Data marketing training

Lesson 1 • What Data Marketing Actually Is

Defines data marketing and distinguishes it from traditional marketing. Establishes the conceptual baseline for all subsequent chapters.

Lesson 2 • Types of Marketing Data

Categorises behavioural, demographic, transactional, and contextual data. Students learn which data types answer which marketing questions.

Lesson 3 • Data Quality and Reliability

Introduces accuracy, completeness, timeliness, and consistency as quality dimensions. Poor data quality is linked directly to flawed marketing outcomes.

Lesson 4 • Ethical and Regulatory Foundations

Covers consumer privacy principles, consent frameworks, and data governance basics. Students understand compliance obligations before handling real data.

Lesson 1 • Data Collection Methods and Sources

Surveys online and offline collection techniques including tracking pixels, forms, and CRM imports. Students select the right method for each data need.

Lesson 2 • Data Governance and Documentation

Establishes naming conventions, data dictionaries, and ownership policies. Proper governance ensures data remains trustworthy as teams and sources scale.

Lesson 3 • Customer Data Platforms and CRMs

Compares CDPs and CRMs as central repositories for unified customer profiles. Students understand when to use each system and how they integrate.

Lesson 4 • Tag Management and Event Tracking

Explains how tag managers deploy tracking code and how events capture user actions. Students configure basic event schemas for marketing measurement.

Lesson 1 • Audience Activation Across Channels

Explains how segments are pushed to ad platforms, email tools, and personalisation engines. Students map segments to the right activation channel.

Lesson 2 • Behavioural and Predictive Audiences

Moves beyond historical behaviour to intent signals and propensity modelling. Students understand how predictive scores improve targeting precision.

Lesson 3 • Segmentation Principles and Criteria

Covers the four classic segmentation bases and criteria for a viable segment. Students evaluate whether a proposed segment is measurable, accessible, and substantial.

Lesson 4 • RFM and Value-Based Segmentation

Teaches recency, frequency, and monetary scoring to rank customer value. Students build RFM models and translate scores into tiered marketing strategies.

Lesson 1 • Building a Measurement Framework

Links business objectives to marketing KPIs through a structured goal hierarchy. Students draft a measurement plan before any campaign launches.

Lesson 2 • Statistical Significance in Marketing

Introduces p-values, confidence intervals, and sample size requirements for marketing tests. Students avoid false conclusions from underpowered or misread results.

Lesson 3 • Web and Campaign Analytics

Covers traffic analysis, funnel metrics, and campaign performance reporting. Students interpret standard analytics reports and identify optimisation opportunities.

Lesson 4 • Dashboard Design and Reporting

Teaches principles of effective dashboard layout, chart selection, and narrative reporting. Students build a marketing performance dashboard aligned to a measurement plan.

Lesson 1 • Running and Monitoring Tests

Addresses test duration, peeking problems, and mid-test quality checks. Students manage live tests without introducing bias or stopping too early.

Lesson 2 • Experimentation Strategy and Culture

Frames testing as a systematic business process rather than a one-off tactic. Students build a prioritised test roadmap aligned to business goals.

Lesson 3 • A/B Test Design Fundamentals

Covers control and variant setup, randomisation, and isolation of variables. Students design tests that produce clean, interpretable results.

Lesson 4 • Analysing and Interpreting Results

Applies statistical significance and practical significance to test outcomes. Students distinguish a real winner from noise and document findings properly.

Lesson 1 • Customer Journey Mapping with Data

Combines qualitative journey maps with quantitative path analysis to reveal real behaviour. Students identify high-impact moments where personalisation adds value.

Lesson 2 • Email and Lifecycle Personalisation

Applies segmentation and behavioural triggers to email sequences and lifecycle programmes. Students build a triggered email flow using real data conditions.

Lesson 3 • Personalisation Frameworks and Rules

Introduces rule-based and algorithmic personalisation approaches and their trade-offs. Students design a personalisation logic tree for a defined use case.

Lesson 4 • Omnichannel Personalisation Orchestration

Coordinates personalisation signals across email, paid, web, and offline channels. Students design a consistent cross-channel experience using a unified customer profile.

Lesson 1 • Bidding Strategies and Automation

Compares manual, rule-based, and algorithmic bidding strategies and their data requirements. Students select and configure a bidding approach for a given campaign goal.

Lesson 2 • Creative Data and Ad Testing

Uses performance data to evaluate creative elements and run structured ad tests. Students build a creative testing framework tied to audience and placement.

Lesson 3 • Audience Targeting in Paid Channels

Applies first-party segments, lookalikes, and retargeting lists to paid campaigns. Students match audience strategy to funnel stage and channel capability.

Lesson 4 • Paid Media Data Fundamentals

Maps the data signals available in search, social, display, and programmatic channels. Students understand how each platform uses data to serve and price ads.

Lesson 1 • Predictive Analytics for Marketing

Applies regression, classification, and clustering models to marketing prediction problems. Students frame a business question as a predictive modelling task.

Lesson 2 • Building a Data-Driven Marketing Culture

Addresses organisational change, team structure, and incentive design for data adoption. Students develop a change management plan to embed data practices across marketing teams.

Lesson 3 • Marketing Mix Modelling Fundamentals

Introduces MMM as a top-down method for measuring channel contribution and diminishing returns. Students interpret MMM outputs to guide budget allocation decisions.

Lesson 4 • Revenue Forecasting and Scenario Planning

Builds bottom-up and top-down revenue forecasts using historical marketing data. Students create scenario models that quantify the impact of budget changes.