Are you a product manager, growth lead, marketing manager, or founder looking to make smarter product decisions using data without relying on a data science team?
In this hands-on course, you’ll learn how to track, interpret, and act on product analytics to improve user experience, retention, and revenue. Whether you're launching a new product or optimizing an existing one, this course gives you the frameworks, metrics, and thinking tools you need to turn user behavior into actionable insights.
We'll go through some real life examples and use cases from products in the iGaming industry.
We’ll cover essential concepts like funnels, retention, churn, LTV, and segmentation, and guide you through practical exercises using real-world data patterns. You’ll learn how to define what to track, make sense of messy spreadsheets, and prioritize decisions that move your product forward.
No coding or advanced math required, just a curiosity for product data and a desire to build better experiences.
By the end of this course, you’ll be able to:
Understand and apply core product analytics concepts
Set up event-based tracking and meaningful metrics
Identify growth opportunities through retention and funnel analysis
Segment users and translate data into product strategy
The course includes:
Part 1: Product Analytics Foundations
Unit 1: What is Product Analytics?
Why do Product Analytics Matter?
Clarity and purpose
Uncovers new insights
Helps you figure out how to not let your product sink
What are the “right” data points to measure?
The “low” performing game
How can metric results influence the product strategy?
Bias in interpretation of data
Unit 2:
Metrics vs Mission, Why they matter and North Star Thinking
Unit 3: Measuring the Entire Journey
Going through the Funnel
Measuring the journey
Getting to the juice
Part 2: Product Metrics (Acquisition, Usage, Retention, Cost & Monetization)
Unit 4: User Data
Installs, First Launches, Sign-ups
Conversion Rate
Unit 5: Revenue Metrics
DAU/MAU Ratio
ARPU
LTV
CAC
Unit 6: User Retention and Stickiness
Retention curves
Revenue retention
Event-based retention
Churn analysis
Reactivation strategies
The cost of poor retention
UX and value examples
Unit 7: Monetization and Metrics
Pricing models and revenue streams
IAPs, Ads, Paywalls, Subscriptions
Monetization and UX tradeoffs
Experimentation and A/B testing
Monetization examples
Unit 8: Distribution and Channels
CAC across channels
Channel competition
Measuring product-channel fit
Key metrics per channel
Part 3: Behavioral and Experience Metrics
Unit 9: Behavioral Metrics
Feature usage
Product and feature pairing
Sentiment analysis
Emotion detection (experimental)
Location analysis (experimental)
User interviews and surveys
Segmentation
Device specs and UI/UX analysis
