Measuring Link Building:
You’re Doing It Wrong
Tom Capper
Moz
@THCapper
@THCapper
@THCapper
@THCapper
@THCapper
@THCapper
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Disclaimer
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Agenda
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Agenda
Why we build links
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Agenda
Why we build links
Why we use these metrics
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@THCapper
Agenda
Why we build links
Why we use these metrics
Better measurement
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@THCapper
Agenda
Why we build links
Why we use these metrics
Better measurement
What to do next
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@THCapper
Agenda
Why we build links
Why we use these metrics
Better measurement
What to do next
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Hold that thought
& imagine it’s 1998
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Ancient history
People navigated the web with
links
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Ancient history
People navigated the web with
links
Links, therefore, were a proxy
for popularity
@THCapper
Ancient history
People navigated the web with
links
Links, therefore, were a proxy
for popularity
This is Google’s main insight &
why they won
@THCapper
Ancient history
People navigated the web with
links
Links, therefore, were a proxy
for popularity
This is Google’s main insight &
why they won*
*gross oversimplification
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Back to 2022
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Is it still true?
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Is it still true?
We still use links, but they’re on
SERPs and feeds
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Is it still true?
We still use links, but they’re on
SERPs and feeds
Google has better data on
popularity anyway
@THCapper
Doesn’t matter
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Doesn’t matter
Core methodology still
underpins how Google works
@THCapper
Doesn’t matter
Core methodology still
underpins how Google works
Albeit more nuanced
@THCapper
Doesn’t matter
Core methodology still
underpins how Google works
Albeit more nuanced
Albeit with more layers on top
@THCapper
AKA
Link equity 🙂
Link flow 🙁
Link juice 🤮
Linking strength 🤔
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So how does it work?
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PageRank
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A proxy for popularity
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PageRank
A
Imagine this is the internet.
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PageRank
A
Imagine this is the internet.
What is the probability a random browser is on this page?
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PageRank
A
Imagine this is the internet.
What is the probability a random browser is on this page?
= 1/n
Where n is the number of
pages on the web.
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PageRank
A 0.85*A
For that value of A:
What about the one page it links to?
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PageRank
A 0.85*A
For that value of A:
What about the one page it links to?
0.15*A
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PageRank
A 0.85*A
For that value of 0.85*A:
What if A links to two pages?
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PageRank
A
0.85*A
/2
0.85*A
/2
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PageRank
A
0.85*A
/2
0.85*A
/2
0.85*0.85*A
/2
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Key features of PageRank
Domain agnostic
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Key features of PageRank
Domain agnostic
Sensitive to obscurity of linking page
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Key features of PageRank
Domain agnostic
Sensitive to obscurity of linking page
Sensitive to number of links
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Let’s Compare
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Link building KPIs
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Link building KPIs
LRDs
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Link building KPIs
LRDs
# of Links
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Link building KPIs
LRDs
# of Links
# of Links over x DA
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Link building KPIs
LRDs
# of Links
# of Links over x DA
# of dofollow / non
syndicate links
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Link building KPIs
LRDs
# of Links
# of Links over x DA
# of dofollow / non
syndicate links
DA
@THCapper
Link building KPIs
LRDs
# of Links
# of Links over x DA
# of dofollow / non
syndicate links
DA
Features of PageRank
Domain agnostic
Sensitive to obscurity of
linking page
Sensitive to number of
links
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Takeaway 1:
Focus on the Mechanism
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@THCapper
Takeaway 1:
Focus on the Mechanism
Aka caring about the linking page,
not just the linking site
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Agenda
Why we build links
Why we use these metrics
Better measurement
What to do next
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Pros & Cons
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Domain level metrics (e.g. DA)
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https://moz.com/blog/
new-domain-authority
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Domain level metrics (e.g. DA)
All things being equal,
how well will a page on this domain rank?
“
”
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Domain level metrics (e.g. DA)
− PAGErank
− Indifferent to link
specifics
− Wrong question
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Domain level metrics (e.g. DA)
− PAGErank
− Indifferent to link
specifics
− Wrong question
+ Available beforehand
+ Google ambiguity
+ Trained on rankings
+ Universally understood
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@RobOusbey
Vs
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Top party games 2022
Spin the
bottle
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Top party games 2022
Spin the
bottle
Truth or
dare
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Top party games 2022
Spin the
bottle
Truth or
dare
Guess the
DA
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Domain level metrics (e.g. DA)
− PAGErank
− Indifferent to link
specifics
− Wrong question
+ Available beforehand
+ Google ambiguity
+ Trained on rankings
+ Universally understood
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Page level metrics (e.g. PA)
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Page level metrics (e.g. PA)
− Wait for page to be
crawled
− Somewhat domain
sensitive
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Page level metrics (e.g. PA)
− Wait for page to be
crawled
− Somewhat domain
sensitive
+ Much closer to value
of link
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https://moz.com/blog
/2021-links-ranking-
correlation-study
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Outcome metrics (e.g. rankings, revenue)
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Outcome metrics (e.g. rankings, revenue)
− Causation vs
correlation
− Effect of individual
link lost in noise
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Outcome metrics (e.g. rankings, revenue)
− Causation vs
correlation
− Effect of individual
link lost in noise
+ Obvious commercial
relevance
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Analytics metrics (impressions, referring traffic)
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Analytics metrics (impressions, referring traffic)
− Access issues
− Wait for data
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Analytics metrics (impressions, referring traffic)
− Access issues
− Wait for data
+ Google’s holy grail
+ Captures non-SEO
value
+ Captures relevance
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@THCapper
Agenda
Why we build links
Why we use these metrics
Better measurement
What to do next
@THCapper
The challenge
The precision & nuance of referral traffic
and
The ubiquity and availability of domain metrics
@THCapper
The challenge
+ Available beforehand
+ Google ambiguity
+ Trained on rankings
+ Universally understood
+ Google’s holy grail
+ Captures non-SEO
value
+ Captures relevance
@THCapper
Metrics for Links
Fast | Slow
Actual
Indicative
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Metrics for Links
Actual
Indicative
DA
Spam Score
# of links
Fast | Slow
@THCapper
Metrics for Links
Actual
Indicative
DA
Spam Score
Reach
# of links
Fast | Slow
@THCapper
Metrics for Links
Actual
Indicative
DA
Spam Score
PA
Reach
# of links
Fast | Slow
@THCapper
Metrics for Links
Actual
Indicative
DA
Spam Score
PA
Reach
Referral Traffic
# of links
Fast | Slow
@THCapper
Metrics for Links
Actual
Indicative
DA
Spam Score
PA
Reach
Referral Traffic
Rankings
# of links
Fast | Slow
@THCapper
@THCapper
Takeaway:
Measure in phases
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Actual
Indicative
DA
Spam Score
PA
Reach
Referral Traffic
Rankings
Metrics for Links
Prospecting
# of links
Fast | Slow
@THCapper
Actual
Indicative
DA
Spam Score
PA
Reach
Referral Traffic
Rankings
Metrics for Links
# of links
Prospecting Reporting
Fast | Slow
@THCapper
Actual
Indicative
DA
Spam Score
PA
Reach
Referral Traffic
Rankings
Metrics for Links
# of links
Prospecting Reporting Post Mortem
Fast | Slow
@THCapper
Ideal world
The precision & nuance of referral traffic
and
The ubiquity and availability of domain metrics
@THCapper
@THCapper
Agenda
Why we build links
Why we use these metrics
Better measurement
What to do next
@THCapper
1. Focus on the mechanism
@THCapper
1. Focus on the mechanism
A
0.85*
A/2
0.85*
A/2
@THCapper
2. Measure in phases
@THCapper
2. Measure in phases
Actual
Indicative
DA
Spam Score
PA
Reach
Referral Traffic
Rankings
# of links
Prospecting Reporting Post Mortem
Fast | Slow
@THCapper
3. Learn from concrete metrics
@THCapper
3. Learn from concrete metrics
Actual
Indicative
DA
Spam Score
PA
Reach
Referral Traffic
Rankings
# of links
Prospecting Reporting Post Mortem
Fast | Slow
@THCapper
3. Learn from concrete metrics
Actual
Indicative
Referral Traffic
Rankings
Prospecting Reporting Post Mortem
Fast | Slow
Thanks!

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