Product Analytics: Data-Driven Growth and Retention

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 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