Digital Marketing Analytics Course

Lesson 1 • Ethics and Privacy in Data Collection

Introduces consent frameworks, data minimisation, and user privacy principles. Establishes responsible data practices as a baseline expectation.

Lesson 2 • What Digital Marketing Analytics Means

Defines analytics in a marketing context and distinguishes it from reporting. Establishes vocabulary used throughout the course.

Lesson 3 • Data Types and Collection Methods

Covers first-, second-, and third-party data and primary collection techniques. Grounds students in data sourcing before tool-specific chapters.

Lesson 4 • The Digital Marketing Ecosystem

Maps owned, earned, and paid channels and their data outputs. Provides context for where analytics is applied in later chapters.

Lesson 1 • Filters, Views, and Data Integrity

Demonstrates how to exclude internal traffic, bots, and spam to maintain clean data. Clean data is a prerequisite for reliable analysis in all later chapters.

Lesson 2 • Configuring Goals and Conversions

Teaches destination, duration, pages-per-session, and event goals. Connects tracked actions to the KPIs defined in Chapter 1.

Lesson 3 • Custom Dimensions and Metrics

Extends default data collection with user- and session-scoped custom attributes. Enables the segmentation and audience analysis covered in Chapter 3.

Lesson 4 • Implementing Tracking Code

Covers manual tag placement and tag manager deployment for accurate data capture. Directly enables the reporting work in subsequent sections.

Lesson 1 • Building and Applying Segments

Covers the mechanics of creating, saving, and comparing segments in an analytics platform. Practical segment-building skills are applied in every subsequent analysis chapter.

Lesson 2 • Behavioural Segmentation Techniques

Groups users by on-site actions, frequency, and recency to reveal engagement patterns. Segments created here feed directly into campaign targeting and personalisation.

Lesson 3 • Understanding Audience Reports

Interprets demographic, geographic, and technology reports to profile site visitors. Builds on tracking setup from Chapter 2 to extract actionable audience insights.

Lesson 1 • Multi-Channel Funnel Analysis

Reveals how channels interact across the conversion path using assisted conversion data. Prepares students for the attribution modelling deep dive in Chapter 5.

Lesson 2 • Traffic Source Classification

Explains how analytics platforms classify organic, direct, referral, and paid traffic. Accurate source classification is the foundation of all campaign measurement.

Lesson 3 • Email and Social Campaign Tracking

Applies UTM strategy to email newsletters and social media posts for accurate attribution. Closes the gap between off-site engagement and on-site conversion data.

Lesson 4 • UTM Parameter Strategy

Teaches consistent UTM tagging conventions for campaigns across all channels. Proper tagging ensures clean source data for the attribution analysis in later sections.

Lesson 1 • Attribution Model Fundamentals

Defines last-click, first-click, linear, time-decay, and position-based models. Understanding model mechanics is required before comparing or selecting models.

Lesson 2 • Calculating Marketing ROI and ROAS

Applies revenue and cost data to compute ROI, ROAS, and cost-per-acquisition by channel. Quantifies marketing efficiency for the budget optimisation work ahead.

Lesson 3 • Attribution Reporting for Stakeholders

Structures attribution findings into executive-ready reports with clear budget implications. Connects analytical outputs to strategic decisions covered in Chapter 8.

Lesson 4 • Comparing Models for Channel Decisions

Uses the model comparison tool to reveal how budget allocation changes under each model. Directly informs the ROI calculations and budget recommendations in this chapter.

Lesson 1 • Hypothesis Formation and Test Design

Teaches a structured hypothesis framework and statistical requirements for valid tests. Rigorous design prevents wasted experiments and misleading results.

Lesson 2 • Running A/B and Multivariate Tests

Covers experiment setup, traffic splitting, and quality assurance for A/B and MVT tests. Hands-on test execution builds the skills needed to interpret results accurately.

Lesson 3 • Interpreting and Acting on Test Results

Explains statistical significance, confidence intervals, and practical significance for test outcomes. Ensures students make correct decisions from test data rather than misreading results.

Lesson 4 • Diagnosing Conversion Funnel Drop-offs

Uses funnel reports, exit pages, and behaviour flow to locate where users abandon. Diagnosis precedes hypothesis formation and experiment design.

Lesson 1 • Storytelling with Marketing Data

Structures data narratives with a clear insight, evidence, and recommendation flow. Storytelling skills amplify the attribution and CRO reporting from earlier chapters.

Lesson 2 • Designing Dashboards for Different Audiences

Tailors dashboard layout and metric selection to executive, manager, and analyst audiences. Audience-specific design ensures dashboards drive action rather than confusion.

Lesson 3 • Maintaining and Iterating Dashboards

Covers dashboard audits, metric deprecation, and version control for long-term usability. Ensures dashboards remain accurate and relevant as business goals evolve.

Lesson 4 • Principles of Effective Data Visualisation

Covers pre-attentive attributes, chart selection, and cognitive load reduction for marketing data. These principles govern every visualisation decision in the chapter.

Lesson 1 • Trend Analysis and Forecasting Methods

Applies moving averages, seasonality decomposition, and regression to forecast marketing KPIs. Forecasting skills enable proactive budget planning and goal-setting.

Lesson 2 • Predictive Audience Modelling

Introduces propensity scoring and churn prediction to identify high-value audience segments. Predictive models extend the segmentation skills built in Chapter 3.

Lesson 3 • Building a Marketing Measurement Plan

Creates a structured document linking business objectives to metrics, data sources, and owners. A measurement plan is the strategic foundation for all analytics activity.

Lesson 4 • Leading an Analytics-Driven Marketing Team

Covers analytics governance, data literacy programmes, and cross-functional alignment for scale. Equips students to embed analytics culture across an entire marketing organisation.