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.

Inherent costs of A/B testing: limited risk results in limited gains

Costs

I’ve already done a detailed breakdown of costs & benefits in A/B testing as well as the risks and rewards and how A/B testing is essentially a risk management solution. In this short installment I’d like to focus on the trade-off between limiting the downside and restricting the upside which is present in all risk management […] Read more…

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Representative samples and generalizability of A/B testing results

Representative samples and generalizability of AB testing results

I see a nice trend in recent discussions on A/B testing: more and more people realize the need for proper statistical design and analysis which is a topic I hold dear as I’ve written dozens of articles and a few white-papers on. However, there are cases in which statistical validity is discussed without consideration for […] Read more…

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Designing successful A/B tests in Email Marketing

The process of A/B testing (a.k.a. online controlled experiments) is well-established in conversion rate optimization for all kinds of online properties and is widely used by e-commerce websites. On this blog I have already written in depth about the statistics involved as well as the ROI calculations in terms of balancing risk and reward for […] Read more…

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Analysis of 115 A/B Tests: Average Lift is 4%, Most Lack Statistical Power

Observed Percent Change Significant

What can you learn from 115 publicly available A/B tests? Usually, not much, since in most cases you would be looking at case studies with very basic data about what was tested and the outcome of the A/B test. Confidence intervals, p-values and other measurements of uncertainty will often be missing, and when present they […] Read more…

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Confidence Intervals & P-values for Percent Change / Relative Difference

In many controlled experiments, including online controlled experiments (a.k.a. A/B tests) the result of interest and hence the inference made is about the relative difference between the control and treatment group. In A/B testing as part of conversion rate optimization and in marketing experiments in general we use the term “percent lift” (“percentage lift”) while in […] Read more…

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Affordable A/B Tests: Google Optimize & AGILE A/B Testing

The problem most-often faced by owners of websites who want to take a scientific approach to improving them by using A/B testing is that they might have relatively small revenue. Thus, when the ROI calculation for the A/B test is done it might turn out that it is economically unfeasible to test. In some cases, […] Read more…

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The Google Optimize Statistical Engine and Approach

Frequentist vs Bayesian A/B testing - Google Optimize

Updated Sep 17, 2018: Minor spelling and language corrections, updates related to role of randomization and external validity / generalizability. Google Optimize is the latest attempt from Google to deliver an A/B testing product. Previously we had “Google Website Optimizer”, then we had “Content Experiments” within Google Analytics, and now we have the latest iteration: […] Read more…

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Risk vs. Reward in A/B Tests: A/B testing as Risk Management

Risks vs Rewards in AB Testing

What is the goal of A/B testing? How long should I run a test for? Is it better to run many quick tests, or one long one? How do I know when is a good time to stop testing? How do I choose the significance threshold for a test? Is there something special about 95%? […] Read more…

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Costs and Benefits of A/B Testing: A Comprehensive Guide

Costs and Benefits in AB Testing

This is a comprehensive guide to the different types of costs and benefits, risks and rewards related to A/B testing. Understanding them in detail should be valuable to A/B testers and businesses considering whether to engage in A/B testing or not, what to A/B test and what not to test, etc. As far as I […] Read more…

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