Course Overview

Welcome to the Product Analytics & Metrics course by Transformentors Academy. This practical 5-day programme is designed to help professionals strengthen their product growth strategies through data-driven decision-making and effective product analytics practices.

Throughout the course, participants will explore the fundamentals of product analytics, including data types, product metrics, and analytics processes across different stages of product development. The programme also covers advanced analytical techniques such as customer segmentation, cohort analysis, retention analysis, funnel analysis, and product experimentation.

Participants will also learn how to apply A/B testing techniques, interpret product data, and utilize analytics insights to improve product performance and business outcomes. Through practical exercises and real-world examples, the course equips professionals with the skills needed to make informed decisions and drive product success.

Agenda

Day — 1 Introduction to Product Analytics

• Defining product analytics and its significance in product management
• Understanding the product analytics process and activities at each stage:
  • Creating questions based on product goals
  • Mapping user journeys
  • Identifying key user events and related data
  • Creating a data governance framework
  • Selecting KPIs and metrics
  • Reporting analytics results
• Differentiating between predictive analytics and prescriptive analytics
• Discovering common analytics frameworks for structuring analysis:
  • Pirate Metrics Framework (AARRR)
  • Retention-First Framework (RARRA)
• Understanding the importance of developing measurable products

Day — 2 Data & Metrics

• Identifying different types of product data:
  • Source-Aligned Data Products
  • Consumer-Aligned Data Products
  • Aggregated Data Products
• Distinguishing between transactional data and behavioural data
• Exploring data collection methods and best practices
• Defining product metrics and their types:
  • Lagging Indicators
  • Leading Indicators
• Understanding metric categories based on the user lifecycle:
  • Acquisition Metrics
  • Activation Metrics
  • Engagement Metrics
  • Retention Metrics
  • Monetization Metrics
  • North Star Metrics

Day — 3 Product Analytics Techniques

• Defining customer segmentation analysis and cohort analysis
• Understanding Customer Lifetime Value (CLV) metrics to identify high-value customer segments
• Describing Customer Segmentation RFM Analysis
• Exploring the types of cohort analysis:
  • Acquisition Cohorts
  • Behavioural Cohorts
• Understanding how cohort analysis helps increase retention and reduce customer churn

Day — 4 Product Analytics Techniques

• Defining retention analysis and funnel analysis
• Exploring retention metrics:
  • Retention Rate
  • Repeat Purchase Rate
  • Churn Rate
  • Aha Moment
• Understanding funnel analysis, including:
  • Conversion and Drop-Off Rates
  • Segmentation and Analytical Techniques
  • Funnel Optimization
• Understanding the North Star Metric and its role in guiding product teams
• Utilizing KPI Trees to visualize the relationship between KPIs and business goals

Day — 5 Product Experimentation & Analytics Best Practices

• Understanding the meaning of product experimentation and the scientific method
• Exploring techniques for designing effective A/B testing experiments
• Discussing factors to consider when interpreting A/B testing results:
  • Sample Size
  • Test Duration
  • Conversion Rate
  • Internal and External Factors
  • Significance Level
• Understanding the essential aspects of a data governance process for product analytics
• Exploring best practices for implementing a data-driven culture within product teams

Learning Outcomes

By the end of this course, participants will be able to:

• Gain a solid understanding of product analytics and its processes
• Transform user data into actionable insights to support product success
• Structure product analytics using frameworks such as AARRR and RARRA
• Understand different types of metrics and their practical applications
• Implement customer segmentation, cohort analysis, retention analysis, and funnel analysis
• Utilize the North Star Metric to guide product development and growth
• Conduct product experimentation and A/B testing effectively
• Apply essential data governance practices to secure and protect product data
• Foster a data-driven culture within product teams and organisations

Who Should Attend

This course is ideal for professionals looking to strengthen their expertise in product analytics and data-driven product management, including:

• Product Managers
• Data Analysts
• Business Strategists
• Product Owners
• Marketing Professionals
• Business Analysts
• UX/UI Designers
• Entrepreneurs
• Quality Assurance Specialists
• Project Managers
• Anyone interested in data analytics and product management