A/B testing is a statistical methodology of comparing different versions of something to see which version performs better. While A/B testing is a scientific approach to problem solving that has been in use for nearly a century, the technique was first adopted by marketers as early as the 1960’s in direct marketing campaigns. But with the rise of the digital era, A/B testing has surged in popularity, partly because launching and analyzing experiments in the online world is relatively easy.
In the context of digital marketing, conversion-obsessed marketers will test different versions of webpages, email headlines, landing pages, ad copy and other user-facing online content to determine which performs better. There are many webpage components that can be tested for performance, including page layout, menu location, headlines, CTAs, images, fonts, colors, image sizes… the list is nearly endless. Results and conversions can differ significantly depending on the combination of elements and the audience.
A/B testing provides a definitive, data-driven approach to determining which version of online content performs better, that is both statistically valid and scientifically sound. In short, A/B testing takes the guesswork out of marketing, replacing subjective decision making with an objective framework for determining winners and losers.
Because the guesswork has been reduced or eliminated, marketers and business managers can consistently improve the results and efficiency of their marketing efforts, or business operations, over time by employing a systematic approach to A/B testing. Often the result of ongoing A/B testing and experimentation is a dramatic improvement in marketing effectiveness and advertising ROI, and sometimes is the difference between the success and failure of a marketing campaign or even a business.
There are many A/B testing tools and software solutions today that can help with launching a successful program of experimentation. These testing tools can help marketers sift through the myriad of attributes, data, and options that can otherwise make A/B testing difficult or cumbersome. Their cost and complexity can vary tremendously depending on the size of your website and your firm’s needs.
In addition to learning and understanding the tools available for proper A/B testing, you’ll also need to have a solid understanding of the statistical principles that underpin all of A/B testing. Without a foundational understanding of how to statistically interpret results, you’ll likely encounter errors and make unreliable decisions. Let’s break down the three most important statistical terms you’ll become acquainted with along the way:
Determining the statistical significance of an A/B test is critical, because it is the statistical validity that gives A/B testing its prescriptive power. Without statistically significant results, marketers are at risk of making either Type I (false positive) or Type II (false negative) errors and misinterpreting the results of their tests.
While A/B testing can play an integral and even essential role in driving marketing results, it is important to acknowledge the inherent challenges of A/B testing, especially for websites, that have led marketers to adopt more advanced ways of intelligently optimizing their website content.
Intellimize is pioneering new advancements in the industry by introducing Continuous Conversion™, a machine learning optimization approach that outperforms A/B testing. While A/B testing is cumbersome and slow, Continuous Conversion™ is fast and efficient, delivering head-turning results in days not weeks.
Intellimize uses machine learning to optimize the individual steps of each buyer’s unique journey in real time and automatically adjusts web content in response to changes in the buyer behavior over time, delivering better conversion results as much as 25x faster, empowering marketers to test more ideas, faster.
Intelligent website optimization can help improve lift and conversions for websites across a host of industries. Here are some use cases of how machine learning can power your website performance improvements.
Here are recommended resources on how and why Intelligent Website Automation outperforms A/B testing.