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Learning Google Analytics: Creating Business Impact and Driving Insights [Mīkstie vāki]

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  • Formāts: Paperback / softback, 339 pages
  • Izdošanas datums: 30-Nov-2022
  • Izdevniecība: O'Reilly Media
  • ISBN-10: 109811308X
  • ISBN-13: 9781098113087
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Learning Google Analytics: Creating Business Impact and Driving InsightsPalielināt
  • Formāts: Paperback / softback, 339 pages
  • Izdošanas datums: 30-Nov-2022
  • Izdevniecība: O'Reilly Media
  • ISBN-10: 109811308X
  • ISBN-13: 9781098113087
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781098113087.html
Keywords:

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

Preface ix
1 The New Google Analytics 4
1(1)
Introducing GA4
1(5)
The Unification of Mobile and Web Analytics
2(1)
Firebase and BigQuery--First Steps into the Cloud
3(1)
GA4 Deployment
3(1)
Universal Analytics Versus GA4
4(2)
The GA4 Data Model
6(5)
Events
7(1)
Custom Parameters
8(1)
Ecommerce Items
9(1)
User Properties
10(1)
Google Cloud Platform
11(7)
Relevant GCP Services
11(1)
Coding Skills
12(2)
Onboarding to GCP
14(1)
Moving Up the Serverless Pyramid
15(3)
Wrapping Up Our GCP Intro
18(1)
Introduction to Our Use Cases
18(6)
Use Case: Predictive Purchases
19(1)
Use Case: Audience Segmentation
20(2)
Use Case: Real-Time Forecasting
22(2)
Summary
24(1)
2 Data Architecture and Strategy
25(22)
Creating an Environment for Success
25(3)
Stakeholder Buy-In
25(1)
A Use Case-Led Approach to Avoiding Spaceships
26(1)
Demonstrating Business Value
27(1)
Assessing Digital Maturity
28(1)
Prioritizing Your Use Cases
28(1)
Technical Requirements
28(2)
Data Ingestion
30(1)
Data Storage
31(3)
Data Modeling
34(4)
Model Performance Versus Business Value
35(1)
Principle of Least Movement (of Data)
36(1)
Raw Data Inputs to Informational Outputs
36(1)
Helping Your Data Scientists/Modelers
36(1)
Setting Model KPIs
37(1)
Final Location of Modeling
37(1)
Data Activation
38(2)
Maybe Its Not a Dashboard
38(1)
Interaction with Your End Users
39(1)
User Privacy
40(3)
Respecting User Privacy Choices
42(1)
Privacy by Design
42(1)
Helpful Tools
43(2)
Gcloud
43(1)
Version Control/Git
43(1)
Integrated Developer Environments
44(1)
Containers (Including Docker)
44(1)
Summary
45(2)
3 Data Ingestion
47(54)
Breaking Down Data Silos
47(2)
Less Is More
48(1)
Specifying Data Schema
48(1)
GA4 Configuration
49(22)
GA4 Event Types
49(4)
GTM Capturing GA4 Events
53(4)
Custom Field Configuration
57(2)
Modifying or Creating GA4 Events
59(2)
User Properties
61(8)
Measurement Protocol v2
69(2)
Exporting GA4 Data via APIs
71(5)
Authentication with Data API
73(1)
Running Data API Queries
74(2)
BigQuery
76(6)
Linking GA4 with BigQuery
76(2)
BigQuery SQL on Your GA4 Exports
78(1)
BigQuery for Other Data Sources
79(1)
Public BigQuery Datasets
80(1)
GTM Server Side
80(2)
Google Cloud Storage
82(13)
Event-Driven Storage
83(11)
Data Privacy
94(1)
CRM Database Imports via GCS
94(1)
Setting Up Cloud Build CI/CD with GitHub
95(5)
Setting Up GitHub
95(1)
Setting Up the GitHub Connection to Cloud Build
95(3)
Adding Files to the Repository
98(2)
Summary
100(1)
4 Datastorage
101(60)
Data Principles
102(7)
Tidy Data
102(5)
Datasets for Different Roles
107(2)
BigQuery
109(5)
When to Use BigQuery
110(1)
Dataset Organization
111(1)
Table Tips
112(2)
Pub/Sub
114(6)
Setting Up a Pub/Sub Topic for GA4 BigQuery Exports
114(2)
Creating Partitioned BigQuery Tables from Your GA4 Export
116(2)
Server-side Push to Pub/Sub
118(2)
Firestore
120(2)
When to Use Firestore
120(1)
Accessing Firestore Data Via an API
121(1)
GCS
122(5)
Scheduling Data Imports
127(18)
Data Import Types: Streaming Versus Scheduled Batches
127(1)
BigQuery Views
128(1)
BigQuery Scheduled Queries
129(2)
Cloud Composer
131(5)
Cloud Scheduler
136(1)
Cloud Build
137(8)
Streaming Data Flows
145(11)
Pub/Sub for Streaming Data
145(1)
Apache Beam/DataFlow
146(6)
Streaming Via Cloud Functions
152(4)
Protecting User Privacy
156(3)
Data Privacy by Design
156(2)
Data Expiration in BigQuery
158(1)
Data Loss Prevention API
159(1)
Summary
159(2)
5 Data Modeling
161(32)
GA4 Data Modeling
161(7)
Standard Reports and Explorations
162(1)
Attribution Modeling
162(2)
User and Session Resolution
164(1)
Consent Mode Modeling
165(1)
Audience Creation
166(1)
Predictive Metrics
167(1)
Insights
167(1)
Turning Data into Insight
168(9)
Scoping Data Outcomes
169(3)
Accuracy Versus Incremental Benefit
172(1)
Choosing Your Method of Approach
173(1)
Keeping Your Modeling Pipelines Up-To-Date
174(1)
Linking Datasets
175(2)
BigQuery ML
177(4)
Comparison of BigQuery ML Models
177(3)
Putting a Model into Production
180(1)
Machine Learning APIs
181(2)
Putting an ML API into Production
182(1)
Google Cloud AI: Vertex AI
183(2)
Putting a Vertex API into Production
185(1)
Integration with R
185(6)
Overview of Capabilities
186(2)
Docker
188(2)
R in Production
190(1)
Summary
191(2)
6 Data Activation
193(42)
Importance of Data Activation
194(1)
GA4 Audiences and Google Marketing Platform
195(6)
Google Optimize
201(2)
Visualization
203(22)
Making Dashboards Work
203(1)
GA4 Dashboarding Options
204(13)
Data Studio
217(4)
Looker
221(1)
Other Third-Party Visualization Tools
222(1)
Aggregate Tables Bring Data-Driven Decisions
223(1)
Caching and Cost Management
224(1)
Creating Marketing APIs
225(9)
Creating Microservices
225(2)
Event Triggers
227(3)
Firestore Integrations
230(4)
Summary
234(1)
7 Use Case: Predictive Purchases
235(10)
Creating the Business Case
236(2)
Assessing Value
236(1)
Estimating Resources
237(1)
Data Architecture
237(1)
Data Ingestion: GA4 Configuration
238(1)
Data Storage and Privacy Design
239(1)
Data Modeling--Exporting Audiences to Google Ads
240(2)
Data Activation: Testing Performance
242(2)
Summary
244(1)
8 Use Case: Audience Segmentation
245(22)
Creating the Business Case
245(5)
Assessing Value
246(1)
Estimating Resources
247(2)
Data Architecture
249(1)
Data Ingestion
250(4)
GA4 Data Capture Configuration
250(2)
GA4 BigQuery Exports
252(2)
Data Storage: Transformations of Your Datasets
254(2)
Data Modeling
256(2)
Data Activation
258(8)
Setting Up GA4 Imports Via GTM SS
260(3)
Exporting Audiences from GA4
263(2)
Testing Performance
265(1)
Summary
266(1)
9 Use Case: Real-Time Forecasting
267(28)
Creating the Business Case
268(2)
Resources Needed
268(1)
Data Architecture
269(1)
Data Ingestion
270(3)
GA4 Configuration
270(3)
Data Storage
273(3)
Hosting the Shiny App on Cloud Run
273(3)
Data Modeling
276(3)
Data Activation--A Real-Time Dashboard
279(13)
R Code for the Real-Time Shiny App
280(2)
GA4 Authentication with a Service Account
282(4)
Putting It All Together in a Shiny App
286(6)
Summary
292(3)
10 Next Steps
295(8)
Motivation: How I Learned What Is in This Book
296(2)
Learning Resources
298(4)
Asking for Help
300(1)
Certifications
301(1)
Final Thoughts
302(1)
Index 303
Mark Edmondson is Senior Data Scientist at IIH Nordic A/S. He is also a Google Developer Expert for Google Analytics and Google Cloud, and has consulted for over 15 years helping global brands with their digital marketing strategy. He contributes to the digital marketing community through his blog and open source contributions, focusing on data science and engineering applications with digital marketing data.

Mark has published several data activation proof of concepts over the years that have been popular within the digital marketing industry, including a web app to measure if certain events affected your data in a statistically significant way; using markov chains with Google Analytics data to predict where users will go so as to prefetch web resources and improve page load speed experience; and using streaming web analytics data to BigQuery to create real-time digital analytics dashboards.