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kevin Indig - Internal Link Building on Steroids (Tech SEO Boost )

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Kevin Indig | @Kevin_Indig | #TechSEOBoost
Kevin Indig

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Kevin Indig | @Kevin_Indig | #TechSEOBoost
Kevin Indig
Internal link building on steroids
… or how to flatten power curves

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Kevin Indig | @Kevin_Indig | #TechSEOBoost

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kevin Indig - Internal Link Building on Steroids (Tech SEO Boost )

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SEO for large sites is completely different than SEO for smaller sites. Large sites have a strong (yet often overlooked!) lever that can boost rankings: internal linking! However, it can be challenging to understand which pages have the highest PageRank, so that you can tweak them to serve important pages better. That can only be determined when you combine internal and external PageRank. Join Kevin Indig as he presents an innovative approach that merges data from crawls, log files, and backlinks to solve the puzzle! You’ll learn how to:
· Combine crawls, log files, and backlinks to find weaknesses in your internal linking structure.
· Analyze the impact of tweaking internal linking before you deploy the changes.
· Understand how to tweak internal linking at scale.

SEO for large sites is completely different than SEO for smaller sites. Large sites have a strong (yet often overlooked!) lever that can boost rankings: internal linking! However, it can be challenging to understand which pages have the highest PageRank, so that you can tweak them to serve important pages better. That can only be determined when you combine internal and external PageRank. Join Kevin Indig as he presents an innovative approach that merges data from crawls, log files, and backlinks to solve the puzzle! You’ll learn how to:
· Combine crawls, log files, and backlinks to find weaknesses in your internal linking structure.
· Analyze the impact of tweaking internal linking before you deploy the changes.
· Understand how to tweak internal linking at scale.

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kevin Indig - Internal Link Building on Steroids (Tech SEO Boost )

  1. 1. Kevin Indig | @Kevin_Indig | #TechSEOBoost Kevin Indig
  2. 2. Kevin Indig | @Kevin_Indig | #TechSEOBoost Kevin Indig Internal link building on steroids … or how to flatten power curves
  3. 3. Kevin Indig | @Kevin_Indig | #TechSEOBoost
  4. 4. Kevin Indig | @Kevin_Indig | #TechSEOBoost Power curves 101 - Vilfredo Pareto: 80/20 - People + riches - Factors + impact - Startups + VC returns
  5. 5. Kevin Indig | @Kevin_Indig | #TechSEOBoost 0 50,000 100,000 150,000 200,000 250,000 300,000 1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139 145 151 157 163 169 175 181 187 193 199 205 211 217 223 229 235 241 247 253 259 265 271 277 283 289 295 301 307 313 319 Clicks Number of URLs KEYWORD CLICK CURVE
  6. 6. Kevin Indig | @Kevin_Indig | #TechSEOBoost 0 100 200 300 400 500 600 1 12 23 34 45 56 67 78 89 100 111 122 133 144 155 166 177 188 199 210 221 232 243 254 265 276 287 298 309 320 331 342 353 364 375 386 397 408 419 430 441 452 463 474 485 496 507 518 529 540 551 562 573 584 595 606 617 628 639 650 661 672 683 694 705 716 Linkingdomains Number of URLs DOMAIN POP DISTRIBUTION
  7. 7. Kevin Indig | @Kevin_Indig | #TechSEOBoost 0 50 100 150 200 250 1 67 133 199 265 331 397 463 529 595 661 727 793 859 925 991 1057 1123 1189 1255 1321 1387 1453 1519 1585 1651 1717 1783 1849 1915 1981 2047 2113 2179 2245 2311 2377 2443 2509 2575 2641 2707 2773 2839 2905 2971 3037 3103 3169 3235 3301 3367 3433 3499 3565 3631 3697 3763 3829 3895 3961 4027 4093 4159 4225 4291 Monthlycrawls Number of URLs CRAWL RATE DISTRIBUTION
  8. 8. Kevin Indig | @Kevin_Indig | #TechSEOBoost Why is that important?
  9. 9. Kevin Indig | @Kevin_Indig | #TechSEOBoost SEO is not getting easier
  10. 10. Kevin Indig | @Kevin_Indig | #TechSEOBoost Internal linking = one of the strongest levers
  11. 11. Kevin Indig | @Kevin_Indig | #TechSEOBoost “Use the Powa of Indernal Lings”
  12. 12. Kevin Indig | @Kevin_Indig | #TechSEOBoost www.kevin-indig.com Tech SEO Lead @ Atlassian Mentor @ German Accelerator
  13. 13. Kevin Indig | @Kevin_Indig | #TechSEOBoost Q: How can we optimize internal linking?
  14. 14. Kevin Indig | @Kevin_Indig | #TechSEOBoost Crawl + calculate internal PR
  15. 15. Kevin Indig | @Kevin_Indig | #TechSEOBoost Use tools to get recommendations
  16. 16. Kevin Indig | @Kevin_Indig | #TechSEOBoost Problem: Most internal link models are inaccurate!
  17. 17. Kevin Indig | @Kevin_Indig | #TechSEOBoost PageRank exists between and within sites
  18. 18. Kevin Indig | @Kevin_Indig | #TechSEOBoost Internal PageRank is only half of the equation Page A Page B Page C Page D Site A
  19. 19. Kevin Indig | @Kevin_Indig | #TechSEOBoost External PageRank is the other side of the equation Page A Page B Page C Page D Site A Site B Page A Page B Page C Page D
  20. 20. Kevin Indig | @Kevin_Indig | #TechSEOBoost What we need is a model that combines internal and external PR
  21. 21. Kevin Indig | @Kevin_Indig | #TechSEOBoost Solution: the “True Internal PR” model (TIPR) CheiRank Backlinks Log files TIPR PageRank
  22. 22. Kevin Indig | @Kevin_Indig | #TechSEOBoost What can you do with TIPR? Calculate “accurate” internal PageRank Identify technical problems Monitor optimization progress
  23. 23. Kevin Indig | @Kevin_Indig | #TechSEOBoost The TIPR process Analysis Recommendations Monitoring
  24. 24. Kevin Indig | @Kevin_Indig | #TechSEOBoost TIPR – step by step 1. Crawl site 2. Calculate internal PR and CR 3. Add backlinks to get “true internal PR” 4. Add crawl rate from log files to understand impact of (internal + external) links over time 5. Sort and rank metrics 6. Optimize for Money Maker Pages
  25. 25. Kevin Indig | @Kevin_Indig | #TechSEOBoost “Robin Hood” principle: take from the rich, give to the poor
  26. 26. Kevin Indig | @Kevin_Indig | #TechSEOBoost Let’s talk some results
  27. 27. Kevin Indig | @Kevin_Indig | #TechSEOBoost Dry testing the model at small scale
  28. 28. Kevin Indig | @Kevin_Indig | #TechSEOBoost
  29. 29. Kevin Indig | @Kevin_Indig | #TechSEOBoost
  30. 30. Kevin Indig | @Kevin_Indig | #TechSEOBoost Crawl to find PR and CR
  31. 31. Kevin Indig | @Kevin_Indig | #TechSEOBoost Extract backlinks
  32. 32. Kevin Indig | @Kevin_Indig | #TechSEOBoost Exported server log files and tracked keywords
  33. 33. Kevin Indig | @Kevin_Indig | #TechSEOBoost 1710 1033 96 3 1 0 200 400 600 800 1000 1200 1400 1600 1800 1 5 20 50 100 NumberofURLs Crawl frequency CRAWL FREQUENCY DISTRIBUTION
  34. 34. Kevin Indig | @Kevin_Indig | #TechSEOBoost 1710 1033 96 3 1 0 200 400 600 800 1000 1200 1400 1600 1800 1 5 20 50 100 NumberofURLs Crawl frequency CRAWL FREQUENCY DISTRIBUTION Guess who this lone fella is?
  35. 35. Kevin Indig | @Kevin_Indig | #TechSEOBoost 19 2 16 401 519 584 921 172 289 10 4129 10 69 11 99 6 222120 2 60 115 150 41 17 25 30 69331211254 39 311253341 30 211212124334122 30 111111111111141111112111111111111111111111111111111111112131111111112221112115234111 0 100 200 300 400 500 600 700 800 900 1000 0 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 48 50 52 54 56 58 61 63 65 70 72 75 79 81 83 86 88 91 94 101 108 110 114 120 126 131 134 143 154 161 180 190 200 216 237 274 290 319 323 326 331 335 344 352 369 375 377 379 603 706 1009 2989 2991 2995 Numberofinlinks Number of URLs INLINKS PER URL
  36. 36. Kevin Indig | @Kevin_Indig | #TechSEOBoost 19 2 16 401 519 584 921 172 289 10 4129 10 69 11 99 6 222120 2 60 115 150 41 17 25 30 69331211254 39 311253341 30 211212124334122 30 111111111111141111112111111111111111111111111111111111112131111111112221112115234111 0 100 200 300 400 500 600 700 800 900 1000 0 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 48 50 52 54 56 58 61 63 65 70 72 75 79 81 83 86 88 91 94 101 108 110 114 120 126 131 134 143 154 161 180 190 200 216 237 274 290 319 323 326 331 335 344 352 369 375 377 379 603 706 1009 2989 2991 2995 Numberofinlinks Number of URLs INLINKS PER URL
  37. 37. Kevin Indig | @Kevin_Indig | #TechSEOBoost 0 50 100 150 200 250 300 350 400 450 0 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 75 77 79 81 83 85 87 89 93 95 97 99 102 105 109 113 119 123 130 142 146 151 155 168 170 183 189 206 235 242 362 Numberofoutgoinglinks Number of URLs OUTGOING INTERNAL LINKS PER URL
  38. 38. Kevin Indig | @Kevin_Indig | #TechSEOBoost 0 100 200 300 400 500 600 700 800 900 1000 0 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 48 50 52 54 56 58 61 63 65 70 72 75 79 81 83 86 88 91 94 101 108 110 114 120 126 131 134 143 154 161 180 190 200 216 237 274 290 319 323 326 331 335 344 352 NumberofInlinks/Outlinks Number of URLs INCOMING VS. OUTGOING INTERNAL LINKS Inlinks Outlinks
  39. 39. Kevin Indig | @Kevin_Indig | #TechSEOBoost 0 100 200 300 400 500 600 700 800 900 1000 0 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 48 50 52 54 56 58 61 63 65 70 72 75 79 81 83 86 88 91 94 101 108 110 114 120 126 131 134 143 154 161 180 190 200 216 237 274 290 319 323 326 331 335 344 352 NumberofInlinks/Outlinks Number of URLs INCOMING VS. OUTGOING INTERNAL LINKS Inlinks Outlinks Optimum
  40. 40. Kevin Indig | @Kevin_Indig | #TechSEOBoost How do we flatten this curve?
  41. 41. Kevin Indig | @Kevin_Indig | #TechSEOBoost So, what do we do with this information? URL Crawl frequency Domain pop PageRank CheiRank /URL1 200 300 0.0810 0.3555 /URL2 150 200 0.0300 0.3422 /URL3 300 100 0.0690 0.3000 /URL4 50 50 0.0220 0.2908
  42. 42. Kevin Indig | @Kevin_Indig | #TechSEOBoost Rank, take average, re-sort URL Crawl frequency Domain pop PageRank CheiRank Average /URL1 2 1 1 1 1.25 /URL2 3 2 3 2 2.5 /URL3 1 3 2 3 2.25 /URL4 4 4 4 4 4
  43. 43. Kevin Indig | @Kevin_Indig | #TechSEOBoost Rank, take average, re-sort URL Crawl frequency Domain pop PageRank CheiRank Average /URL1 2 1 1 1 1.25 /URL3 1 3 2 3 2.25 /URL2 3 2 3 2 2.5 /URL4 4 4 4 4 4
  44. 44. Kevin Indig | @Kevin_Indig | #TechSEOBoost Look for pattern in URLs and optimize accordingly 3.99000 0.03000 0.00972 0.02000 0.01000 0.03000 0.03000 0.07000 0.01000 0.00000 0.00000 0.05000 0.10000 0.15000 0.20000 0.25000 0.30000 0.35000 0.40000 0.45000 Categories Apps Add-ons Vendors Plugins Average PageRank and CheiRank by directory PageRank CheiRank
  45. 45. Kevin Indig | @Kevin_Indig | #TechSEOBoost
  46. 46. Kevin Indig | @Kevin_Indig | #TechSEOBoost 2,958 incoming links 1,094 outgoing links46 outgoing links 12 incoming links
  47. 47. Kevin Indig | @Kevin_Indig | #TechSEOBoost What happened when we rolled out the changes?
  48. 48. Kevin Indig | @Kevin_Indig | #TechSEOBoost
  49. 49. Kevin Indig | @Kevin_Indig | #TechSEOBoost
  50. 50. Kevin Indig | @Kevin_Indig | #TechSEOBoost Recap: TIPR Crawl PR + CR Backlinks Log files Power curves
  51. 51. Kevin Indig | @Kevin_Indig | #TechSEOBoost More lessons Robots.txt XML sitemaps 404 errors
  52. 52. Kevin Indig | @Kevin_Indig | #TechSEOBoost Limitations of the model • Way more ranking factors than PageRank • Only suitable for a certain size of sites • Just tested on a few sites (yet) • Still trying to find the right weighting
  53. 53. Kevin Indig | @Kevin_Indig | #TechSEOBoost Taking the concept one step further • Automating the model • Predicting success with staging environments
  54. 54. Kevin Indig | @Kevin_Indig | #TechSEOBoost Thanks for your attention Thanks to Catalyst, Audisto, and Nozzle. @Kevin_Indig www.kevin-indig.com

Editor's Notes

  • I should have called this presentation: “lessons of power curves in SEO”.
  • Power curves: a universal principle; also called “power laws” or “pareto 80/20 principle”
    Vilfredo Pareto
    Say hat a few minority has a big impact on a majority.
    We see that principle everywhere in SEO
  • We see that principle everywhere in SEO: a few keywords bring in the most traffic…
  • A few pages receive the most backlinks
  • - A few pages get crawled the most
    - It’s important to understand what moves the needle in SEO nowadays because …
  • … SEO is not getting easier!
    - We need to find more efficient tactics to not waste time and energy.
  • References of all bible verses -> idea of internal linking not new -> bar chart = chapters, length = number of verses
    Or, as my homie Arnie would say…
  • - Question for the crowd:
  • The problem with these oldschool approaches is that they’re inaccurate.
    Let me explain...
  • But all of our internal PageRank and internal link optimization models forget external PageRank.
    Most internal link models are just missing one crucial component: backlinks
  • We often calculate internal PR for internal link optimization
    But for the full picture, we need to take backlinks into account…
  • When we add links from other sites into the model, the whole PageRank equation changes!
    So, how do we solve this issue?...
  • I created a model called “TIPR” to solve this issue.
    It combines PageRank, CheiRank, Backlinks, and Log Files
    PageRank and CheiRank to create the internal link graph
    Backlinks to make the internal link graph accurate
    Log files to monitor changes; much better than rankings or organic traffic
    High correlation between crawl frequency and rankings
  • Primary Goal: Calculate ”real” internal PageRank with crawl data, backlinks, log files to optimize internal linking accurately
    Secondary Goal: Monitor crawl rate to identify technical problems
    Tertiary goal: Understand what impacts crawl rate and therefore leads to better rankings
    Let’s go through this for a minute
  • Identify pages with high PageRank (internal + external) and give to “MoneyMaker” pages with lower PR
    This is where we get back to power curves
  • - First, I tried the model on a smaller site (~3,000 pages)
  • Simple model: pulled crawl frequency, traffic, links, and other metrics
    Flattened the curve by linking from strong pages to weaker ones
  • - +160% organic traffic over time within 15 months
  • - Site with roughly 40K pages
  • Crawled site with ~40,000 URLs
    Thanks to Audisto for providing the data here.
    German engineering!
  • Used AHREFs for this, but you can also use other tools or even semrush
    Feature: most linked pages
    Then you can either use URL rating (prop. Metric) or domain pop (which I found to correlate heavily with most prop. metrics)
  • Thanks to Nozzle, for providing keyword data to better understand impact of TIPR
    Nozzle.io
  • - Some observations: power curve for crawl frequency
  • Anybody have a guess? A single file that’s being crawled double as much as any other file?
    > Robots.txt
  • Majority of URLs receive 0-10 links
  • Notice the perfect Power curve!
  • - Outgoing internal links are much more spread out but still unevenly distributed
  • - Comparison: should be much more evenly spread.
  • - Comparison: should be much more evenly spread.
  • Crawl frequency is monthly
    Dummy data
  • -
  • - Now we have a true view of which pages are strong and which are weak
  • This is the average PR and CR per directory
    We clearly see that the categories directory has way more PageRank than others
    “Addons” has much higher CheiRakn than other directories
  • This guy had just 46 outgoing links
    When we changed that…
  • - We saw our crawl rate go up
  • - We saw traffic go up
  • Robots.txt is the most crawled url across the board (probably depends on change rate)
    Google spends longest time on xml sitemaps
    Finding a set of 404s on one of our site, Google reacted with a reduction in crawl rate and ranking across the board
  • How thinks there is one?
    How thinks there are two?
  • Google transitions from search to discovery engine.
    For its 20th anniversary, Google announced It will focus on user journeys and recommendations.
    Big shift! What does that mean for SEOs and webmasters? Two things…
  • First, SEO becomes a winner takes it all game.

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