Get started with Feature Experimentation

This course will provide an overview of Optimizely’s Experimentation methodology. Learn to set experimentation goals in alignment with corporate objectives and design metrics that measure what matters.

This course will teach you the ins and outs of various data sources and how you can use them to craft meaningful problem statements that lead you to data-driven solutions. We'll also cover the all-important P-S-R method for generating hypotheses.

This course will walk you through the process of scoring your test ideas, using the score to prioritize them, and show you how to visualize your experimentation project plan.

This course will help you understand the architecture of Feature Experimentation to help you code flags and variables efficiently and plan for multiple uses, leading to actionable results. Gain awareness of how testing in your company's architecture may vary based on system layout and learn to frame your experimentation strategies accordingly. Explore using the test options, Feature Management and Targeted Delivery functions. Select the appropriate SDK or Optimizely Agent based on your environment Learn to tag users effectively for accurate test results using User IDs. Follow the setup guide using a minimalistic login app to practice and share Feature Experimentation with others in your company.

This course covers how to use the preview tool and quality assurance best practices. Then, we'll look at how to check the design and functionality of your experiments, actually launching your experiments, and managing experiments post-launch.

This course covers our data presentation tools, including the Results page that allows you to analyze metrics even while the experiment is running. We'll also provide a foundational understanding of the Optimizely Stats Engine and show you how to read data and trends to save time and money even before an experiment has achieved results.

This course covers what to do when the experiment concludes and results are achieved. We look how to act on winning, losing, and inconclusive results. You'll understand how to use iteration and implement what was learned in your previous Experiment Cycle, helping you keep your idea backlog full and fresh.