Building an ML Model for Predicting SERP Rankings

Moz & Russ Jones | @moz @rjonesx | #TechSEOBoost
TechSEO Boost Research Finalists
Do Links Still Matter for
SEO in 2017?
Utilizing Cloudflare Workers to
Overcome the Challenges of
Legacy Tech Stacks and High
DevOps Costs
Building a Machine Learning
Model Capable of Predicting a
Webpage Ranking in SERP
with a Reliability of 92%
Eric EngeDan TaylorVincent Terrasi
Sponsored by

Vincent Terrasi | @VincentTerrasi | #TechSEOBoost
BUILDING AN ML
MODEL CAPABLE
OF PREDICTING A
WEBPAGE’S
RANKING IN SERP
–
With a reliability of 92%

OVH
• Global company, specialising in cloud
solutions and internet infrastructure.
• 1.3 million customers in 140 different
countries.
• 300k servers
VINCENT+DREAM TEAM
• All knowledge is connected to all other
knowledge. The fun is in making the
connections.
• With data marketing, we can find new
connections everyday !

Purpose
Every year, websites share the ranking
criteria that they think are most relevant for
SEO.
OVH’s solution uses machine learning to
identify the most relevant SEO factors for
specific themes, with no human intervention.

“Overall, the reason for
combining different tools is
to get as close as possible
to Google’s ranking factors”

Methodology
• SEMrush
• Top 100 URLs for more than 3,000
keywords
• Three additional pieces of software:
• Majestic (for external criteria)
• ScreamingFrog & Oncrawl
(for internal criteria)
• Visiblis (for semantic content)
• Using ML algorithms: XGBoost or Random Forest
• Industrialise with Dataiku – the data science platform

“In other words, we are now
able to test a page to determine
its chances of being in the top
10 Google results for relevant
searches.”
https://github.com/ovh/summit2016-RankingPredict

Results
• OVH was able to define relevant variables
for specific topics with an extremely high
reliability (92%).
• Each time a new webpage is created, we
can tell whether it will rank first in Google
searches, check its position, and compare
it to other websites – all in real time.

Big Thanks to Tech SEO Boost!
–
OVHCloud.com
@ovh
My blog: data-seo.com

Dan Taylor | @salt_agency | #TechSEOBoost
UTILIZING CLOUDFLARE
WORKERS TO
OVERCOME THE
CHALLENGES OF
LEGACY TECH STACKS
AND HIGH DEVOPS
COSTS
–
Igor Krestov & Dan Taylor
SALT.agency

Hypothesis
The purpose of our research was to establish
whether or not serverless technology can provide
workable and accessible solutions to businesses,
at scale.
1. Utilizing the latest serverless technology, can
we implement technical elements of SEO,
such as basic Hreflang, redirects, and page
level meta robots tags through service worker
technology.
2. If (1) is successful, can the implementations
be picked up by Google in the rendered
HTML of the page (making them useful).

Methodology
In order to test our hypothesis, we used a
mixture of real-world applications and testing
websites. The participant websites within this
research were:
• A personal website - testing Hreflang
implementations injected via Service
Worker into the <head>
• A San Francisco based OS API gateway
provider - using service workers to
implement 301 redirects following a
migration

Findings
Through our testing we found that at scale,
we could utilize Cloudflare workers for a
number of tasks, including:
<head> Hreflang implementation, redirects,
custom HTTP headers based on user IP,
customer JS overlays based on user
IP/device, page level robots, implementing &
modifying canonicals, A/B testing…
And this technology can be scaled, and made
accessible.

Pros & Cons
Pros
• Workers are written in JavaScript
• Simple API
• One-click deployment
• Next to zero DevOps
Cons
• Potential to affect and impact all requests
• Potential to add additional latency and slow page load times, depending on implementation
• Potential to introduce front-end bugs that are difficult to debug, when there is little/no access to
the backend, and when it is unclear what is being modified/injected through stream
transformation.

TechSEO Boost Research Finalists
Do Links Still Matter for
SEO in 2017?
Utilizing Cloudflare Workers to
Overcome the Challenges of
Legacy Tech Stacks and High
DevOps Costs
Building a Machine Learning
Model Capable of Predicting a
Webpage Ranking in SERP
with a Reliability of 92%
Eric EngeDan TaylorVincent Terrasi
Sponsored by

Eric Enge | @stonetemple | #TechSEOBoost
Do Links Still Matter?
• There remains a continuing debate about
the value of links
• Long gone are the days where you could
get a Tupperware page to rank for a used
car query
• Moz and Searchmetrics used to publish
correlation studies, but they have cooled
off on the efforts
• We set out to do our own correlation study
on links as a ranking factor, and to
maintain it over time

We Used New Math
• Brought in elite statistics experts
to improve the approach
• Correlation value “r” has no
meaning in statistics
• We used “r2”, which tells us the
percent of the variability in Y
which is explained by X.
• We then took the quadratic
mean of all the r squared values
across 16K SERPs.

The Basic Results
• The correlation score
INCREASED from
0.303 to 0.325.
• That variability
probably falls within
measurement error
ranges
• But, if definitely did
not show a decrease

One More Novel Approach
• Normalized link totals for every SERP to
values between 0 and 1
• Prevented highly-linked-to web pages from
popular queries from skewing the results
• Summed up the totals of all the links for
each ranking position (total links to all
position 1, total to all position 2, …)
• Calculated the quadratic mean of these
values to get new correlation scores
• Aggregated view as to whether or not links
matter
• You bet they do!

Thank You Judges!
Sponsored by
Ruth Burr Reedy Rhea Drysdale Geoff KenyonAlexis Sanders Russ Jones
Neil Martinsen-Burrell Paul Shapiro Aleyda Solis Clark Boyd

And the winner is….

Catalyst | @CatalystSEM | #TechSEOBoost

Building an ML Model for Predicting SERP Rankings

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