Web Analyst Course

Lesson 1 • Key Performance Indicators

Teaches how to select and define KPIs aligned to business objectives. Students practise translating goals into measurable metrics.

Lesson 2 • The Web Analytics Ecosystem

Maps the tools, stakeholders, and data pipelines that form a typical analytics stack. Connects analyst responsibilities to business decision-making.

Lesson 3 • Data Privacy and Ethical Measurement

Covers consent frameworks, user privacy rights, and ethical data collection practices. Grounds all subsequent tracking work in responsible standards.

Lesson 1 • E-commerce and Conversion Tracking

Covers transaction data layers and goal configuration for revenue measurement. Enables accurate attribution of sales and lead events.

Lesson 2 • Tag Management Fundamentals

Explains how tag managers control script deployment without code releases. Students configure containers, triggers, and variables from scratch.

Lesson 3 • Tracking Quality Assurance

Provides systematic methods to validate that tags fire correctly and data is clean. Prevents downstream reporting errors caused by faulty implementation.

Lesson 4 • Implementing an Analytics Platform

Walks through property creation, data stream setup, and base tag deployment. Connects tag manager output to an analytics reporting interface.

Lesson 1 • Custom Dimensions and Metrics

Extends default data models with user-defined attributes tied to hits, sessions, or users. Enables segmentation beyond out-of-the-box dimensions.

Lesson 2 • How Analytics Platforms Process Data

Describes the hit-to-session processing pipeline and how platforms aggregate raw data. Builds understanding needed to interpret reports accurately.

Lesson 3 • Data Sampling and Unsampled Data

Explains when sampling occurs and how it distorts analysis. Students apply techniques to retrieve unsampled datasets for accurate reporting.

Lesson 1 • Audience and Acquisition Reports

Covers demographic, geographic, and channel reports that describe who visits and how they arrive. Connects traffic sources to downstream behaviour.

Lesson 2 • Segmentation Techniques

Applies segments to isolate specific user groups and compare behaviour across cohorts. Transforms aggregate data into targeted insights.

Lesson 3 • Conversion and Goal Reporting

Measures goal completions, funnel drop-off, and revenue performance. Quantifies the business impact of user behaviour.

Lesson 4 • Scheduled Reports and Dashboards

Builds automated reporting workflows and stakeholder dashboards for ongoing monitoring. Reduces manual effort while maintaining data visibility.

Lesson 1 • Attribution Models Explained

Compares last-click, first-click, linear, time-decay, and data-driven attribution models. Students select models appropriate to business goals and sales cycles.

Lesson 2 • Multi-Channel Funnel Analysis

Examines the full conversion path across multiple touchpoints before a goal is completed. Reveals the supporting role of upper-funnel channels.

Lesson 3 • Attribution Model Comparison

Uses model comparison tools to quantify how credit shifts between channels under different models. Informs channel investment and budget reallocation.

Lesson 4 • Channel Grouping and Source Analysis

Explains how platforms classify traffic into channels and how to customise groupings. Enables accurate comparison of organic, paid, and referral traffic.

Lesson 1 • Identifying Optimisation Opportunities

Uses funnel analysis, heatmaps, and session data to locate high-impact friction points. Prioritises pages and flows with the greatest revenue potential.

Lesson 2 • A/B and Multivariate Testing

Covers test design, traffic splitting, and statistical validity for controlled experiments. Students configure and launch tests using experimentation platforms.

Lesson 3 • Interpreting Test Results

Teaches statistical significance, confidence intervals, and practical significance for test evaluation. Prevents premature or incorrect conclusions from experiment data.

Lesson 4 • Hypothesis Development

Structures observations into testable hypotheses using evidence-based frameworks. Ensures tests are grounded in data rather than opinion.

Lesson 1 • Behavioural Cohort Analysis

Groups users by acquisition date or first action to track retention and lifetime value over time. Reveals how different cohorts respond to product and marketing changes.

Lesson 2 • Predictive Audience Modeling

Introduces platform-generated predictive metrics such as purchase probability and churn likelihood. Students activate predictive audiences for marketing use cases.

Lesson 3 • RFM and Value-Based Segmentation

Segments users by recency, frequency, and monetary value to identify high-value and at-risk customers. Enables targeted retention and upsell campaigns.

Lesson 1 • Analytics Governance and Documentation

Establishes naming conventions, change logs, and ownership policies for a scalable analytics practice. Prevents data inconsistency as teams and tools evolve.

Lesson 2 • Structuring an Analytics Report

Defines the components of a high-impact analytics report: executive summary, findings, and recommendations. Teaches logical flow from data to decision.

Lesson 3 • Communicating Insights to Stakeholders

Adapts analytical communication style to technical and non-technical audiences. Builds credibility and drives action through clear, confident delivery.

Lesson 4 • Measurement Planning and Strategy

Builds a measurement plan that connects business objectives to KPIs, segments, and targets. Aligns analytics work to organisational strategy from the start.