Learning Google Analytics

Spis treści książki

• Preface
  • Who This Book Is For
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. The New Google Analytics 4
  • Introducing GA4
    • The Unification of Mobile and Web Analytics
    • Firebase and BigQueryFirst Steps into the Cloud
    • GA4 Deployment
    • Universal Analytics Versus GA4
      • A new data model
      • A more flexible approach to metrics
      • BigQuery exports
      • No samplingeverything is real time
      • Privacy and digital analytics data
      • When is GA4 the answer?
  • The GA4 Data Model
    • Events
    • Custom Parameters
    • Ecommerce Items
    • User Properties
  • Google Cloud Platform
    • Relevant GCP Services
    • Coding Skills
    • Onboarding to GCP
    • Moving Up the Serverless Pyramid
    • Wrapping Up Our GCP Intro
  • Introduction to Our Use Cases
    • Use Case: Predictive Purchases
    • Use Case: Audience Segmentation
    • Use Case: Real-Time Forecasting
  • Summary
• 2. Data Architecture and Strategy
  • Creating an Environment for Success
    • Stakeholder Buy-In
    • A Use CaseLed Approach to Avoiding Spaceships
    • Demonstrating Business Value
    • Assessing Digital Maturity
    • Prioritizing Your Use Cases
  • Technical Requirements
  • Data Ingestion
  • Data Storage
  • Data Modeling
    • Model Performance Versus Business Value
    • Principle of Least Movement (of Data)
    • Raw Data Inputs to Informational Outputs
    • Helping Your Data Scientists/Modelers
    • Setting Model KPIs
    • Final Location of Modeling
  • Data Activation
    • Maybe Its Not a Dashboard
    • Interaction with Your End Users
  • User Privacy
    • Respecting User Privacy Choices
    • Privacy by Design
  • Helpful Tools
    • gcloud
    • Version Control/Git
    • Integrated Developer Environments
    • Containers (Including Docker)
  • Summary
• 3. Data Ingestion
  • Breaking Down Data Silos
    • Less Is More
    • Specifying Data Schema
  • GA4 Configuration
    • GA4 Event Types
      • Automatic events
      • Enhanced measurement events
      • Recommended events
      • Custom events
    • GTM Capturing GA4 Events
    • Custom Field Configuration
    • Modifying or Creating GA4 Events
    • User Properties
      • Google Signals
    • Measurement Protocol v2
      • Roles for MP
  • Exporting GA4 Data via APIs
    • Authentication with Data API
    • Running Data API Queries
  • BigQuery
    • Linking GA4 with BigQuery
    • BigQuery SQL on Your GA4 Exports
    • BigQuery for Other Data Sources
    • Public BigQuery Datasets
    • GTM Server Side
      • Server-side write to BigQuery
  • Google Cloud Storage
    • Event-Driven Storage
      • Cloud Storage Pub/Sub triggers into Cloud Functions
        • Cloud Storage file structure
        • Cloud Function example for importing from GCS to BigQuery
    • Data Privacy
    • CRM Database Imports via GCS
  • Setting Up Cloud Build CI/CD with GitHub
    • Setting Up GitHub
    • Setting Up the GitHub Connection to Cloud Build
    • Adding Files to the Repository
  • Summary
• 4. Data Storage
  • Data Principles
    • Tidy Data
      • Example of tidying GA4 data
    • Datasets for Different Roles
  • BigQuery
    • When to Use BigQuery
    • Dataset Organization
    • Table Tips
  • Pub/Sub
    • Setting Up a Pub/Sub Topic for GA4 BigQuery Exports
    • Creating Partitioned BigQuery Tables from Your GA4 Export
    • Server-side Push to Pub/Sub
  • Firestore
    • When to Use Firestore
    • Accessing Firestore Data Via an API
  • GCS
  • Scheduling Data Imports
    • Data Import Types: Streaming Versus Scheduled Batches
    • BigQuery Views
    • BigQuery Scheduled Queries
    • Cloud Composer
      • DAGs
      • Tips for using Airflow/Cloud Composer
    • Cloud Scheduler
    • Cloud Build
      • Cloud Build configurations
      • Build Triggers
      • GA4 applications for Cloud Build
      • Cloud Build integrations for CI/CD
  • Streaming Data Flows
    • Pub/Sub for Streaming Data
    • Apache Beam/DataFlow
    • Streaming Via Cloud Functions
  • Protecting User Privacy
    • Data Privacy by Design
    • Data Expiration in BigQuery
    • Data Loss Prevention API
  • Summary
• 5. Data Modeling
  • GA4 Data Modeling
    • Standard Reports and Explorations
    • Attribution Modeling
    • User and Session Resolution
    • Consent Mode Modeling
    • Audience Creation
    • Predictive Metrics
    • Insights
  • Turning Data into Insight
    • Scoping Data Outcomes
    • Accuracy Versus Incremental Benefit
    • Choosing Your Method of Approach
    • Keeping Your Modeling Pipelines Up-To-Date
    • Linking Datasets
  • BigQuery ML
    • Comparison of BigQuery ML Models
    • Putting a Model into Production
  • Machine Learning APIs
    • Putting an ML API into Production
  • Google Cloud AI: Vertex AI
    • Putting a Vertex API into Production
  • Integration with R
    • Overview of Capabilities
    • Docker
    • R in Production
  • Summary
• 6. Data Activation
  • Importance of Data Activation
  • GA4 Audiences and Google Marketing Platform
  • Google Optimize
  • Visualization
    • Making Dashboards Work
    • GA4 Dashboarding Options
      • GA4 Reports
      • GA4 Explorations
    • Data Studio
    • Looker
    • Other Third-Party Visualization Tools
    • Aggregate Tables Bring Data-Driven Decisions
    • Caching and Cost Management
  • Creating Marketing APIs
    • Creating Microservices
    • Event Triggers
      • Streaming GA4 events into Pub/Sub with GTM SS
    • Firestore Integrations
      • Importing BigQuery into Firestore
  • Summary
• 7. Use Case: Predictive Purchases
  • Creating the Business Case
    • Assessing Value
    • Estimating Resources
    • Data Architecture
  • Data Ingestion: GA4 Configuration
  • Data Storage and Privacy Design
  • Data ModelingExporting Audiences to Google Ads
  • Data Activation: Testing Performance
  • Summary
• 8. Use Case: Audience Segmentation
  • Creating the Business Case
    • Assessing Value
    • Estimating Resources
    • Data Architecture
  • Data Ingestion
    • GA4 Data Capture Configuration
    • GA4 BigQuery Exports
  • Data Storage: Transformations of Your Datasets
  • Data Modeling
  • Data Activation
    • Setting Up GA4 Imports Via GTM SS
    • Exporting Audiences from GA4
    • Testing Performance
  • Summary
• 9. Use Case: Real-Time Forecasting
  • Creating the Business Case
    • Resources Needed
    • Data Architecture
  • Data Ingestion
    • GA4 Configuration
  • Data Storage
    • Hosting the Shiny App on Cloud Run
  • Data Modeling
  • Data ActivationA Real-Time Dashboard
    • R Code for the Real-Time Shiny App
    • GA4 Authentication with a Service Account
    • Putting It All Together in a Shiny App
  • Summary
• 10. Next Steps
  • Motivation: How I Learned What Is in This Book
  • Learning Resources
    • Asking for Help
    • Certifications
  • Final Thoughts
• Index

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