Mark Edmondson
Learning Google Analytics
Creating Business Impact and Driving Insights
Paperback Engels 2022 1e druk 9781098113087Verwachte levertijd ongeveer 16 werkdagen
Samenvatting
Why is Google Analytics 4 the most modern data model available for digital marketing analytics? Rather than simply reporting what has happened, GA4's new cloud integrations enable more data activation, linking online and offline data across all your streams to provide end-to-end marketing data. This practical book prepares you for the future of digital marketing by demonstrating how GA4 supports these additional cloud integrations.
Author Mark Edmondson, Google developer expert for Google Analytics and Google Cloud, provides a concise yet comprehensive overview of GA4 and its cloud integrations. Data, business, and marketing analysts will learn major facets of GA4's powerful new analytics model, with topics including data architecture and strategy, and data ingestion, storage, and modeling. You'll explore common data activation use cases and get the guidance you need to implement them.
You'll learn:
- How Google Cloud integrates with GA4
- The potential use cases that GA4 integrations can enable
- Skills and resources needed to create GA4 integrations
- How much GA4 data capture is necessary to enable use cases
- The process of designing dataflows from strategy through data storage, modeling, and activation
- How to adapt the use cases to fit your business needs
Specificaties
ISBN13:9781098113087
Trefwoorden:Google Analytics
Taal:Engels
Bindwijze:paperback
Aantal pagina's:250
Uitgever:O'Reilly
Druk:1
Verschijningsdatum:30-11-2022
Hoofdrubriek:IT-management / ICT
Lezersrecensies
Wees de eerste die een lezersrecensie schrijft!
Inhoudsopgave
Preface
Who This Book Is For
Conventions Used in This Book
Using Code Examples
O'Reilly Online Learning
How to Contact Us
Acknowledgments
1. The New Google Analytics 4
Introducing GA4
The Unification of Mobile and Web Analytics
Firebase and BigQuery First Steps into the Cloud
GA4 Deployment
Universal Analytics Versus GA4
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 Case'Led 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 It's 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
GTM Capturing GA4 Events
Custom Field Configuration
Modifying or Creating GA4 Events
User Properties
Measurement Protocol v2
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
Google Cloud Storage
Event-Driven Storage
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
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
Cloud Scheduler
Cloud Build
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
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
Firestore Integrations
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 Modeling Exporting 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 Activation A 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
About the Author
Who This Book Is For
Conventions Used in This Book
Using Code Examples
O'Reilly Online Learning
How to Contact Us
Acknowledgments
1. The New Google Analytics 4
Introducing GA4
The Unification of Mobile and Web Analytics
Firebase and BigQuery First Steps into the Cloud
GA4 Deployment
Universal Analytics Versus GA4
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 Case'Led 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 It's 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
GTM Capturing GA4 Events
Custom Field Configuration
Modifying or Creating GA4 Events
User Properties
Measurement Protocol v2
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
Google Cloud Storage
Event-Driven Storage
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
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
Cloud Scheduler
Cloud Build
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
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
Firestore Integrations
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 Modeling Exporting 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 Activation A 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
About the Author
Rubrieken
- advisering
- algemeen management
- coaching en trainen
- communicatie en media
- economie
- financieel management
- inkoop en logistiek
- internet en social media
- it-management / ict
- juridisch
- leiderschap
- marketing
- mens en maatschappij
- non-profit
- ondernemen
- organisatiekunde
- personal finance
- personeelsmanagement
- persoonlijke effectiviteit
- projectmanagement
- psychologie
- reclame en verkoop
- strategisch management
- verandermanagement
- werk en loopbaan
