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Course

A/B testing & Optimization

This course will teach you the fundamentals of A/B testing and optimization – from basic concepts, common pitfalls, and proven methods, all the way through evaluating and scaling your results. 

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Lessons

A/A testing and decision making in experimentation

Learn what AA testing is, why it's essential before A/B testing, how it validates experimentation accuracy, and helps identify tracking issues early.
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How to analyze and interpret A/B testing results

Learn how to analyze A/B test results, interpret statistical significance, avoid common mistakes, and make confident, data-driven optimization decisions.
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Segmented A/B tests: Avoiding average experiences

Discover why running A/B tests without audience segmentation can limit insights and learn how targeted experiments improve personalization and results.
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An introduction to A/B testing and optimization

Learn the fundamentals of A/B testing, from creating hypotheses and measuring outcomes to making evidence-based optimization decisions.
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Beyond A/B testing: Multi-armed bandit experiments

Learn how contextual bandit optimization uses real-time user context to automatically deliver better experiences and improve conversion performance.
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Client-side vs server-side A/B testing and personalization

Learn how client-side testing enables fast experimentation and personalized experiences while understanding its benefits, limitations, and use cases.
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Understanding conversion attribution scoping in A/B testing

Understand conversion attribution scoping, how attribution windows affect experiment results, and how to measure conversions more accurately with Dynamic Yield.
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Frequentist vs Bayesian approach in A/B testing

Understand Bayesian testing, how it measures experiment outcomes, supports faster optimization, and improves confidence in testing decisions.
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Guidelines for running effective Bayesian A/B tests

Understand the Frequentist approach to A/B testing, including statistical significance, p-values, confidence levels, and experiment interpretation.
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There are no failed A/B tests: How to ensure every experiment yields meaningful results

Learn why every A/B test delivers valuable insights, even without a winning variation, and how to turn experiment outcomes into smarter decisions.
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Choosing the right conversion optimization objective

Learn how to define the right optimization objective for experiments and personalization to align testing efforts with measurable business outcomes.
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Why reaching and protecting statistical significance is so important in A/B tests

Understand statistical significance, why it matters in experimentation, and how to interpret test results with greater confidence before making decisions.
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Tactics for sending high-impact triggered emails

Discover high-impact triggered email tactics that engage customers at key moments, strengthen relationships, and increase conversions through timely messaging.
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Traffic Allocation in A/B Testing Explained

Understand traffic allocation in A/B testing, how visitor distribution affects experiment accuracy, and when to adjust allocation for better outcomes.
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