As marketing data grows, spreadsheets break down. In this course, you’ll learn how to design structured data models that support accurate, scalable reporting across campaigns, channels, and customer data. You’ll begin by understanding how data models solve common problems like inconsistent reporting and duplicated data. You’ll learn the difference between fact and dimension tables, and how to design star and snowflake schemas that organize marketing data for efficient analysis. Next, you’ll apply these concepts to build your own marketing data model. You’ll define tables, primary and foreign keys, and relationships that connect campaign performance, customer data, and channel activity into a single, reliable system. You’ll also learn how to translate business goals into measurable outcomes by building a KPI framework. Using a funnel-based approach, you’ll map objectives like awareness, conversion, and retention to specific metrics, data sources, and targets, ensuring every report answers a clear business question. By the end of this course, you’ll be able to design data models that scale with your organization and create KPI frameworks that turn raw data into actionable insights. These skills are essential for analysts working in marketing analytics, business intelligence, and data-driven organizations.
What you'll learn
Understand the purpose of data models in marketing analytics
Differentiate between fact and dimension tables
Design star and snowflake schemas for data organization
Build a marketing data model with defined tables and relationships
Create a KPI framework aligned with business goals