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A/B Testing & Optimization Course
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.
15 Lessons 
Lesson 1
Read ArticleAn introduction to A/B testing and optimization
A comprehensive guide to A/B testing, explaining the differences between A/B and multivariate testing, how to conduct tests in a structured and progressive way, and the thought process behind choosing the right experiment.
Lesson 2
Read ArticleA/A testing and decision making in experimentation
A/A testing is meant to check that the underlying system is running properly. In this post, we’ll cover some of subtle issues to consider as you run them.
Lesson 3
Read ArticleWhy reaching and protecting statistical significance is so important in A/B tests
Delve deeper into the mechanics of “classic” A/B testing, become familiar with the actual meaning of statistical significance, and discover the pitfalls threatening the validity of your test results.
Lesson 4
Read ArticleChoosing the right traffic allocation in A/B testing
Learn how to choose the right traffic allocation method for your A/B tests and avoid common pitfalls, ensuring impactful, valid results.
Lesson 5
Read ArticleUnderstanding conversion attribution scoping in A/B testing
Understand the influence of different conversion attribution configurations on your personalization and A/B testing campaigns.
Lesson 6
Read ArticleChoosing the right conversion optimization objective
Conversion optimization can be a very powerful process, but it’s easy to get carried away by the methodology. Here’s how to choose the right optimization objective.
Lesson 7
Read ArticleFrequentist vs. Bayesian approach in A/B testing
The industry is moving toward the Bayesian framework as it is a simpler, less restrictive, more reliable, and more intuitive approach to A/B testing.
Lesson 8
Read ArticleGuidelines for running effective Bayesian A/B tests
In this post, we describe the basic ideas behind Bayesian statistics and how they feed into business decisions you will need to make at the end of a test.
Lesson 9
Read ArticleBeyond A/B testing: Multi-armed bandit experiments
Learn how multi-armed bandit algorithms conclude experiments earlier than the classic A/B test while making fewer statistical errors on the way.
Lesson 10
Read ArticleClient-side vs server-side A/B testing and personalization
An in-depth analysis of the most important technical considerations when comparing client-side vs server-side rendering.
Lesson 11
Read ArticleSegmented A/B tests: Avoiding average experiences
One of the biggest mistakes a company makes when A/B testing is selecting an average audience. Instead, they should be making segmentation an essential part of their test design — here’s how.
Lesson 12
Read ArticleThe impact of A/B tests and personalization on SEO
Read about Google’s official SEO advice on A/B or multivariate testing, and how to minimize the possible impact of A/B testing on SEO.
Lesson 13
Read ArticleThere are no failed A/B tests: How to ensure every experiment yields meaningful results
With a comprehensive testing methodology and a renewed perspective toward experiment analysis, marketers can start gleaning meaningful insights from every A/B test, no matter the outcome.
Lesson 14
Read ArticleHow to analyze and interpret A/B testing results
Analyzing A/B testing results is one of the most important stages of an experiment. But it’s also the least talked about. Here’s how to do it right.
Lesson 15
Read ArticleBuilding an experimentation growth plan: A Kopari Beauty case study
A conversation on personalization, conversion optimization and what makes the coconut the most kickass ingredient known to (wo)man with cosmetics retailer Kopari.