Product Analytics Course

Lesson 1 • Key Metrics and Measurement Concepts

Introduces metrics taxonomy: inputs, outputs, leading, and lagging indicators. Connects metric selection to product goals and business outcomes.

Lesson 2 • What Product Analytics Means

Defines product analytics and distinguishes it from marketing and business analytics. Establishes the scope and purpose that frames the entire course.

Lesson 3 • The Product Analytics Workflow

Maps the end-to-end process from question formulation to insight delivery. Gives students a repeatable framework applied throughout the course.

Lesson 1 • Designing a Tracking Plan

Teaches how to define, document, and prioritize events before implementation. A solid tracking plan prevents data debt and misaligned analysis downstream.

Lesson 2 • Implementing and Validating Tracking

Covers the technical steps to deploy tracking and verify data quality post-launch. Ensures students can confirm that collected data matches intended behavior.

Lesson 3 • Identity Resolution and User Stitching

Addresses how anonymous and authenticated sessions are linked to a single user profile. Accurate identity resolution is essential for lifecycle and cohort analysis.

Lesson 1 • User Flow and Path Analysis

Explores unstructured navigation paths to reveal unexpected user behavior. Complements funnel analysis by uncovering routes not anticipated in product design.

Lesson 2 • Funnel Analysis

Teaches construction and interpretation of conversion funnels across key user journeys. Funnel analysis is the primary tool for identifying where users abandon critical flows.

Lesson 3 • Retention and Churn Analysis

Measures how well a product retains users over time using cohort retention curves. Retention is the foundational signal of product-market fit and long-term growth.

Lesson 1 • Principles of User Segmentation

Defines segmentation dimensions: demographic, behavioral, technographic, and lifecycle stage. Proper segmentation prevents misleading averages from hiding critical subgroup differences.

Lesson 2 • Building and Reading Cohort Tables

Teaches construction of acquisition and behavioral cohort tables and how to read diagonal trends. Cohort tables reveal whether product changes improve outcomes for new users.

Lesson 3 • Cohort Comparison and Benchmarking

Compares cohorts across time periods and acquisition channels to isolate improvement signals. Benchmarking cohorts against each other reveals the impact of product and growth changes.

Lesson 1 • Interpreting and Acting on Results

Teaches how to read experiment results, handle inconclusive tests, and make ship decisions. Connects statistical output to product judgment and stakeholder communication.

Lesson 2 • Statistical Foundations for A/B Testing

Covers p-values, confidence intervals, statistical power, and sample size calculation. Students gain the statistical literacy needed to design valid tests and avoid false conclusions.

Lesson 3 • Running Experiments in Practice

Addresses randomization, traffic splitting, guardrail metrics, and experiment monitoring. Practical execution details prevent instrumentation errors that invalidate test results.

Lesson 4 • Advanced Experimentation Techniques

Introduces multivariate testing, holdout groups, and sequential testing methods. Extends foundational A/B skills to more complex experimental scenarios in mature products.

Lesson 1 • Acquisition Funnel Analytics

Analyzes top-of-funnel traffic through activation to identify the highest-leverage acquisition improvements. Connects marketing channel data to in-product behavior for full-funnel visibility.

Lesson 2 • Activation and Aha Moment Analysis

Identifies the specific actions correlated with long-term retention, known as the aha moment. Optimizing activation is the highest-leverage early-lifecycle intervention for growth.

Lesson 3 • Growth Accounting Framework

Decomposes net growth into new, retained, resurrected, and churned users each period. Growth accounting reveals which lever—acquisition, retention, or resurrection—drives or limits growth.

Lesson 4 • Revenue Growth and Expansion Analytics

Tracks MRR movements, expansion revenue, and net revenue retention to measure monetization health. Revenue analytics closes the loop between product usage and business outcomes.

Lesson 1 • Monetization Funnel Analysis

Maps the conversion path from free or trial users to paying customers and identifies friction points. Monetization funnel data directly informs pricing page, trial, and paywall design.

Lesson 2 • Customer Lifetime Value Fundamentals

Defines LTV conceptually and mathematically, covering simple and predictive formulations. LTV is the anchor metric for evaluating acquisition spend and retention investment.

Lesson 3 • Pricing Analytics and Willingness to Pay

Uses behavioral and survey data to estimate price sensitivity and optimal price points. Data-driven pricing decisions reduce revenue leakage from under- or over-pricing.

Lesson 1 • Building a Product Metrics Framework

Designs a coherent metrics hierarchy aligned to company strategy, from north star to input metrics. A well-structured framework prevents metric proliferation and misaligned team priorities.

Lesson 2 • Dashboards and Reporting for Product Teams

Designs operational dashboards that surface the right signals at the right cadence for product teams. Effective dashboards reduce time-to-insight and support faster product decisions.

Lesson 3 • Fostering a Data-Driven Product Culture

Addresses organizational habits, processes, and incentives that embed analytics into product workflows. Culture change is the final barrier to realizing the full value of analytics investment.