Ecommerce Analyst Course

Lesson 1 • The E-commerce Data Landscape

Covers the types of data generated across online retail touchpoints. Establishes the scope of analyst responsibilities within a digital commerce operation.

Lesson 2 • Core E-commerce KPIs

Defines the essential performance metrics used to evaluate store health. Connects each metric to a specific business decision or operational outcome.

Lesson 3 • Data Quality and Reliability Basics

Introduces common data quality issues specific to e-commerce platforms. Students learn to identify and flag unreliable data before analysis begins.

Lesson 1 • Session and User Identification

Explains how sessions, users, and devices are identified across visits. Accurate user identification underpins cohort and retention analysis in later chapters.

Lesson 2 • Tag Management Fundamentals

Explains how tag management systems deploy and control tracking scripts. Directly enables accurate data collection for all subsequent analysis work.

Lesson 3 • Tracking Audits and QA Processes

Provides a systematic approach to validating that tracking is complete and accurate. Students produce an audit checklist applicable to any e-commerce property.

Lesson 1 • Cart and Checkout Abandonment Analysis

Focuses on diagnosing why users abandon carts and checkout flows. Findings directly feed into A/B testing and UX improvement prioritisation.

Lesson 2 • Funnel Visualization and Drop-off Analysis

Teaches construction and interpretation of conversion funnels from raw event data. Connects funnel stages to specific page types and user actions.

Lesson 3 • Search and Discovery Behavior

Analyses on-site search queries and product discovery patterns to surface demand signals. Connects search behaviour to catalogue gaps and merchandising decisions.

Lesson 4 • Path Analysis and Navigation Flows

Maps the actual routes users take through a site before converting or abandoning. Uncovers unexpected navigation patterns that standard funnel views miss.

Lesson 1 • RFM Analysis for Customer Valuation

Applies recency, frequency, and monetary scoring to rank and group customers. RFM outputs feed directly into retention and reactivation campaign targeting.

Lesson 2 • Segmentation Strategy and Frameworks

Introduces demographic, behavioural, and value-based segmentation approaches. Establishes criteria for choosing the right segmentation model for a given business question.

Lesson 3 • Cohort Analysis and Retention Curves

Builds acquisition cohorts and tracks their behaviour over time to measure retention. Cohort curves reveal product-market fit signals and lifecycle health.

Lesson 1 • Incrementality and Holdout Testing

Introduces incrementality measurement to isolate the true causal impact of marketing. Students design holdout experiments to validate attribution model outputs.

Lesson 2 • Multi-Touch Attribution in Practice

Applies multi-touch attribution to real customer journey data across channels. Students compare channel performance rankings under different attribution assumptions.

Lesson 3 • Attribution Model Fundamentals

Explains how credit for conversions is assigned across touchpoints in different models. Establishes the trade-offs between simplicity and accuracy in attribution.

Lesson 4 • Paid Channel Performance Analysis

Evaluates paid search, display, and social channel efficiency using cost and revenue data. Connects spend data to attributed revenue for ROAS and efficiency scoring.

Lesson 1 • Product Performance Metrics

Defines the key metrics for evaluating individual product and category performance. Connects product-level data to revenue contribution and margin impact.

Lesson 2 • Inventory and Availability Analytics

Links inventory availability data to lost sales and conversion rate suppression. Students quantify the revenue impact of stockouts and overstock situations.

Lesson 3 • Category and Assortment Analysis

Evaluates category-level performance and identifies gaps or redundancies in the assortment. Findings guide ranging, delisting, and new product introduction decisions.

Lesson 1 • Statistical Foundations for Testing

Introduces the statistical concepts required to interpret A/B test results correctly. Students apply significance testing and confidence intervals to conversion data.

Lesson 2 • Analysing and Communicating Results

Teaches structured analysis of completed experiments and result communication to stakeholders. Connects statistical outputs to business impact estimates for decision-making.

Lesson 3 • Experiment Design Principles

Covers hypothesis formation, metric selection, and sample size planning for A/B tests. Proper design prevents the most common sources of invalid experiment results.

Lesson 4 • Advanced Experimentation Techniques

Introduces multivariate testing, sequential testing, and Bayesian approaches for mature programmes. Prepares students to scale experimentation beyond simple A/B comparisons.

Lesson 1 • Dashboard Design Principles

Applies data visualisation best practices to e-commerce reporting contexts. Students design dashboards that surface actionable signals without cognitive overload.

Lesson 2 • Analytical Roadmap and Prioritisation

Teaches analysts to build and manage a prioritised backlog of analytical projects. Connects analytical output to business value and resource constraints.

Lesson 3 • Insight Narrative and Storytelling

Structures analytical findings into a clear problem-evidence-recommendation narrative. Effective storytelling increases the likelihood that insights drive action.