Product Analytics & Metrics

Course Info

Date: Apr-07-2025

Length: 1 Week

City: Kuala Lumpur

Fees: 3,990

Type: In Classroom

Available Dates

  • Jan-06-2025

    Kuala Lumpur

  • Apr-07-2025

    Kuala Lumpur

  • July-07-2025

    Kuala Lumpur

  • Oct-06-2025

    Kuala Lumpur

Dates in Other Venues

  • Dec-30-2024

    Amsterdam

  • Jan-06-2025

    Singapore

  • Jan-06-2025

    Istanbul

  • Jan-06-2025

    Paris

  • Jan-06-2025

    Barcelona

  • Jan-06-2025

    Amsterdam

  • Jan-06-2025

    London

  • Feb-03-2025

    Dubai

  • Mar-03-2025

    London

  • Apr-07-2025

    Barcelona

  • Apr-07-2025

    Amsterdam

  • Apr-07-2025

    Paris

  • Apr-07-2025

    Istanbul

  • Apr-07-2025

    Singapore

  • Apr-07-2025

    Dubai

  • May-05-2025

    London

  • June-02-2025

    Dubai

  • July-07-2025

    London

  • July-07-2025

    Istanbul

  • July-07-2025

    Barcelona

  • July-07-2025

    Singapore

  • July-07-2025

    Amsterdam

  • July-07-2025

    Paris

  • Aug-04-2025

    Dubai

  • Sep-01-2025

    London

  • Oct-06-2025

    Singapore

  • Oct-06-2025

    Barcelona

  • Oct-06-2025

    Dubai

  • Oct-06-2025

    Istanbul

  • Oct-06-2025

    Paris

  • Oct-06-2025

    Amsterdam

  • Nov-03-2025

    London

  • Dec-01-2025

    Dubai

Course Details

Course Outline

5 days course

Introduction to Product Analytics

  • Defining product analytics and its significance in product management
  • Understanding the process of product analytics and what should be completed at each stage:

  •          Creating questions based on product goal
  •          Mapping user journeys
  •          Identifying key user events and related data
  •          Creating a data governance framework
  •          Selecting KPIs and metrics
  •          Reporting the analytics results

  • Differentiating between predictive analytics and prescriptive analytics
  • Discovering the common analytics frameworks for structuring the analysis:

  •          Pirate Metrics Framework (AARRR)
  •          Retention-First Framework (RARRA)

  • Determining the importance of developing measurable products

Data & Metrics

  • Identifying the types of product data:

  •         Source-aligned data products
  •         Consumer-aligned data products
  •         Aggregated data products

  • Distinguishing between transactional data and behavioral data
  • Exploring different data collection methods and best practices
  • Defining product metrics and its types:

  •         Lagging indicator
  •         Leading indicator

  • Overview of metrics categories based on user lifecycle:

  •         Acquisition metrics
  •         Activation metrics
  •         Engagement metrics
  •         Retention metrics
  •         Monetization metrics
  •         North Star metrics

Product Analytics Techniques

  • Defining customer segmentation analysis and cohort analysis
  • Understanding customer lifetime value (CLV) metrics to identify high-value customer segments
  • Describing the Customer Segmentation RFM Analysis
  • Exploring the types of cohort analysis:

  • Acquisition cohorts
  • Behavioral cohorts

  • Describing how cohort analysis is used to increase retention and decrease churn

Product Analytics Techniques

  • Defining retention analysis and funnel analysis
  • Exploring Retention metrics:

  •        Retention Rate
  •        Repeat Purchase Rate
  •        Churn Rate
  •         Aha Moment

  • Understanding the Funnel analysis including:

  •        Conversion and drop-off rates
  •        Segmentation and analytical techniques
  •         Funnel optimization

  • Understanding the North Star metric and how it works for guiding the team
  • Utilizing KPI trees to visualize the relationship between KPI and business goals

 

Product Experimentation & Analytics Best Practices

  • Understanding the meaning of product experimentation and scientific method
  • Exploring the techniques to design effective A/B testing
  • Discussing factors to consider when interpreting A/B testing results

  •          Sample size
  •          Test duration
  •          Conversion rate
  •          Internal and external factors
  •          Significance level

  • Discussing the essential aspects of a data governance process for product analytics
  • Exploring the best practices to implement the data-driven culture within the product team   

 

Course Video