Marketing Analytics for Customers Course

Lesson 1 • Customer Data Landscape Overview

Maps the categories of customer data available to marketers and their sources. Connects data types to specific marketing decisions.

Lesson 2 • Key Metrics and KPIs

Introduces the most critical marketing performance indicators and how they are calculated. Grounds students in the numbers they will analyze throughout the course.

Lesson 3 • What Marketing Analytics Means

Defines marketing analytics, its scope, and how it differs from general business intelligence. Establishes the vocabulary used throughout the course.

Lesson 1 • Privacy, Consent, and Data Governance

Addresses the ethical and regulatory requirements for collecting and using customer data. Ensures students build compliant analytics pipelines from the start.

Lesson 2 • Data Quality and Validation

Covers techniques for assessing and improving the accuracy, completeness, and consistency of customer data. Poor data quality is the leading cause of misleading analytics outputs.

Lesson 3 • Customer Identity and Data Unification

Explains how to link customer records across channels into a single profile. Enables accurate cross-channel analysis covered in later chapters.

Lesson 1 • Principles of Customer Segmentation

Establishes why segmentation improves marketing effectiveness and what makes a segment actionable. Provides the evaluative criteria used to judge all segmentation outputs.

Lesson 2 • Rule-Based and RFM Segmentation

Teaches manual and recency-frequency-monetary segmentation as practical starting points. These methods require no advanced modeling and deliver immediate business value.

Lesson 3 • Validating and Activating Segments

Covers methods for testing segment stability and translating segments into campaign targeting. Closes the loop between analytical output and marketing execution.

Lesson 4 • Behavioral and Psychographic Segmentation

Extends segmentation beyond demographics to attitudes, motivations, and usage patterns. Produces richer segments that drive more personalized marketing strategies.

Lesson 1 • Attribution Modeling Fundamentals

Explains how credit for conversions is assigned across touchpoints and why model choice matters. Lays the groundwork for advanced attribution covered in Chapter 6.

Lesson 2 • Mapping the Customer Journey

Introduces journey mapping as an analytical tool grounded in behavioral data. Connects qualitative journey maps to quantitative funnel metrics.

Lesson 3 • Path Analysis and Sequence Mining

Explores techniques for discovering the most common and most valuable customer paths. Reveals non-obvious routes to conversion that standard funnels miss.

Lesson 1 • Historical CLV Calculation Methods

Walks through aggregate and individual-level historical CLV formulas using transaction data. Builds on data management skills from Chapter 2.

Lesson 2 • CLV Concepts and Business Impact

Defines CLV and explains why it is the central metric for customer-centric marketing. Connects CLV to acquisition cost, retention spend, and profitability decisions.

Lesson 3 • Applying CLV to Marketing Decisions

Translates CLV outputs into actionable acquisition, retention, and win-back strategies. Demonstrates how CLV changes resource allocation across customer segments.

Lesson 1 • A/B and Multivariate Testing

Teaches the mechanics of running A/B and multivariate tests across email, ads, and landing pages. Connects test design to the KPIs established in Chapter 1.

Lesson 2 • Statistical Significance and Confidence

Explains p-values, confidence intervals, and effect sizes in plain marketing language. Equips students to avoid false positives and premature test conclusions.

Lesson 3 • Incrementality and Holdout Testing

Introduces holdout groups and geo-based experiments to measure true incremental lift. Addresses the limitations of last-click and correlation-based measurement.

Lesson 4 • Campaign Reporting and Iteration

Covers how to document test results, share findings, and feed learnings into future campaigns. Builds a continuous improvement culture grounded in data.

Lesson 1 • Budget Optimization with Attribution Data

Applies attribution and MMM outputs to reallocate media budgets for maximum return. Connects analytical findings to strategic investment decisions.

Lesson 2 • Limitations of Rule-Based Attribution

Revisits attribution models from Chapter 4 and exposes their structural biases. Motivates the need for data-driven and econometric alternatives.

Lesson 3 • Media Mix Modeling Fundamentals

Introduces econometric media mix modeling as a channel-level measurement approach. Explains how MMM complements user-level attribution models.

Lesson 1 • Model Deployment and Monitoring

Covers the operationalization of predictive models in marketing systems and their ongoing performance management. Ensures models remain accurate as customer behavior evolves.

Lesson 2 • Propensity and Upsell Models

Covers models that predict purchase likelihood, upsell readiness, and product affinity. Enables precision targeting that improves campaign ROI.

Lesson 3 • Next-Best-Action Frameworks

Introduces decision frameworks that combine multiple model outputs to select the optimal customer interaction. Represents the strategic apex of the analytics skill set built in this course.

Lesson 4 • Predictive Modeling Foundations

Establishes the supervised learning framework and the model development lifecycle. Builds on CLV and segmentation concepts from Chapters 3 and 5.