Marketing Analytics Course
Master the full spectrum of marketing analytics — from data collection and customer segmentation to predictive modeling and budget optimization. This course gives you the frameworks, tools, and hands-on techniques that data-driven marketers use to make smarter decisions and prove real business impact. Whether you're analyzing campaign performance or building a marketing mix model, you'll finish ready to lead with data.
What you will learn:
You'll start by building a solid foundation in marketing data sources, key metrics, and the analytics workflow. From there, you'll move into customer segmentation, churn prediction, and lifetime value modeling using real quantitative methods. You'll learn how to design and analyze A/B tests, apply multi-touch attribution, and build marketing mix models that justify budget decisions. The course also covers SQL for marketing data extraction, data visualization best practices, and how AI tools are reshaping analyst workflows. By the end, you'll have the skills to measure, optimize, and communicate marketing performance at a strategic level.
How you study in practice Marketing Analytics Course
How you practice Marketing Analytics Course
For companies that want to train their team
With Dedika for Business, the course includes exercises and examples tailored to your own business and the way your company needs.
Course content
8 Chapters • 32 LessonsDuration between 4 and 360 hours (you decide)
Chapter 1HideHide detailsSee detailsFoundations of Marketing Analytics
Foundations of Marketing Analytics
Lesson 1 • What Marketing Analytics Means
Defines marketing analytics and distinguishes it from general business analytics. Establishes the scope and vocabulary used throughout the course.
Lesson 2 • Core Marketing Metrics and KPIs
Introduces the most critical marketing performance indicators and their formulas. Connects metric selection to specific business objectives.
Lesson 3 • The Analytics Workflow
Outlines the end-to-end process from business question to actionable insight. Provides a repeatable framework applied in every subsequent chapter.
Lesson 4 • Data Sources in Marketing
Maps the landscape of first-, second-, and third-party data available to marketers. Shows how source quality affects analytical reliability.
Chapter 2HideHide detailsSee detailsData Collection and Management
Data Collection and Management
Lesson 1 • Tracking and Tagging Fundamentals
Explains how digital tracking technologies capture user behavior across channels. Directly enables accurate data collection for all downstream analysis.
Lesson 2 • Data Integration Across Channels
Covers methods for combining data from multiple marketing platforms into a unified view. Resolves the fragmentation problem that distorts cross-channel analysis.
Lesson 3 • Privacy and Consent in Data Collection
Addresses consumer privacy principles and consent frameworks that govern marketing data use. Ensures analytical practices remain ethical and compliant.
Lesson 4 • Data Cleaning and Validation
Teaches systematic techniques for detecting and correcting errors in raw marketing datasets. Clean data is the prerequisite for every analytical method in this course.
Chapter 3HideHide detailsSee detailsDescriptive Analytics and Reporting
Descriptive Analytics and Reporting
Lesson 1 • Exploratory Data Analysis for Marketers
Applies statistical summaries and visualizations to uncover patterns in marketing datasets. Builds the habit of interrogating data before drawing conclusions.
Lesson 2 • Segmentation and Cohort Analysis
Groups customers by shared attributes or behavior to reveal differences in performance. Cohort analysis tracks how groups evolve over time.
Lesson 3 • Building Marketing Dashboards
Covers dashboard design principles and tool selection for ongoing performance monitoring. Connects visualization choices to the decisions dashboards must support.
Lesson 4 • Reporting Cadence and Storytelling
Establishes how to structure recurring reports and present findings to non-technical stakeholders. Bridges the gap between analysis and organizational action.
Chapter 4HideHide detailsSee detailsCustomer Analytics and Segmentation
Customer Analytics and Segmentation
Lesson 1 • Churn Prediction and Retention Analytics
Builds models that identify customers at risk of leaving before they disengage. Enables proactive retention campaigns grounded in data.
Lesson 2 • Behavioral Clustering Techniques
Uses unsupervised machine learning to group customers by behavioral similarity without predefined labels. Reveals natural audience segments for targeting.
Lesson 3 • Customer Journey Mapping with Data
Uses behavioral event data to reconstruct the paths customers take from awareness to purchase. Identifies friction points and optimization opportunities.
Lesson 4 • Customer Lifetime Value Modeling
Builds CLV models from historical purchase data to forecast long-term customer revenue. CLV underpins budget allocation and acquisition decisions covered later.
Chapter 5HideHide detailsSee detailsCampaign Analytics and Attribution
Campaign Analytics and Attribution
Lesson 1 • Multi-Touch Attribution in Practice
Applies attribution models to real campaign data and reconciles conflicting channel signals. Produces actionable budget reallocation recommendations.
Lesson 2 • Attribution Model Fundamentals
Compares rule-based attribution models and explains the assumptions behind each. Establishes why model choice materially changes budget decisions.
Lesson 3 • Incrementality and Lift Testing
Measures the true causal lift of a campaign by comparing exposed and unexposed groups. Distinguishes correlation from causation in campaign measurement.
Lesson 4 • Data-Driven Attribution
Introduces algorithmic attribution that uses actual conversion paths to assign credit. Requires the CLV and data integration skills from earlier chapters.
Chapter 6HideHide detailsSee detailsExperimentation and A/B Testing
Experimentation and A/B Testing
Lesson 1 • Interpreting and Acting on Results
Translates statistical outputs into business decisions and documents learnings for future tests. Closes the loop between experimentation and strategy.
Lesson 2 • Running A/B and Multivariate Tests
Walks through the operational steps of launching and monitoring live experiments. Covers both simple A/B and more complex multivariate designs.
Lesson 3 • Statistical Foundations for Testing
Covers the statistical concepts required to interpret A/B test results correctly. Builds on descriptive analytics from Chapter 3 to add inferential reasoning.
Lesson 4 • Hypothesis Formation and Test Design
Teaches how to convert a marketing question into a testable hypothesis with clear success criteria. Proper design prevents the most common testing errors.
Chapter 7HideHide detailsSee detailsPredictive Modeling for Marketing
Predictive Modeling for Marketing
Lesson 1 • Regression Models for Marketing Forecasting
Uses linear and logistic regression to predict continuous and binary marketing outcomes. Extends the statistical foundation built in the experimentation chapter.
Lesson 2 • Classification Models for Propensity Scoring
Builds models that score each customer's likelihood to convert, churn, or respond. Propensity scores directly feed targeting and personalization strategies.
Lesson 3 • Demand Forecasting and Budget Planning
Applies time-series and regression methods to forecast sales volume and marketing demand. Outputs feed directly into budget allocation decisions.
Lesson 4 • Model Evaluation and Deployment
Covers train-test splits, cross-validation, and the steps needed to move a model into production. Ensures models remain accurate after deployment.
Chapter 8HideHide detailsSee detailsMarketing Mix Modeling and Budget Optimization
Marketing Mix Modeling and Budget Optimization
Lesson 1 • Budget Optimization Techniques
Uses MMM outputs to find the spend allocation that maximizes revenue within budget constraints. Directly answers the core marketing investment question.
Lesson 2 • Communicating MMM Insights to Leadership
Translates complex model outputs into executive-ready narratives and investment recommendations. Applies the storytelling skills from Chapter 3 at a strategic level.
Lesson 3 • Building and Calibrating an MMM
Walks through model specification, estimation, and calibration against known results. Produces a validated model ready for optimization.
Lesson 4 • Marketing Mix Modeling Fundamentals
Introduces the statistical structure of MMM and the data inputs required. Positions MMM as the strategic complement to the attribution models in Chapter 5.
Your valid completion certificate
This course is for you:
Marketing manager: wants to ground campaign decisions in rigorous data analysis.
Digital marketing specialist: ready to move beyond platform dashboards and surface metrics.
Business analyst: shifting focus toward marketing-specific measurement and optimization problems.
Startup growth lead: needs to prove channel ROI with limited budget and resources.
Product marketer: looking to quantify audience behavior and lifecycle value systematically.
Career changer: entering marketing analytics from a adjacent field like finance or operations.
What our students say
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