Cambridge mathematics degree came in handy) helped explain these issues. I&apos;ll do my best to replicate that here:
• In this imaginary example, every word in the English language is related to either &amp;quot; cat &amp;quot; or &amp;quot; dog “. They are the only topics available. To measure whether a word is more related to &amp;quot;dog,&amp;quot; we use a vector space model that displays those relationships mathematically. The illustration does a reasonable job showing our simplistic world. Words like &amp;quot;bigfoot&amp;quot; are perfectly in the middle with no more closeness to &amp;quot;cat&amp;quot; than &amp;quot;dog.&amp;quot; But words like &amp;quot;canine&amp;quot; and &amp;quot;feline&amp;quot; are clearly closer to one that the other and the degree of the angle in the vector model illustrates this- and gives us a number. BTW, in an LDA vector space model, topics wouldn&apos;t have exact label associations like &amp;quot;dog&amp;quot; and &amp;quot;cat&amp;quot; but would instead be things like &amp;quot;the vector around the topic of dogs.“ Taking the simple model above and scaling it to thousands or millions of topics, each of which would have its own dimension. Using this construct, the model can compute the similarity between any word or groups of words and the topics its created. You can learn more about this from Stanford University&apos;s posting of Introduction to Information Retrieval , &lt;http://nlp.stanford.edu/IR-book/html/htmledition/irbook.html&gt; which has a specific section on Vector Space Models &lt;http://nlp.stanford.edu/IR-book/html/htmledition/dot-products-1.html&gt;
• Correlation of our LDA Results w/ Google.com Rankings The SEOmoz team have put together a topic modeling system based on a relatively simple implementation of LDA.
• Vertical bars indicate Standard Error in the diagram. SE is relatively low thanks to a large sample set.
• Are good links are more likely to point more &amp;quot;relevant” pages? Do other aspects of Google&apos;s algorithm naturally bias toward these results? Correlation is NOT causation!
• Let’s review some of the basic tenets that Google reps continue to stress to search marketers – once you’ve chosen a keyword with the highest value / lowest competition quotient, it takes more than simple frequency of use to rank well for that word.
• The recent Panda updates addressed a number of spam issues in an attempt to provide a better result to searchers.
• This snippet of an episode from Alan Sorkin’s The West Wing exquisitely demonstrates what I’d love to convey.
• The variety of places from which we get traffic today helps us segment our audience by interest, online behavioral habits, and comfort zones. People who spend a great deal of time in Facebook are comfortable with its interface and layout. If you’re creating content for a landing page for folks coming from Facebook, consider a layout and post(s) that will be a good segue between their Facebook experience and your site. Same goes for Twitter, YouTube, and other social network visitors.
• Highly targeted infographics are the new link bait. Because we have an exponentially increasing volume of information to absorb, we need better ways to absorb it faster and more efficiently. Info-graphics aren’t a flash in the pan.
• There’s no short cut for making info-graphics go viral. Great graphics make it happen. Invest in a decent info designer and keep him/her close.
• Image, news, maps, video… there’s more to search than the general SERPs.
• Image, news, maps, video… there’s more to search than the general SERPs.
• Image, news, maps, video… there’s more to search than the general SERPs.
• Image, news, maps, video… there’s more to search than the general SERPs.
• Who needs position 1 when video click through rates exceed that position, even when located in position 3 or 4?
• Who needs position 1 when video click through rates exceed that position, even when located in position 3 or 4?
• Q&amp;A sites can bring significant direct traffic, establish you as an authority in your field, and invite news media to contact you when they need expert info or opinions.
• User generated content can help you create consistently fresh, unique content. Careful moderation and high standards will maintain the quality standards you need.
• SEOmoz’ YOUmoz is a place for our community to share info.
• Social bookmarking websites also spread the word.
• ### 2011 12 ECMOD360 Writing for Readers and Search Bots

1. 1. On Beyond Keywords Writing to Delight People and Search Bots Gillian Muessig | Founding President http://downloadurl
2. 2. World’s most popular provider of search marketing software
3. 3. www.seomoz.org/signup code: ECMOD2011 I come bearing gifts
4. 4. Oh Yes! Congratulations Season-to-date online spend: \$ 13B UP 15% Black Friday Online spend: \$816MM UP 26% Thanksgiving Day Online Sales: \$479MM UP 18%
5. 5. Today’s Menu Topic modeling Vector space models Latent Dirichlet Allocation & the SERPs Tactical Advice
6. 6. Search Ranking Factors 2009
7. 7. Search Ranking Factors 2011 Link Signals LanguageSignals
8. 8. Topic Modeling Topic models provide a simple way to analyze large volumes of unlabeled text.
9. 9. Sub Section Heading goes here Notes on this slide, possibly including a link reference or two
10. 10. Topic Modeling http://neoformix.com/archive.html Topic: A cluster of words that frequently occur together.
11. 11. What Topic Modeling Does Topic modeling uses contextual clues to connect words w/similar meanings and distinguish between uses of words w/multiple meanings. http://www.stanford.edu/~kaisa/research.html
12. 12. Keyword Usage
13. 13. TF | IDF Term Frequency | Inverse Document Frequency/: this example shows the inherent weakness of the keyword density metric.
14. 14. Co-Occurance
15. 15. Topic Modeling
16. 16. Rock or Baseball? Content-related signals require the ability to determine INTENT
17. 17. If your response is…
18. 18. Simplistic Term Vector Model
19. 19. Correlation is Strong
20. 20. Deviation is Small Standard Deviation
21. 21. Causation is not Correlation Good links may point to more relevant page; other algo aspects may be at play
22. 22. Out of the SERPs! Keyword spamming does not improve your chances of ranking!
23. 23. What to Write & How to Write It Tips for Building a Keyword List
24. 24. Ask Someone Who Knows
25. 25. Sales People & Customers
29. 29. Internal Site Search Stats
30. 30. Competitive Keyword Research Restrict query to your competitor’s domain
31. 31. How Tough Is It to Rank for That?
32. 32. Choose “ Best ” Words to Target
33. 33. Search Demand Curve Determine your most valuable long tail key phrases
34. 34. Conversion Rate by KW Length Start by optimizing for the valuable 4 & 3 word key phrases
35. 35. Generic Hot Topics These subjects get picked up faster than articles on latent dirichlet allocation Money | Health | Family
36. 36. Ephermal Hot Topics These subjects get picked up faster than articles on latent dirichlet allocation News | Celebrities | Finance
37. 37. What to Write & How to Write It Tips for Writing Copy
38. 38. It’s More Than KW Frequency These subjects get picked up faster than articles on latent dirichlet allocation
39. 39. QDF: New Meaning to Freshness
40. 40. Query Deserves Diversity Even a search for ‘best gifts 2011’ yields some diversity in results
41. 41. Query Deserves Originality Avoid duplicated content with clear rel=author & rel-canonical tags. http://www.blindfiveyearold.com/how-to-implement-rel-author
42. 42. Query Deserves Quality http://googleblog.blogspot.com/2011/02/finding-more-high-quality-sites-in.html
43. 43. Say it beautifully http://www.youtube.com/watch?v=Uh4DGUNWmiU
44. 44. Content is more than words Content with images is better content – infor is absorbed better than words alone
45. 45. Good Content with images is better content – infor is absorbed better than words alone
46. 46. Better Tag, transcribe, include multiple ways to get your point across
47. 47. Write For Your Audience http://www.seomoz.org/blog/the-rich-get-richer-true-in-seo-social-all-organic-marketing Blogs + Blogging Comment Marketing News/Media/PR SEO Social Networks Word of Mouth Q+A Sites Forums Online Video Podcasting Webinars Research/White Papers Infographics Social Bookmarking INBOUND MARKETING! (AKA “free” traffic sources) Direct/Referring Links Type-In Traffic Email
48. 48. The New Link Bait http://guides.seomoz.org/beginners-guide-to-search-engine-optimization
49. 49. Great Graphics http://guides.seomoz.org/beginners-guide-to-search-engine-optimization
50. 50. Graphics That Worked for Us http://guides.seomoz.org/beginners-guide-to-search-engine-optimization
51. 51. Drawing Traffic to Your Now-Great Content 5 Top Tips
52. 52. Comment Generously http://www.seomoz.org/blog/recommendations-blog-commenting-marketing-strategy 5
53. 53. Exploit Vertical Search 4
54. 54. Local Results Convert Better 4
55. 55. Image Results in the SERPs 4
56. 56. Video Results 4
57. 57. Video Results 4
58. 58. Shopping Results 4
59. 59. Share Your Expertise 3
60. 60. Incented Sharing Community 2
61. 61. Incented Sharing Community 2
62. 62. Spread the Word Everywhere 1
63. 63. Spread the Word Everywhere 1 http://www.ebizmba.com/articles/social-bookmarking-websites
64. 64. @SEOmom www.seomoz.org/blog [email_address] Gillian on the web http://www.slideshare.net
65. 65. www.seomoz.org/signup ECMOD2011