Visão Geral
Este Curso Google Analytics 4 for Product Analytics and Growth é focado no uso do Google Analytics 4 (GA4) para Product Analytics e Growth, capacitando profissionais a entender profundamente o comportamento do usuário, adoção de funcionalidades, retenção, engajamento e crescimento do produto. O treinamento ensina como estruturar métricas orientadas a produto, analisar jornadas completas, validar hipóteses de growth, medir experimentos e apoiar decisões estratégicas com dados confiáveis e acionáveis.
Conteúdo Programatico
Module 1: Product Analytics and Growth Fundamentals
- Product analytics concepts
- Growth frameworks (AARRR, North Star Metric)
- Product vs marketing analytics
- Metrics hierarchy
Module 2: Event Modeling for Products
- Product-oriented event design
- User actions and properties
- Naming conventions and standards
- Measurement plan creation
Module 3: User Journey and Funnel Analysis
- Acquisition to activation funnels
- Feature adoption funnels
- Drop-off analysis
- Path exploration techniques
Module 4: Engagement and Retention Analysis
- Engagement metrics
- Retention and churn analysis
- Cohort analysis
- Stickiness and usage frequency
Module 5: Feature Adoption and Product Usage
- Feature tracking strategies
- Usage segmentation
- Power users identification
- Product optimization insights
Module 6: Growth Experiments and Hypothesis Validation
- Growth experiment design
- Measuring experiments in GA4
- A/B testing analysis concepts
- Learning loops and iteration
Module 7: Conversion and Monetization Analysis
- Product-led conversion metrics
- Subscription and freemium models
- Revenue attribution for products
- LTV and cohort revenue
Module 8: Advanced Explorations and BigQuery
- Advanced explorations
- GA4 BigQuery export for product analysis
- SQL queries for product metrics
- Custom analysis use cases
Module 9: Product Dashboards and Storytelling
- Product KPI dashboards
- North Star Metric visualization
- Stakeholder reporting
- Data storytelling for product teams
Module 10: Governance and Best Practices for Product Analytics
- Data quality and validation
- Privacy and consent considerations
- Scaling product analytics
- Real-world product analytics scenarios