Articles on Conversion optimization

Articles on conversion rate optimization a.k.a. CRO with primary focus on e-commerce and online businesses and e-mail marketing optimization.

Statistical Significance in A/B Testing – a Complete Guide

Statistical Significance P Value

The concept of statistical significance is central to planning, executing and evaluating A/B (and multivariate) tests, but at the same time it is the most misunderstood and misused statistical tool in internet marketing, conversion optimization, landing page optimization, and user testing. This article attempts to lay it out in as plain English as possible: covering […] Read more…

Also posted in A/B testing, Multiple variations testing, Statistical significance, Statistics | Tagged , , , , , , , , ,

Futility Stopping Rules in AGILE A/B Testing

Stopping for Lack of Effect (Futility)

In this article we continue our examination of the AGILE statistical approach to AB testing with a more in-depth look into futility stopping, or stopping early for lack of positive effect (lack of superiority). We’ll cover why such rules are helpful and how they help boost the ROI of A/B testing, why a rigorous statistical rule […] Read more…

Also posted in A/B testing, AGILE A/B testing, Statistics | Tagged , , , , , ,

Efficient AB Testing with the AGILE Statistical Method

AGILE AB Testing

Don’t we all want to run tests as quickly as possible, reaching results as conclusive and as certain as possible? Don’t we all want to minimize the number of users we send to an inferior variant and to implement a variant with positive lift as quickly as possible? Don’t we all want to get rid of […] Read more…

Also posted in A/B testing, AGILE A/B testing, Statistics | Tagged , , , , , , , , , , , ,

Improving ROI in A/B Testing: the AGILE AB Testing Approach

After many months of statistical research and development we are happy to announce two major releases that we believe have the potential to reshape statistical practice in the area of A/B testing by substantially increasing the accuracy, efficiency and ultimately return on investment of all kinds of A/B testing efforts in online marketing: a free white […] Read more…

Also posted in A/B testing, AGILE A/B testing, Statistics | Tagged , , , , , , , , , ,

The Importance of Statistical Power in Online A/B Testing

Statistical Power and Test Sensitivity

What is Statistical Power? In null-hypothesis statistical testing (NHST) – the procedure most commonly applied in A/B tests, there are two types of errors that practitioners should care about, type I and type II errors. Type I is the probability of the test procedure to falsely reject a true null hypothesis. Type II error is […] Read more…

Also posted in A/B testing, AGILE A/B testing, Statistical significance, Statistics | Tagged , , , , ,

5 Reasons to Go Bayesian in AB Testing – Debunked

Frequentist vs Bayesian A/B Testing

As someone who spent a good deal of time on trying to figure out how to run A/B tests properly and efficiently, I was intrigued to find a slide from a presentation by VWO®’s data scientist Chris Stucchio, where he goes over the main reasons that caused him and the VWO® team to consider and […] Read more…

Also posted in A/B testing, Bayesian A/B testing, Statistical significance, Statistics | Tagged , , , , , ,

The Bane of AB Testing: Reaching Statistical Significance

Illusory Results AB Testing

Now, this is not a new topic on this blog. I’ve discussed the issue of optional stopping based on “achieving” or “reaching” statistical significance in my Why Every Internet Marketer Should be a Statistician post more than two years ago and others have touched on it as well – both before and after me. However, the issue […] Read more…

Also posted in A/B testing, Statistical significance, Statistics | Tagged , , , , , , , , , ,

Bayesian AB Testing is Not Immune to Optional Stopping Issues

Fantasy vs real world in bayesian ab testing

Fantasy vs the Real World: Naive Bayesian AB Testing vs Proper Statistical Inference This post is addressed at a certain camp of proponents and practitioners of A/B testing based on Bayesian statistical methods, who claim that outcome-based optional stopping, often called data peeking or data-driven stopping, has no effect on the statistics and thus inferences […] Read more…

Also posted in A/B testing, Bayesian A/B testing, Statistical significance, Statistics | Tagged , , , , , , ,

Tracking Site Speed with Google Analytics for UX and SEO

Site Speedometer

Given that site speed is a direct factor for AdWords landing pages, a major factor in Usability/UX/Conversion Rate optimization and a direct or undirect (I don’t think there is a consensus on that) factor in SEO, it’s no wonder many specialists nowadays pay close attention to page load times. Why Google Analycis for Site Speed […] Read more…

Also posted in Google Analytics | Tagged , , , , ,

Overview of Books on Conversion Optimization, Web Analytics, SEO & PPC

Books on online A/B testing

This overview is a little different than most overeviews out there. It will focus on three specific basic statistical concepts and would show if and how well they are explored in 18 of the top books on Conversion Optimization, A/B testing, web analytics, SEO & PPC/AdWords. The three concepts this review focuses on are statistical […] Read more…

Also posted in A/B testing, Google Analytics, Internet marketing, Multiple variations testing, Statistical significance, Statistics | Tagged , ,

Should you do A/A, A/A/B or A/A/B/B tests in CRO?

A/A Split Test

This question pops up often in more “advanced” discussion forums and blogs on Conversion Optimization and usually one (or several) of the following advices are given: Do an A/A test first in order to test your split testing framework. If the difference between the two is statistically significant at the decided level, then your framework is […] Read more…

Also posted in A/B testing, Statistics | Tagged , ,

Why Every Internet Marketer Should Be a Statistician

Marketing as Fortune Telling

I’ll start this post right with the punch line: Internet marketing at its current state is dominated by voodoo magic. There, I’ve said it, and I’ve said it in bold italic so you know it must be true. Internet marketing is not as data-driven as most of us have been led to believe, it is […] Read more…

Also posted in A/B testing, Internet marketing, Multiple variations testing, Statistical significance, Statistics | Tagged , , , , , , ,