Applying Data Analytics in Marketing Course

Lesson 1 • Key Metrics and KPIs in Marketing

Introduces standard marketing performance indicators and explains how to select metrics aligned to business goals. Prevents vanity-metric traps early in the course.

Lesson 2 • The Marketing Analytics Workflow

Outlines the end-to-end process from business question to actionable insight. Provides a repeatable framework students apply throughout the course.

Lesson 3 • What Marketing Analytics Actually Is

Defines marketing analytics, distinguishes it from general business intelligence, and maps its scope. Establishes the conceptual baseline for all subsequent chapters.

Lesson 1 • Digital Tracking and Tag Management

Explains how pixels, cookies, and tags capture user behavior across digital touchpoints. Grounds students in the mechanics behind web and app analytics data.

Lesson 2 • Survey and Primary Research Data

Covers survey design, sampling methods, and primary research collection for marketing insights. Complements digital tracking with direct consumer input.

Lesson 3 • Building a Marketing Data Stack

Introduces the concept of a composable marketing data stack and the role of each layer. Prepares students to evaluate and select tools appropriate to their organization.

Lesson 4 • CRM and Marketing Automation Data

Examines how CRM platforms and automation tools generate and store customer interaction data. Links data management to campaign execution and personalization.

Lesson 1 • Segmentation Through Exploratory Analysis

Uses cross-tabulation and grouping to identify natural customer segments in raw data. Lays the groundwork for formal segmentation models in later chapters.

Lesson 2 • Anomaly Detection and Data Auditing

Teaches methods to spot outliers, data spikes, and tracking errors in marketing datasets. Protects analytical conclusions from corrupted or misleading data.

Lesson 3 • Descriptive Statistics in Marketing Context

Covers mean, median, variance, and distribution shape as applied to marketing metrics. Builds numerical literacy needed for all analytical work ahead.

Lesson 4 • Correlation and Relationship Detection

Introduces correlation coefficients and scatter analysis to find relationships between marketing variables. Establishes the distinction between correlation and causation.

Lesson 1 • Persona Development from Analytical Segments

Translates quantitative segment profiles into narrative marketing personas. Bridges the gap between data output and creative campaign strategy.

Lesson 2 • RFM Analysis for Customer Scoring

Teaches recency, frequency, and monetary scoring to rank customers by value and engagement. Produces immediately deployable segments for retention and upsell campaigns.

Lesson 3 • Lookalike and Predictive Audience Modeling

Covers lookalike modeling logic and propensity scoring to expand high-value audiences. Prepares students for advanced predictive applications in later chapters.

Lesson 4 • Clustering Techniques for Audience Discovery

Introduces k-means and hierarchical clustering to find natural groupings in customer data. Extends segmentation beyond predefined rules to data-driven discovery.

Lesson 1 • Channel Performance Analysis

Measures efficiency and ROI across paid, owned, and earned channels using standardized metrics. Enables apples-to-apples comparison of diverse marketing investments.

Lesson 2 • Cross-Device and Cross-Channel Tracking

Addresses identity resolution challenges when customers switch devices and channels. Ensures attribution models reflect true customer journeys.

Lesson 3 • Multi-Touch Attribution in Practice

Applies multi-touch attribution to real campaign data to assign fractional credit across touchpoints. Directly informs channel investment decisions.

Lesson 4 • Attribution Reporting and Budget Decisions

Translates attribution outputs into budget reallocation recommendations for marketing leadership. Closes the loop between analysis and strategic spending decisions.

Lesson 1 • Experimental Design for Marketers

Covers hypothesis formation, control and treatment group setup, and randomization principles. Prevents common experimental design errors that invalidate test results.

Lesson 2 • Testing Roadmap and Prioritization

Introduces frameworks for prioritizing and sequencing a continuous testing program. Scales individual test skills into an organizational experimentation culture.

Lesson 3 • Statistical Significance and Sample Size

Explains p-values, confidence intervals, and minimum detectable effect for marketing tests. Ensures students run tests long enough to produce reliable conclusions.

Lesson 4 • A/B and Multivariate Testing Methods

Distinguishes A/B from multivariate testing and guides element selection for each approach. Expands the testing toolkit beyond simple two-variant experiments.

Lesson 1 • Customer Lifetime Value Calculation

Covers historical and predictive CLV formulas and their inputs from marketing data. Connects CLV to acquisition budget caps and segment investment decisions.

Lesson 2 • Predictive Modeling Concepts for Marketers

Introduces supervised learning logic, feature selection, and model evaluation without deep math. Demystifies predictive modeling for non-data-scientist marketers.

Lesson 3 • Lead Scoring and Conversion Prediction

Applies predictive scoring to rank leads by conversion likelihood using CRM and behavioral data. Improves sales and marketing alignment through shared scoring logic.

Lesson 4 • Churn Prediction and Retention Modeling

Builds churn prediction models using behavioral signals to identify at-risk customers. Directly enables proactive retention campaigns before customers disengage.

Lesson 1 • Continuous Optimization Frameworks

Establishes a structured review-and-optimize cycle that embeds analytics into ongoing marketing operations. Transforms one-time analysis into a sustained performance improvement engine.

Lesson 2 • Building a Marketing Analytics Strategy

Defines the components of an analytics strategy aligned to business objectives and team capabilities. Moves students from tactical analysis to strategic analytics leadership.

Lesson 3 • Marketing Mix Modeling Fundamentals

Introduces marketing mix modeling as a strategic tool for measuring aggregate channel contribution. Complements attribution models with a top-down measurement perspective.

Lesson 4 • Measuring Marketing's Business Impact

Connects marketing analytics outputs to revenue, profit, and growth metrics valued by the C-suite. Equips students to demonstrate and defend marketing's financial contribution.