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A/B testing and SEO: How to navigate pitfalls and maximize results

Search Engine Land

A/B testing can be a great tool for enhancing website functionality, user experience and conversions. However, when done at an enterprise scale, A/B testing poses unique challenges that can inadvertently undermine a site’s SEO performance.

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A/B testing mistakes PPC marketers make and how to fix them

Search Engine Land

Have you ever implemented the top-performing variation from a PPC ad copy A/B test but don’t actually see any improvement? A/B testing works – you just need to avoid some common pitfalls. Not running tests long enough to get sufficient data. And you also want to design your A/B tests differently.

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Confidence Intervals: A Guide for A/B Testing

ConversionXL

Confidence intervals are a standard output of many free and paid A/B testing tools. Most A/B test reports contain one or more interval estimates. If you’re in charge of preparing and presenting those reports, it’s essential. Most commonly, we work with differences in conversion rates or average revenue per user.

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How To Visualize A/B Test Results

ConversionXL

You may be wondering, “why should I make my own visualization of my A/B test results?” ” Because the A/B testing tools in the market already provide you all the necessary tables and graphs, right? They tell you when an A/B test is significant and what the expected uplift is. So why bother?

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Beyond “One Size Fits All” A/B Tests

ConversionXL

If you’re invested in improving your A/B testing game, you’ve probably read dozens of articles and discussions on how to plan and run A/B tests. One widely used testing platform solidifies this idea by imposing account-wide significance levels. Costs in A/B testing ROI analysis. Let’s see why.

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How to Minimize A/B Test Validity Threats

ConversionXL

You have an A/B testing tool, a well-researched hypothesis and a winning test with 95% confidence. There are factors threatening the validity of your test, without you even realizing it. Quite simply, validity threats are the factors that threaten the validity of your A/B test results. Not so fast.

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How to Make More Money With Bayesian A/B Test Evaluation

ConversionXL

The traditional (and most used) approach to analyzing A/B tests is to use a so-called t-test , which is a method used in frequentist statistics. In this blogpost, I will argue why a post-hoc Bayesian test evaluation is a better evaluation method than a frequentist one for growing your business. We Need More Winners!