Course Outline

Module 1: Introduction to Marketing Experimentation

• Why experimentation matters in modern marketing

• Types of experiments: A/B, split, multivariate, bandit tests

• Overview of testing platforms and integrations
  Case Study: A/B testing success story in e-commerce

Module 2: Foundations of A/B Testing

• Hypothesis building and defining success metrics

• Sample size calculation and test duration

• Avoiding biases and false positives
  Practical Exercise: Build a test hypothesis framework

Module 3: Designing Experiments for Marketing Channels

• Email A/B tests (subject lines, timing, personalization)

• Website & landing page tests (layout, CTA, copy)

• Ad creative tests (images, videos, text)
  Exercise: Design a cross-channel A/B test

Module 4: Implementing Tests with Tools

• Using Google Optimize, Optimizely, and VWO

• Integrating with GA4 and CRM platforms

• Setting up test variations and randomization
  Practical: Run a sample landing page test

Module 5: Statistical Methods in A/B Testing

• Confidence intervals, p-values, statistical significance

• Frequentist vs Bayesian approaches

• Common pitfalls and how to avoid false conclusions
  Case Study: Misinterpretation of test results in a real campaign

Module 6: Multivariate Testing & Advanced Experimentation

• Designing and running multivariate tests

• Multi-armed bandit testing

• Personalization and adaptive experiments
  Practical: Design a multivariate test for a pricing page

Module 7: Data Analysis & Visualization

• Analyzing results in Excel, R, Python, and Power BI

• Visualization techniques for test outcomes

• Communicating results to stakeholders
  Exercise: Build a results dashboard for A/B test outcomes

Module 8: Scaling Experimentation Across Marketing

• Creating an experimentation roadmap

• Prioritization frameworks (ICE, PIE)

• Embedding experimentation in campaign workflows
  Case Study: Building a culture of testing at a global brand

Module 9: AI & Automation in Experimentation

• AI-driven test optimization

• Predictive personalization with machine learning

• Automating A/B tests with marketing automation tools
  Practical: Experimentation using AI-powered platforms

Module 10: Capstone Project – End-to-End Experiment

• Design, implement, and analyze a complete A/B test

• Apply statistical methods and create a results report

• Present insights and recommendations to executives
  Capstone Project: Run a full marketing experiment and build a results dashboard