Lean Analytics: Using Data to Grow Course

Lesson 1 • Key Analytical Concepts and Terms

Covers essential vocabulary—ratios, rates, cohorts, and segments—used throughout the course. Ensures all students share a common analytical language.

Lesson 2 • Data-Driven vs. Data-Informed Culture

Distinguishes between blindly following data and using it as one input among many. Prepares students to build healthier analytical cultures.

Lesson 3 • What Lean Analytics Means

Defines Lean Analytics by contrasting it with traditional analysis approaches. Grounds students in the philosophy before introducing frameworks.

Lesson 1 • Matching Metrics to Business Models

Connects each business model to its critical success metrics. Students practice selecting relevant metrics rather than copying generic dashboards.

Lesson 2 • Choosing a Framework for Your Context

Guides students in adapting or combining frameworks rather than applying them rigidly. Reinforces the OMTM concept within a broader metric architecture.

Lesson 3 • The Pirate Metrics Framework

Introduces the AARRR funnel—Acquisition, Activation, Retention, Revenue, Referral. Students map their own product to each funnel stage.

Lesson 1 • Setting Stage-Appropriate Goals

Connects each stage to specific, measurable targets rather than aspirational statements. Students practice writing testable hypotheses tied to stage goals.

Lesson 2 • The Five Lean Analytics Stages

Introduces Empathy, Stickiness, Virality, Revenue, and Scale as sequential growth stages. Provides the roadmap students use to sequence analytical work.

Lesson 3 • Transitioning Between Stages

Defines the criteria that signal readiness to advance to the next stage. Students learn to avoid the trap of optimising the wrong stage.

Lesson 1 • Instrumentation and Event Tracking

Covers the design of event schemas and the placement of tracking calls in products. Connects proper instrumentation to trustworthy downstream analysis.

Lesson 2 • Qualitative Data Collection Methods

Introduces interviews, surveys, and usability tests as complements to quantitative data. Students learn when qualitative data resolves what numbers cannot explain.

Lesson 3 • Privacy and Ethical Data Practices

Covers consent, data minimisation, and user rights as non-negotiable collection constraints. Students align their tracking plans with ethical and regulatory expectations.

Lesson 1 • Cohort Analysis in Depth

Teaches construction and interpretation of cohort retention tables and curves. Students identify retention patterns that aggregate metrics conceal.

Lesson 2 • Avoiding Analytical Pitfalls

Addresses survivorship bias, Simpson's paradox, and correlation-causation confusion. Students apply diagnostic questions to challenge their own analytical conclusions.

Lesson 3 • Funnel Analysis and Drop-Off Diagnosis

Applies funnel visualisation to identify where users abandon conversion flows. Students prioritise which funnel steps to optimise based on impact and effort.

Lesson 1 • Forming Testable Hypotheses

Teaches the structure of a well-formed hypothesis linking a change to a measurable outcome. Students convert vague ideas into falsifiable experiment designs.

Lesson 2 • A/B Testing Fundamentals

Covers randomisation, control and treatment groups, and sample size calculation. Students design an A/B test for a realistic growth scenario.

Lesson 3 • Interpreting and Acting on Results

Teaches how to read experiment results, including inconclusive and negative outcomes. Students practice writing experiment summaries that drive clear next actions.

Lesson 1 • Acquisition Channel Analytics

Evaluates paid, organic, viral, and partnership channels using cost and quality metrics. Students calculate channel efficiency and identify the best-performing sources.

Lesson 2 • Building a Growth Backlog

Teaches prioritisation frameworks—ICE, RICE—to rank growth experiments by impact and effort. Students produce a prioritised backlog ready for sprint planning.

Lesson 3 • Revenue Optimisation Techniques

Applies pricing analytics, upsell triggers, and LTV modelling to maximise revenue per user. Students calculate LTV:CAC ratio and identify revenue expansion opportunities.

Lesson 4 • Activation and Onboarding Optimisation

Analyses the critical first-use experience using activation rate and time-to-value metrics. Students redesign onboarding flows based on drop-off and engagement data.

Lesson 1 • Embedding Analytics in Team Workflows

Integrates metric reviews into sprint ceremonies, product reviews, and hiring decisions. Students map analytics touchpoints across their team's existing workflow.

Lesson 2 • Dashboard Design Principles

Applies data visualisation best practices to build dashboards that communicate clearly. Students critique and redesign a poorly structured dashboard.

Lesson 3 • Communicating Insights to Stakeholders

Teaches narrative framing, executive summaries, and data storytelling for non-technical audiences. Students translate a complex analysis into a one-page stakeholder brief.