Move beyond reporting and learn how to optimize marketing performance using experimentation, forecasting, and advanced analytics workflows. In this course, you’ll design A/B tests, evaluate statistical significance, create forecasting models, and use GA4 insights to improve campaign execution and measurement quality. You’ll learn how to structure meaningful A/B tests with clear hypotheses, success metrics, sample-size planning, and evaluation criteria. You’ll also create forecasting models that project future campaign performance and budget requirements using historical marketing data and trend analysis. In addition, you’ll analyze GA4 implementations to identify tracking gaps, troubleshoot instrumentation issues, and improve campaign attribution accuracy. Throughout the course, you’ll use optimization frameworks to recommend tactical and strategic marketing improvements across multiple channels. By the end of the course, you’ll be able to execute data-driven optimization strategies that combine experimentation, forecasting, and analytics into actionable marketing recommendations.
What you'll learn
Design A/B tests with clear hypotheses, success metrics, and appropriate sample size calculations
Evaluate statistical significance in marketing experiments
Create forecasting models to project campaign performance and budget needs using historical data
Analyze GA4 implementations to identify tracking gaps and instrumentation issues
Troubleshoot campaign attribution problems in analytics platforms
Build optimization frameworks that combine experimentation and forecasting into actionable recommendations
Course objectives
Execute data-driven optimization strategies across multiple marketing channels
Structure meaningful experiments with proper evaluation criteria
Improve campaign measurement quality through better analytics implementation
Make tactical and strategic marketing improvements based on experimental and forecasting insights