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SEO &
STATISTICS
BUSINESS WORLD APPLICATIONS
Micah Fisher-Kirshner
Whatis
SearchEngineOptimization
The practice of increasing the quantity and quality
of traffic to your website through org...
I forge
global fame
T h e M y s t e r i o u s E x p l a n a t i o n
Powerful
Ominous
May be a double-edge sword…
How SEO Needs Data Science
Today’sGoogle is
complicated
M u l t i - a l g o r i t h m i c
Content algorithms
Link algorithms
Technical structure
With...
Algorithmson
Content
Machine learning on determining low quality sites
based on content quality patterns at scale
original...
Algorithmson
Links
Machine learning on determining patterns of
artificially bought links at scale originally needing
offli...
Algorithmson
Technical Structure
Multifactorial that understands what is an ad, what
the structures are, and what is above...
Algorithmson
UserIntents
A neural network that better understands the intent
of a search, such as the use of prepositions ...
Algorithmson
Devices
Separated out what ranking factors mattered on
desktop queries vs mobile queries
Mobilegeddon
MicahFisher-Kirshner
Work Life Personal Life
• 10+ years doing SEO
• Director of SEO & Content @
Turn/River Capital
• Pres...
Doing Good Data Science
Necessityof
good models
S t e p - b y - s t e p
Transparent
Methodological
Industry expertise
Deferential
Data collection
...
TheRight
DataCollection
Consistent Removals
• Mobile vs desktop
• Time-series vs one-time
• Source quality
• Low correlati...
Ondevice
rankings
On
temporal
effects
Factoring
Interactions
Positives Negatives
• Authority
• Branding
• Violations
• Spam
On
generating
branding
HavingaSolid
Interpretation
Reasonable Outliers
• Incredulous
• Endogeneity
• Large websites
• Esoteric subjects
On
critical
thinking
external links
attribution model
technical bug
brand validation
social shares
direct traffic
Google p...
On
real-world
situations
Defining
Categories
Groups Subsets
• Search volume
• Positional
• Query intent
• Heteroskedasticity
On
desires
On
fluctuations The best place to hide a dead body is page two of Google
Proper
Visualizations
Graphs Numbers
• Scatterplots
• Error ranges
• Regression formats
• Confidence levels
On
terminology
highly
significant
On
studies
YourWork
Reviewed
Validation Predictive
• Peer review
• Avoiding overfits
• Consistent
• Reusable
On
avoiding
chagrin
On
notdoing
harm
On
selection
bias
Difficultywith SEO Data Science
Missing
features
W o r k i n g w i t h w h a t y o u h a v e
Costly analyses
Missing tools
Large uncertainties
Company pri...
Astartingplan
y = 𝜷0 + 𝜷1x1 + 𝜷2x2 + 𝜷3x3 + 𝜷4x4 + ...
+ 𝜷5x1x2 + 𝜷6x1x3 + 𝜷7x2x3 + ...
+ 𝜷8x1x2x4 + ... + 𝜷nxn + 𝜺
Withmultipleareas
y = 𝜷0 + 𝜮𝜷2(CONTENT)
+ 𝜮𝜷3(LINKS) + 𝜮𝜷4(DOMAIN)
+ 𝜮𝜷5(STRUCTURE) + 𝜮𝜷6(UX) + 𝜺
Each havetensof factors
y = ... + 𝜷2a(exact phrase title tag) + 𝜷2b(title tag length) + 𝜷2c(order in
title tag) + 𝜷2d(bran...
On
fluctuatio
ns
Changing
of the
guard
Thankyou!
Twitter, LinkedIn, and the Internet
micahfk
SEO & Statistics Presentation by Micah Fisher-Kirshner for UC Davis Graduate Students in 2020
SEO & Statistics Presentation by Micah Fisher-Kirshner for UC Davis Graduate Students in 2020
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SEO & Statistics presentation about business world applications with how Google and SEOs use (and misuse) statistics.

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SEO & Statistics Presentation by Micah Fisher-Kirshner for UC Davis Graduate Students in 2020

  1. 1. SEO & STATISTICS BUSINESS WORLD APPLICATIONS Micah Fisher-Kirshner
  2. 2. Whatis SearchEngineOptimization The practice of increasing the quantity and quality of traffic to your website through organic search engine results via improvements in content, links, user experience, and technical site structure. The Boring Explanation
  3. 3. I forge global fame T h e M y s t e r i o u s E x p l a n a t i o n Powerful Ominous May be a double-edge sword…
  4. 4. How SEO Needs Data Science
  5. 5. Today’sGoogle is complicated M u l t i - a l g o r i t h m i c Content algorithms Link algorithms Technical structure With much more: UX, Domains, by Industries, by Intent, by Device, etc. Multifactorial Machine learning Neural networks Time-based Offline processes
  6. 6. Algorithmson Content Machine learning on determining low quality sites based on content quality patterns at scale originally need offline processes Panda
  7. 7. Algorithmson Links Machine learning on determining patterns of artificially bought links at scale originally needing offline processes Penguin
  8. 8. Algorithmson Technical Structure Multifactorial that understands what is an ad, what the structures are, and what is above the fold, penalizing anything with too much ad space Page Layout (Ads)
  9. 9. Algorithmson UserIntents A neural network that better understands the intent of a search, such as the use of prepositions like “to” in a phrase BERT
  10. 10. Algorithmson Devices Separated out what ranking factors mattered on desktop queries vs mobile queries Mobilegeddon
  11. 11. MicahFisher-Kirshner Work Life Personal Life • 10+ years doing SEO • Director of SEO & Content @ Turn/River Capital • President & Founder @ BayAreaSearch.org • Econometrics background • Modern nerd • High school buddy of your professor (unfortunately)
  12. 12. Doing Good Data Science
  13. 13. Necessityof good models S t e p - b y - s t e p Transparent Methodological Industry expertise Deferential Data collection Interaction effects Interpretations Categories Visualizations Reviews
  14. 14. TheRight DataCollection Consistent Removals • Mobile vs desktop • Time-series vs one-time • Source quality • Low correlations
  15. 15. Ondevice rankings
  16. 16. On temporal effects
  17. 17. Factoring Interactions Positives Negatives • Authority • Branding • Violations • Spam
  18. 18. On generating branding
  19. 19. HavingaSolid Interpretation Reasonable Outliers • Incredulous • Endogeneity • Large websites • Esoteric subjects
  20. 20. On critical thinking external links attribution model technical bug brand validation social shares direct traffic Google penalty buying ads social shares direct traffic Google penalty buying ads
  21. 21. On real-world situations
  22. 22. Defining Categories Groups Subsets • Search volume • Positional • Query intent • Heteroskedasticity
  23. 23. On desires
  24. 24. On fluctuations The best place to hide a dead body is page two of Google
  25. 25. Proper Visualizations Graphs Numbers • Scatterplots • Error ranges • Regression formats • Confidence levels
  26. 26. On terminology highly significant
  27. 27. On studies
  28. 28. YourWork Reviewed Validation Predictive • Peer review • Avoiding overfits • Consistent • Reusable
  29. 29. On avoiding chagrin
  30. 30. On notdoing harm
  31. 31. On selection bias
  32. 32. Difficultywith SEO Data Science
  33. 33. Missing features W o r k i n g w i t h w h a t y o u h a v e Costly analyses Missing tools Large uncertainties Company priorities Need to be heard more? Need more enticing copy? Need to improve what is important? Need clarity in what you do?
  34. 34. Astartingplan y = 𝜷0 + 𝜷1x1 + 𝜷2x2 + 𝜷3x3 + 𝜷4x4 + ... + 𝜷5x1x2 + 𝜷6x1x3 + 𝜷7x2x3 + ... + 𝜷8x1x2x4 + ... + 𝜷nxn + 𝜺
  35. 35. Withmultipleareas y = 𝜷0 + 𝜮𝜷2(CONTENT) + 𝜮𝜷3(LINKS) + 𝜮𝜷4(DOMAIN) + 𝜮𝜷5(STRUCTURE) + 𝜮𝜷6(UX) + 𝜺
  36. 36. Each havetensof factors y = ... + 𝜷2a(exact phrase title tag) + 𝜷2b(title tag length) + 𝜷2c(order in title tag) + 𝜷2d(brand name used) + 𝜷2e(title tag relevance) + 𝜷2f(exact phrase h1) + 𝜷2g(exact phrase largest font) + 𝜷2h(h1 relevance) + 𝜷2i(largest font relevance) + 𝜷2j(exact phrase body copy) + 𝜷2k(BM25 score) + 𝜷2l(Flesch-Kincaid readability) + 𝜷2m(exact phrase first 100 words) + 𝜷2n(exact phrase URL) + 𝜷2o(URL relevance) + 𝜷2p(exact phrase title tag * order in title tag) + 𝜷2q(exact phrase title tag * largest font * URL) + 𝜷2r(exact phrase title tag * BM25 score) + ... + 𝜺
  37. 37. On fluctuatio ns
  38. 38. Changing of the guard
  39. 39. Thankyou! Twitter, LinkedIn, and the Internet micahfk

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