Data-driven SEO with Python : solve SEO challenges with data science using python

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. 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 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