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Optimizely Knowledge Base

Studying for the Optimizely X Strategy Foundations Exam

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Congratulations! By deciding to study for the exam, you are one step closer to becoming certified with our Optimizely X Strategy Foundations exam!

This study guide consists of dozens of articles that go into depth on all the concepts that you need to know to pass the Optimizely X Strategy Foundations exam.  Please feel free to also browse around our community and read interesting discussions by customers like you. 

This study guide is made to be taken in the suggested sequence, but feel free to jump around if you already feel comfortable with certain topics. Good luck!

Ideation

Key concepts:

  • Experiment goals
  • Using a goal tree
  • Primary versus secondary metrics
  • Using analytics reports to generate hypotheses
  • Hypothesis structure
  • Prioritization framework
  • Macro versus micro conversions
  1. Improve metrics that matter
  2. Using a goal tree
  3. Primary versus secondary metrics
  4. Using analytics to generate hypotheses
  5. Design an effective hypothesis
  6. From research to hypothesis creation
  7. Create a basic prioritization framework
  8. Macro versus micro conversions

Planning

Key concepts:

  • Optimization Methodology
  • Building an optimization culture
  • Build an effective optimization team
  • Create a roadmap
  • Experiment length
  • Minimum detectable effect (MDE)
  1. Optimization methodology
  2. Blog: Optimization culture
  3. Build an effective optimization team
  4. Create a roadmap
  5. How to run a long test
  6. Minimum detectable effect

Analysis

Key concepts:

  • Statistical significance
  • Interpret your results
  • Understanding winning, losing and inconclusive results
  • Take action on your results
  • How to share results with stakeholders and your team
  1. Statistical significance
  2. Interpret your results
  3. Take action on your results
  4. Why experiments fail to reach significance
  5. How to share results

We also recommend taking our self-paced learning paths in order to dive deeper into each of these topics. 

Access the "Generate Ideas for Experimentation" learning path here.

Access the "Plan and Prioritize Experiments" learning path here.