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.

The post There are no failed A/B tests: How to ensure every experiment yields meaningful results appeared first on Dynamic Yield.

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.

The post Guidelines for running effective Bayesian A/B tests appeared first on Dynamic Yield.

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.

The post A/A testing and decision making in experimentation appeared first on Dynamic Yield.

An in-depth analysis of the most important technical considerations when comparing client-side vs server-side rendering.

The post Client-side vs server-side A/B testing and personalization appeared first on Dynamic Yield.

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.

The post The impact of A/B tests and personalization on SEO appeared first on Dynamic Yield.

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.

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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.

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A conversation on personalization, conversion optimization and what makes the coconut the most kickass ingredient known to (wo)man with cosmetics retailer Kopari.

The post Building an experimentation growth plan: A Kopari Beauty case study appeared first on Dynamic Yield.

Understand the influence of different conversion attribution configurations on your personalization and A/B testing campaigns.

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Learn how to choose the right traffic allocation method for your A/B tests and avoid common pitfalls, ensuring impactful, valid results.

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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.

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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.

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Learn how multi-armed bandit algorithms conclude experiments earlier than the classic A/B test while making fewer statistical errors on the way.

The post Beyond A/B testing: Multi-armed bandit experiments appeared first on Dynamic Yield.

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.

The post Why reaching and protecting statistical significance is so important in A/B tests appeared first on Dynamic Yield.

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.

The post An introduction to A/B testing and optimization appeared first on Dynamic Yield.