A B Testing
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A / B Testing
How Your Business Can Use the Science That Helped Win the White House The average conversion rate—the rate at which visitors convert into customers—across the web is only 2%. That means it's likely that 98% of visitors to your website won't end up converting into customers. What's the solution? A/B testing. A/B testing is the simple idea of showing several different versions of a web page to live traffic, and then measuring the effect each version has on visitors. Using A/B testing, companies can improve the effectiveness of their marketing and user experience and, in doing so, can sometimes double or triple their conversion rates. Testing has been fundamental in driving the success of Google, Amazon, Netflix, and other top tech companies. Even Barack Obama and Mitt Romney had dedicated teams A/B testing their campaign websites during the 2012 Presidential race. In the past, marketing teams were unable to unleash the power of A/B testing because it required costly engineering and IT resources. Today, a new generation of technology that enables marketers to run A/B tests without depending on engineers is emerging and quickly becoming one of the most powerful tools for making data-driven decisions. Authors Dan Siroker and Pete Koomen are cofounders of Optimizely, the leading A/B testing platform used by more than 5,000 organizations across the world. A/B Testing: The Most Powerful Way to Turn Clicks Into Customers offers best practices and lessons learned from more than 300,000 experiments run by Optimizely customers. You'll learn: What to test How to choose the testing solution that's right for your organization How to assemble an A/B testing dream team How to create personalized experiences for every visitor And much more Marketers and web professionals will become obsolete if they don't embrace a data-driven approach to decision making. This book shows you how, no matter your technical expertise.
Statistical Methods in Online A/B Testing
"Statistical Methods in Online A/B Testing" is a comprehensive guide to statistics in online controlled experiments, a.k.a. A/B tests, that tackles the difficult matter of statistical inference in a way accessible to readers with little to no prior experience with it. Each concept is built from the ground up, explained thoroughly, and illustrated with practical examples from website testing. The presentation is straight to the point and practically oriented so you can apply the takeaways in your daily work.It is a must-read for anyone looking for a deep understanding of how to make data-driven business decisions through experimentation: conversion rate optimizers, product managers, growth experts, data analysts, marketing managers, experts in user experience and design. The new research presented and the fresh perspective on how to apply statistics and experimentation to achieve business goals make for an interesting read even for experienced statisticians.The book deals with scientific methods, but their introductions and explanations are grounded in the business goals they help achieve, such as innovating under controlled risk, and estimating the effect of proposed business actions before committing to them. While the book doesn't shy away from math and formulas, it is to the extent to which these are essential for understanding and applying the underlying concepts. The presentation is friendly to readers with little to no prior knowledge in statistics. Artificial and impractical examples like dice rolling and betting are absent, instead statistical concepts are illustrated through scenarios which might well be mistaken with the last couple of A/B tests you managed.This book also doesn't shy away from the fact that much of the current statistical theory and practice in online A/B testing is misguided, misinterpreted, or misapplied. It also addresses the issue of blind copying of scientific applications without due consideration of the unique features of online business, which is widespread. The book will help you avoid these malpractices by explicitly pointing out frequent mistakes, while also helping you align your usage of statistics and experimentation with any business goals you might want to pursue.
Designing with Data
Author: Rochelle King
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2017-03-29
On the surface, design practices and data science may not seem like obvious partners. But these disciplines actually work toward the same goal, helping designers and product managers understand users so they can craft elegant digital experiences. While data can enhance design, design can bring deeper meaning to data. This practical guide shows you how to conduct data-driven A/B testing for making design decisions on everything from small tweaks to large-scale UX concepts. Complete with real-world examples, this book shows you how to make data-driven design part of your product design workflow. Understand the relationship between data, business, and design Get a firm grounding in data, data types, and components of A/B testing Use an experimentation framework to define opportunities, formulate hypotheses, and test different options Create hypotheses that connect to key metrics and business goals Design proposed solutions for hypotheses that are most promising Interpret the results of an A/B test and determine your next move