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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Use tools to get recommendations
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Problem: Most internal link models are inaccurate!
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PageRank exists between and within sites
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Internal PageRank is only half of the equation
Page A Page B
Page C
Page D
Site A
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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
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What we need is a model that
combines internal and external PR
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Solution: the “True Internal PR” model (TIPR)
CheiRank Backlinks
Log files
TIPR
PageRank
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What can you do with TIPR?
Calculate
“accurate” internal
PageRank
Identify technical
problems
Monitor
optimization
progress
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The TIPR process
Analysis Recommendations Monitoring
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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
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“Robin Hood” principle: take from
the rich, give to the poor
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Let’s talk some results
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Dry testing the model at small scale
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How do we flatten this curve?
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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
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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
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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
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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
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Recap: TIPR
Crawl PR + CR Backlinks Log files
Power
curves
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More lessons
Robots.txt XML sitemaps 404 errors
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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
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Taking the concept one step further
• Automating the model
• Predicting success with staging environments
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Thanks for your attention
Thanks to Catalyst, Audisto, and Nozzle.
@Kevin_Indig
www.kevin-indig.com
Notas del editor
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…