Better metrics for measuring link building.

1. Measuring Link Building:
You’re Doing It Wrong
Tom Capper
Moz
@THCapper

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@THCapper

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@THCapper

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@THCapper
Disclaimer

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@THCapper
Agenda

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@THCapper
Agenda
Why we build links

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@THCapper
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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@THCapper
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

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Ancient history
People navigated the web with
links
Links, therefore, were a proxy
for popularity
This is Google’s main insight &
why they won

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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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@THCapper
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

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Doesn’t matter

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Doesn’t matter
Core methodology still
underpins how Google works

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Doesn’t matter
Core methodology still
underpins how Google works
Albeit more nuanced

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Doesn’t matter
Core methodology still
underpins how Google works
Albeit more nuanced
Albeit with more layers on top

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AKA
Link equity 🙂
Link flow 🙁
Link juice 🤮
Linking strength 🤔

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@THCapper
So how does it work?

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PageRank

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@THCapper
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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@THCapper
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

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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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@THCapper
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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@THCapper
Agenda
Why we build links
Why we use these metrics
Better measurement
What to do next

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@THCapper
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
@THCapper

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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

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The challenge
The precision & nuance of referral traffic
and
The ubiquity and availability of domain metrics

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The challenge
+ Available beforehand
+ Google ambiguity
+ Trained on rankings
+ Universally understood
+ Google’s holy grail
+ Captures non-SEO
value
+ Captures relevance

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Metrics for Links
Fast | Slow
Actual
Indicative

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Metrics for Links
Actual
Indicative
DA
Spam Score
# of links
Fast | Slow

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Metrics for Links
Actual
Indicative
DA
Spam Score
Reach
# of links
Fast | Slow

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Metrics for Links
Actual
Indicative
DA
Spam Score
PA
Reach
# of links
Fast | Slow

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Metrics for Links
Actual
Indicative
DA
Spam Score
PA
Reach
Referral Traffic
# of links
Fast | Slow

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Metrics for Links
Actual
Indicative
DA
Spam Score
PA
Reach
Referral Traffic
Rankings
# of links
Fast | Slow

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@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

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Actual
Indicative
DA
Spam Score
PA
Reach
Referral Traffic
Rankings
Metrics for Links
# of links
Prospecting Reporting
Fast | Slow

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Actual
Indicative
DA
Spam Score
PA
Reach
Referral Traffic
Rankings
Metrics for Links
# of links
Prospecting Reporting Post Mortem
Fast | Slow

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Ideal world
The precision & nuance of referral traffic
and
The ubiquity and availability of domain metrics

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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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1. Focus on the mechanism

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1. Focus on the mechanism
A
0.85*
A/2
0.85*
A/2

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2. Measure in phases

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2. Measure in phases
Actual
Indicative
DA
Spam Score
PA
Reach
Referral Traffic
Rankings
# of links
Prospecting Reporting Post Mortem
Fast | Slow

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3. Learn from concrete metrics

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3. Learn from concrete metrics
Actual
Indicative
DA
Spam Score
PA
Reach
Referral Traffic
Rankings
# of links
Prospecting Reporting Post Mortem
Fast | Slow

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3. Learn from concrete metrics
Actual
Indicative
Referral Traffic
Rankings
Prospecting Reporting Post Mortem
Fast | Slow

98. Thanks!