Customer Engagement Analytics Course

Lesson 1 • Data Sources for Engagement Analytics

Catalogs first-, second-, and third-party data sources relevant to engagement. Prepares learners to evaluate data availability and quality.

Lesson 2 • Defining Customer Engagement

Establishes what customer engagement means across channels and touchpoints. Grounds subsequent analytics work in a shared, precise definition.

Lesson 3 • Core Engagement Metrics

Introduces foundational KPIs used to measure engagement quality and quantity. Provides the measurement vocabulary used throughout the course.

Lesson 4 • Ethics and Data Governance Basics

Covers consent, privacy principles, and responsible data use in analytics. Ensures learners apply ethical standards from the start of any project.

Lesson 1 • Data Ingestion Methods

Covers batch, streaming, and API-based ingestion patterns for engagement data. Learners select the right ingestion method for each data source type.

Lesson 2 • Pipeline Orchestration and Monitoring

Teaches scheduling, dependency management, and alerting for data pipelines. Learners can maintain reliable data flows that support ongoing analytics.

Lesson 3 • Data Quality and Validation

Defines data quality dimensions and methods to detect and fix issues. Ensures analysts trust the data before drawing conclusions.

Lesson 4 • Data Storage and Warehousing

Introduces data warehouse and data lake architectures for engagement data. Connects storage decisions to query performance and analytics flexibility.

Lesson 1 • Hypothesis Generation and Testing

Guides learners from exploratory observations to testable hypotheses. Prepares them for the controlled experimentation covered in later chapters.

Lesson 2 • Data Visualization Principles

Covers chart selection, visual encoding, and storytelling with engagement data. Enables analysts to communicate findings clearly to diverse audiences.

Lesson 3 • Funnel Analysis

Teaches step-by-step conversion funnel construction and drop-off diagnosis. Connects funnel insights to actionable engagement improvement opportunities.

Lesson 4 • Descriptive Statistics for Engagement

Applies measures of central tendency, spread, and distribution to engagement metrics. Builds the statistical intuition needed for all subsequent analytical work.

Lesson 1 • RFM and Value-Based Segmentation

Applies recency, frequency, and monetary analysis to rank customer value. Produces high-value segments that prioritise retention and upsell efforts.

Lesson 2 • Segment Monitoring and Refresh

Establishes processes to track segment stability and update definitions over time. Prevents segment decay from undermining targeting accuracy.

Lesson 3 • Persona Development from Segments

Translates quantitative segments into narrative personas for cross-functional use. Bridges analytics output and marketing or product strategy teams.

Lesson 4 • Segmentation Strategy and Design

Defines segmentation objectives and criteria aligned with business goals. Ensures segments are actionable, measurable, and strategically relevant.

Lesson 1 • Propensity and Conversion Modeling

Estimates the probability that a customer will take a desired action. Supports prioritisation of outreach and personalisation efforts.

Lesson 2 • Next Best Action Prediction

Combines multiple models to recommend the optimal engagement action per customer. Integrates predictive outputs into real-time decisioning systems.

Lesson 3 • Predictive Modelling Fundamentals

Introduces supervised learning concepts and the modeling workflow for engagement outcomes. Establishes the foundation for all predictive techniques in this chapter.

Lesson 4 • Model Validation and Governance

Covers performance monitoring, retraining triggers, and model documentation standards. Ensures models remain accurate and auditable in production.

Lesson 1 • Statistical Significance and Power

Explains p-values, confidence intervals, and statistical power in experiment analysis. Prevents false positives and underpowered tests from misleading decisions.

Lesson 2 • Multivariate and Sequential Testing

Extends A/B testing to multiple variables and adaptive stopping rules. Enables faster and more complex experimentation programs.

Lesson 3 • Experiment Governance and Culture

Establishes processes for experiment documentation, review, and organizational learning. Scales experimentation from individual tests to a systematic program.

Lesson 4 • Quasi-Experimental Methods

Introduces difference-in-differences, regression discontinuity, and synthetic controls. Enables causal inference when randomised experiments are not feasible.

Lesson 1 • Attribution Modeling

Compares rule-based and data-driven attribution models for engagement touchpoints. Enables accurate credit assignment across the customer journey.

Lesson 2 • Cross-Channel Data Integration

Merges engagement data from disparate channels into a unified customer record. Resolves identity across systems to enable holistic analysis.

Lesson 3 • Customer Journey Mapping with Data

Reconstructs actual customer journeys from event-level data to identify friction and delight. Connects journey insights to targeted engagement improvements.

Lesson 4 • Offline and In-Store Engagement Metrics

Integrates point-of-sale, loyalty, and location data into the engagement analytics framework. Completes the omnichannel picture for businesses with physical presence.

Lesson 1 • Executive Dashboards and Reporting

Designs concise, decision-oriented dashboards for senior leadership audiences. Ensures analytics insights drive timely strategic action.

Lesson 2 • Cross-Functional Analytics Collaboration

Establishes workflows for sharing engagement insights with marketing, product, and service teams. Maximizes the organizational impact of analytics outputs.

Lesson 3 • ROI Measurement of Engagement Programs

Quantifies the financial return of engagement initiatives using controlled measurement. Justifies analytics investment and guides resource allocation decisions.

Lesson 4 • Building an Analytics-Driven Culture

Embeds data-driven decision-making into organizational processes and leadership behavior. Sustains long-term competitive advantage through analytics capability.