Marketing Analytics Foundation Course

Lesson 1 • Key Metrics and KPIs

Introduces the most critical marketing performance indicators and explains how to select KPIs aligned to business objectives.

Lesson 2 • Data Sources in Marketing

Surveys first-, second-, and third-party data sources used in marketing. Students learn to evaluate source quality and relevance before analysis.

Lesson 3 • The Marketing Funnel and Data

Maps standard funnel stages to measurable data signals. Connects funnel thinking to the metrics students will track throughout the course.

Lesson 4 • The Analyst's Role and Workflow

Outlines the end-to-end analytics workflow from question framing to insight delivery. Positions the analyst as a strategic partner, not just a report generator.

Lesson 1 • Web Analytics Setup

Covers configuring a web analytics platform to capture meaningful site data. Students learn to define goals, filters, and views that produce clean, usable datasets.

Lesson 2 • Tracking and Tagging Fundamentals

Explains how pixels, tags, and tracking codes capture user behaviour across digital channels. Provides the technical foundation for all subsequent data collection work.

Lesson 3 • Building a Marketing Data Stack

Surveys the tools and integrations that form a modern marketing data infrastructure. Students evaluate stack components against business size and analytics maturity.

Lesson 4 • CRM Data and Customer Records

Addresses how CRM systems store and structure customer data for marketing use. Connects CRM records to campaign targeting and lifecycle analysis.

Lesson 1 • Dashboard Design Principles

Teaches layout, hierarchy, and chart selection for effective marketing dashboards. Students design dashboards that communicate insights at a glance.

Lesson 2 • Benchmarking and Trend Analysis

Introduces period-over-period comparisons, industry benchmarks, and trend identification. Students contextualise performance data against meaningful reference points.

Lesson 3 • Exploratory Data Analysis for Marketers

Applies EDA techniques to marketing datasets to surface patterns and anomalies. Builds the habit of understanding data before drawing conclusions.

Lesson 4 • Reporting Cadence and Stakeholder Needs

Aligns reporting frequency and format to different stakeholder audiences. Students learn to tailor depth and detail to executive, tactical, and operational consumers.

Lesson 1 • Segment Activation and Targeting

Covers how to push defined segments into ad platforms, email tools, and CRM workflows. Students connect segmentation outputs to live campaign execution.

Lesson 2 • RFM Analysis

Teaches recency, frequency, and monetary value scoring to rank and prioritise customers. RFM outputs feed directly into retention and reactivation campaigns.

Lesson 3 • Clustering and Behavioural Segmentation

Introduces k-means clustering and behavioural grouping to discover natural audience segments. Students interpret cluster outputs and translate them into marketing actions.

Lesson 4 • Segmentation Strategy and Logic

Establishes the strategic rationale for segmentation and the criteria used to define meaningful groups. Connects segmentation decisions to campaign and budget allocation.

Lesson 1 • Integrated Campaign Reporting

Combines cross-channel data into a unified campaign performance view. Students build reports that show total campaign impact rather than siloed channel results.

Lesson 2 • Content and SEO Performance

Measures organic search visibility, content engagement, and on-page performance. Students connect content metrics to pipeline and revenue outcomes.

Lesson 3 • Email Marketing Analytics

Analyses open rates, click-through rates, deliverability, and list health metrics. Students diagnose email performance issues and apply data-driven fixes.

Lesson 4 • Social Media Analytics

Evaluates reach, engagement, and audience growth across social channels. Students distinguish between vanity metrics and metrics tied to business outcomes.

Lesson 1 • Incrementality and Lift Testing

Covers holdout tests and geo-based experiments to measure true incremental lift. Students design tests that isolate marketing's causal contribution to outcomes.

Lesson 2 • Budget Optimisation Using Attribution Data

Translates attribution and MMM outputs into actionable budget reallocation recommendations. Students build a data-driven case for shifting spend across channels.

Lesson 3 • Marketing Mix Modeling Concepts

Introduces MMM as a statistical approach to measuring the aggregate impact of marketing spend. Students learn when MMM is preferable to MTA and what inputs it requires.

Lesson 4 • Multi-Touch Attribution in Practice

Applies multi-touch attribution to real campaign data to reveal channel contribution. Students interpret MTA outputs and adjust budget recommendations accordingly.

Lesson 1 • Demand Forecasting for Campaigns

Applies time-series forecasting to predict future demand, traffic, and revenue. Students use forecasts to set realistic campaign targets and budget plans.

Lesson 2 • Model Evaluation and Iteration

Teaches precision, recall, AUC, and RMSE to assess model quality. Students learn to diagnose model weaknesses and improve performance through iteration.

Lesson 3 • Lead Scoring and Propensity Models

Develops propensity-to-convert models that rank leads by purchase likelihood. Students integrate scores into CRM and sales workflows to prioritise outreach.

Lesson 4 • Predictive Analytics Foundations

Introduces the predictive modelling workflow and the types of problems it solves in marketing. Students distinguish regression, classification, and forecasting use cases.

Lesson 1 • Building an Experimentation Culture

Addresses the organisational and process changes needed to sustain continuous testing. Students design frameworks that make experimentation a team habit.

Lesson 2 • Analytics-Driven Marketing Strategy

Connects analytics outputs to strategic planning cycles, OKRs, and resource allocation. Students translate data insights into strategic recommendations for leadership.

Lesson 3 • Multivariate Testing and Personalisation

Extends A/B testing to multivariate and personalisation experiments across channels. Students design tests that optimise multiple elements simultaneously.

Lesson 4 • A/B Testing Design and Execution

Covers hypothesis formation, sample size calculation, and test execution for marketing experiments. Students run valid tests that produce reliable, actionable results.