This document discusses using data science and machine learning techniques like XGBoost for search engine optimization (SEO). It recommends building an SEO datamart using data from sources like Semrush, Majestic, and Screaming Frog. Models can be built using XGBoost to predict Google rankings based on variables like backlinks, domains, trust flow, and response time. The document advocates for a new role of data scientist SEO to help automate SEO using machine learning platforms like Dataiku.
14. EMPIRICISM
01.
MAKE OBSERVATIONS
05BIS.
REFINE, ALTER, EXPAND,
OR REJECT HYPOTHESES
04.
DEVELOP TESTABLE
PREDICTIONS
02.
THINK OF INTERESTING
QUESTIONS
06.
DEVELOP
GENERAL THEORIES
05.
GATHER DATA TO TEST
PREDICTIONS
03.
FORMULATE
HYPOTHESES
22. LEARNING DATA
SCIENCE
– Data Scientist Toolbox
– Getting & cleaning Data
– R / Python Programming
– Explorary data
– Machine Learning
– Big Data
23. SEO DATAMART
COMPETITORS
OTHER TRAFFIC SOURCES DATA
SOCIAL NETWORK
SEARCH CONSOLE
CRAWLS
STOCK, PRICES, SALES DATA
CUSTOMERS DATA
EVENTS
WEB ANALYTICS
NETLINKING
SEMANTICAL
WEBPERFS
SEARCH TRENDS
SERVER LOGS
33. CLEAN
DATA
REMOVE INVALID URLS
Slow Crawl
Rate
Non-HTML
Content
Network
Problems
Slow
Web Servers
WAIT TIMES
Errors from
Web Servers
URL Moved Permanently
Redirect (301)
URL Moved Temporarily
Redirect (302)
Authentication Required (401)
or Document Not Found (404)
Cyclic
Redirects
41. TAKE AWAY
…
AUTOMATED MACHINE
LEARNING WITH DATAIKU
AUTOMATED KPI REPORTING SEO DATALAKE TEXT GENERATION
OPPORTUNITIES DETECTION PREDICTIVE ANALYSIS PROCESS MINING
AUTOMATED MACHINE
LEARNING WITH DATAIKU
SEO DATAMART
42. NOW, MACHINES CAN LEARN
AND ADAPT, IT IS TIME TO TAKE
ADVANTAGE OF THE OPPORTUNITY TO
CREATE NEW JOBS.
Data-SEO, Data-Doctor,
Data-Journalist …