Solve SEO problems using data science. This hands-on book is packed with Python code and data science techniques to help you generate data-driven recommendations and automate the SEO workload.
This book is a practical, modern introduction to data science in the SEO context using Python. With social media, mobile, changing search engine algorithms, and ever-increasing expectations of users for super web experiences, too much data is generated for an SEO professional to make sense of in spreadsheets. For any modern-day SEO professional to succeed, it is relevant to find an alternate solution, and data science equips SEOs to grasp the issue at hand and solve it. From machine learning to Natural Language Processing (NLP) techniques, Data-Driven SEO with Python provides tried and tested techniques with full explanations for solving both everyday and complex SEO problems.
This book is ideal for SEO professionals who want to take their industry skills
to the next level and enhance their business value, whether they are a new starter or highly experienced in SEO, Python programming, or both.
What You’ll Learn See how data science works in the SEO context Think about SEO challenges in a data driven way Apply the range of data science techniques to solve SEO issues Understand site migration and relaunches are Who This Book Is For
SEO practitioners, either at the department head level or all the way to the new career starter looking to improve their skills. Readers should have basic knowledge of Python to perform tasks like querying an API with some data exploration and visualization.
What You’ll Learn See how data science works in the SEO context Think about SEO challenges in a data driven way Apply the range of data science techniques to solve SEO issues Understand site migration and relaunches are Who This Book Is For
SEO practitioners, either at the department head level or all the way to the new career starter looking to improve their skills. Readers should have basic knowledge of Python to perform tasks like querying an API with some data exploration and visualization.
Table of Contents
About the Author
About the Contributing Editor
About the Technical Reviewer
Acknowledgments
Why I Wrote This Book
Foreword
Chapter 1: Introduction
The Inexact (Data) Science of SEO
Noisy Feedback Loop
Diminishing Value of the Channel
Making Ads Look More like Organic Listings
Lack of Sample Data
Things That Can’t Be Measured
High Costs
Why You Should Turn to Data Science for SEO
SEO Is Data Rich
SEO Is Automatable
Data Science Is Cheap
Summary
Chapter 2: Keyword Research
Data Sources
Google Search Console (GSC)
Import, Clean, and Arrange the Data
Segment by Query Type
Round the Position Data into Whole Numbers
Calculate the Segment Average and Variation
Compare Impression Levels to the Average
Explore the Data
Export
About the Author
About the Contributing Editor
About the Technical Reviewer
Acknowledgments
Why I Wrote This Book
Foreword
Chapter 1: Introduction
The Inexact (Data) Science of SEO
Noisy Feedback Loop
Diminishing Value of the Channel
Making Ads Look More like Organic Listings
Lack of Sample Data
Things That Can’t Be Measured
High Costs
Why You Should Turn to Data Science for SEO
SEO Is Data Rich
SEO Is Automatable
Data Science Is Cheap
Summary
Chapter 2: Keyword Research
Data Sources
Google Search Console (GSC)
Import, Clean, and Arrange the Data
Segment by Query Type
Round the Position Data into Whole Numbers
Calculate the Segment Average and Variation
Compare Impression Levels to the Average
Explore the Data
Export
Your High Value Keyword List
Activation
Google Trends
Single Keyword
Multiple Keywords
Visualizing Google Trends
Forecast Future Demand
Exploring Your Data
Decomposing the Trend
Fitting Your SARIMA Model
Test the Model
Forecast the Future
Clustering by Search Intent
Starting Point
Filter Data for Page 1
Convert Ranking URLs to a String
Compare SERP Distance
SERP Competitor Titles
Filter and Clean the Data for Sections Covering Only What You Sell
Extract Keywords from the Title Tags
Filter Using SERPs Data
Summary
Chapter 3: Technical
Where Data Science Fits In
Modeling Page Authority
Filtering in Web Pages
Examine the Distribution of Authority Before Optimization
Calculating the New Distribution
Internal Link Optimization
By Site Level
Site-Level URLs That Are Underlinked
By Page Authority
Page Authority URLs That Are Underlinked
Content Type
Combining Site Level and Page Authority
Anchor Texts
Anchor Issues by Site Level
Anchor Text Relevance
Location
Anchor Text Words
Core Web Vitals (CWV)
Landscape
Onsite CWV
Summary
Chapter 4: Content and UX
Content That Best Satisfies the User Query
Data Sources
Keyword Mapping
String Matching
String Distance to Map Keyword Evaluation
Content Gap Analysis
Getting the Data
Creating the Combinations
Finding the Content Intersection
Establishing Gap
Content Creation: Planning Landing Page Content
Getting SERP Data
Crawling the Content
Extracting the Headings
Cleaning and Selecting Headings
Cluster Headings
Reflections
Summary
Chapter 5: Authority
Some SEO History
A Little More History
Authority, Links, and Other
Examining Your Own Links
Importing and Cleaning the Target Link Data
Targeting Domain Authority
Domain Authority Over Time
Targeting Link Volumes
Analyzing Your Competitor’s Links
Data Importing and Cleaning
Anatomy of a Good Link
Link Quality
Link Volumes
Link Velocity
Link Capital
Finding Power Networks
Taking It Further
Summary
Chapter 6: Competitors
And Algorithm Recovery Too!
Defining the Problem
Outcome Metric
Why Ranking?
Features
Data Strategy
Data Sources
Explore, Clean, and Transform
Import Data – Both SERPs and Features
Start with the Keywords
Focus on the Competitors
Join the Data
Derive New Features
Single-Level Factors (SLFs)
Rescale Your Data
Near Zero Variance (NZVs)
Median Impute
One Hot Encoding (OHE)
Eliminate NAs
Modeling the SERPs
Evaluate the SERPs ML Model
The Most Predictive Drivers of Rank
How Much Rank a Ranking Factor Is Worth
The Winning Benchmark for a Ranking Factor
Tips to Make Your Model More Robust
Activation
Automating This Analysis
Summary
Chapter 7: Experiments
How Experiments Fit into the SEO Process
Generating Hypotheses
Competitor Analysis
Website Articles and Social Media
You/Your Team’s Ideas
Recent Website Updates
Conference Events and Industry Peers
Past Experiment Failures
Experiment Design
Zero Inflation
Split A/A Analysis
Determining the Sample Size
Test and Control Assignment
Running Your Experiment
Ending A/B Tests Prematurely
Not Basing Tests on a Hypothesis
Simultaneous Changes to Both Test and Control
Non-QA of Test Implementation and Experiment Evaluation
Split A/B Exploratory Analysis
Inconclusive Experiment Outcomes
Summary
Chapter 8: Dashboards
Data Sources
Don’t Plug Directly into Google Data Studio
Using Data Warehouses
Extract, Transform, and Load (ETL)
Extracting Data
Google Analytics
DataForSEO SERPs API
Google Search Console (GSC)
Google PageSpeed API
Transforming Data
Loading Data
Visualization
Automation
Summary
Chapter 9: Site Migration Planning
Verifying Traffic and Ranking Changes
Identifying the Parent and Child Nodes
Separating Migration Documents
Finding the Closest Matching Category URL
Mapping Current URLs to the New Category URLs
Mapping the Remaining URLs to the Migration URL
Importing the URLs
Migration Forensics
Traffic Trends
Segmenting URLs
Time Trends and Change Point Analysis
Segmented Time Trends
Analysis Impact
Diagnostics
Road Map
Summary
Chapter 10: Google Updates
Algo Updates
Dedupe
Domains
Reach Stratified
Rankings
WAVG Search Volume
Visibility
Result Types
Cannibalization
Keywords
Token Length
Token Length Deep Dive
Target Level
Keywords
Pages
Segments
Top Competitors
Visibility
Snippets
Summary
Chapter 11: The Future of SEO
Aggregation
Distributions
String Matching
Clustering
Machine Learning (ML) Modeling
Set Theory
What Computers Can and Can’t Do
For the SEO Experts
Summary
Index
Activation
Google Trends
Single Keyword
Multiple Keywords
Visualizing Google Trends
Forecast Future Demand
Exploring Your Data
Decomposing the Trend
Fitting Your SARIMA Model
Test the Model
Forecast the Future
Clustering by Search Intent
Starting Point
Filter Data for Page 1
Convert Ranking URLs to a String
Compare SERP Distance
SERP Competitor Titles
Filter and Clean the Data for Sections Covering Only What You Sell
Extract Keywords from the Title Tags
Filter Using SERPs Data
Summary
Chapter 3: Technical
Where Data Science Fits In
Modeling Page Authority
Filtering in Web Pages
Examine the Distribution of Authority Before Optimization
Calculating the New Distribution
Internal Link Optimization
By Site Level
Site-Level URLs That Are Underlinked
By Page Authority
Page Authority URLs That Are Underlinked
Content Type
Combining Site Level and Page Authority
Anchor Texts
Anchor Issues by Site Level
Anchor Text Relevance
Location
Anchor Text Words
Core Web Vitals (CWV)
Landscape
Onsite CWV
Summary
Chapter 4: Content and UX
Content That Best Satisfies the User Query
Data Sources
Keyword Mapping
String Matching
String Distance to Map Keyword Evaluation
Content Gap Analysis
Getting the Data
Creating the Combinations
Finding the Content Intersection
Establishing Gap
Content Creation: Planning Landing Page Content
Getting SERP Data
Crawling the Content
Extracting the Headings
Cleaning and Selecting Headings
Cluster Headings
Reflections
Summary
Chapter 5: Authority
Some SEO History
A Little More History
Authority, Links, and Other
Examining Your Own Links
Importing and Cleaning the Target Link Data
Targeting Domain Authority
Domain Authority Over Time
Targeting Link Volumes
Analyzing Your Competitor’s Links
Data Importing and Cleaning
Anatomy of a Good Link
Link Quality
Link Volumes
Link Velocity
Link Capital
Finding Power Networks
Taking It Further
Summary
Chapter 6: Competitors
And Algorithm Recovery Too!
Defining the Problem
Outcome Metric
Why Ranking?
Features
Data Strategy
Data Sources
Explore, Clean, and Transform
Import Data – Both SERPs and Features
Start with the Keywords
Focus on the Competitors
Join the Data
Derive New Features
Single-Level Factors (SLFs)
Rescale Your Data
Near Zero Variance (NZVs)
Median Impute
One Hot Encoding (OHE)
Eliminate NAs
Modeling the SERPs
Evaluate the SERPs ML Model
The Most Predictive Drivers of Rank
How Much Rank a Ranking Factor Is Worth
The Winning Benchmark for a Ranking Factor
Tips to Make Your Model More Robust
Activation
Automating This Analysis
Summary
Chapter 7: Experiments
How Experiments Fit into the SEO Process
Generating Hypotheses
Competitor Analysis
Website Articles and Social Media
You/Your Team’s Ideas
Recent Website Updates
Conference Events and Industry Peers
Past Experiment Failures
Experiment Design
Zero Inflation
Split A/A Analysis
Determining the Sample Size
Test and Control Assignment
Running Your Experiment
Ending A/B Tests Prematurely
Not Basing Tests on a Hypothesis
Simultaneous Changes to Both Test and Control
Non-QA of Test Implementation and Experiment Evaluation
Split A/B Exploratory Analysis
Inconclusive Experiment Outcomes
Summary
Chapter 8: Dashboards
Data Sources
Don’t Plug Directly into Google Data Studio
Using Data Warehouses
Extract, Transform, and Load (ETL)
Extracting Data
Google Analytics
DataForSEO SERPs API
Google Search Console (GSC)
Google PageSpeed API
Transforming Data
Loading Data
Visualization
Automation
Summary
Chapter 9: Site Migration Planning
Verifying Traffic and Ranking Changes
Identifying the Parent and Child Nodes
Separating Migration Documents
Finding the Closest Matching Category URL
Mapping Current URLs to the New Category URLs
Mapping the Remaining URLs to the Migration URL
Importing the URLs
Migration Forensics
Traffic Trends
Segmenting URLs
Time Trends and Change Point Analysis
Segmented Time Trends
Analysis Impact
Diagnostics
Road Map
Summary
Chapter 10: Google Updates
Algo Updates
Dedupe
Domains
Reach Stratified
Rankings
WAVG Search Volume
Visibility
Result Types
Cannibalization
Keywords
Token Length
Token Length Deep Dive
Target Level
Keywords
Pages
Segments
Top Competitors
Visibility
Snippets
Summary
Chapter 11: The Future of SEO
Aggregation
Distributions
String Matching
Clustering
Machine Learning (ML) Modeling
Set Theory
What Computers Can and Can’t Do
For the SEO Experts
Summary
Index
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