What is A/B Testing?
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 technology has made launching and analyzing experiments relatively easy.
In the context of digital marketing, 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 countless webpage components that can be tested for performance, including page layout, menu location, headlines, calls to action (CTA), images, fonts, colors, image sizes and many more—the list is endless. Results and conversions can differ significantly depending on the type of elements tested and the audience.
Why A/B Test?
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 testing method reduces or eliminates guesswork, marketers and business managers can consistently improve the results of their efforts 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 launch, maintain, and scale a successful experimentation program. 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 organization’s needs.
In addition to understanding the tools available for proper A/B testing, you’ll also need to have a solid grasp of the statistical principles that underpin A/B testing. Without a foundational understanding of how to statistically interpret results, you’ll likely encounter errors or make unreliable business decisions. Let’s break down the three most important statistical terms you’ll become acquainted with along the way:
- Mean – The mean (or average) is a measure for determining how each variable we test results in something. You’ll want to tabulate the mean click rate or conversion rate depending on what you’re testing.
- Variance – The variance is used to determine the variability of the data being measured. The lower the variance, the more accurate the mean will be. Likewise, if there’s a large variance in what you’re testing, the confidence interval of the sample mean will be larger and less accurate as a measure.
- Sampling – In order for the data to be statistically meaningful, there needs to be a large enough sample size. If we test only a handful of interactions with a particular website test, the sampling might not be large enough to have significance.
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 1 (false positive) or Type 2 (false negative) errors and misinterpreting the results of their tests.
A/B Testing Challenges on Your Website
While A/B testing can play an integral 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 different ways of optimizing their website content.
- A/B testing can be time consuming. With A/B testing, you should test one variation at a time. Testing more than one at a time can compromise the results of both experiments, undermining the statistical significance and defeating the point of the controlled experiment. And, depending on the amount of traffic your website gets, the results can take time – weeks or even months to reach statistical significance. In a business environment that demands fast results this week, A/B testing may not be the proper test type to run.
- A/B testing can ignore smaller segments. A/B testing doesn’t take into consideration each unique visitor, but rather groups visitors into large randomly assigned segments. For example, if one variation outperforms another 60% to 40% the winning variation may be more effective for a majority of your website visitors, but there may still be a large segment in your overall population that would respond better to the losing variation. In A/B testing, only one variation wins and is served to all future web visitors rather than serving different variations to different segments over time which is possible with other testing types.
- A/B testing can be labor intensive. A/B testing requires marketers to closely monitor metrics, measure improvements, and update the website with new testing variations. This often requires the resources of web developers, programmers, graphic designers, and possibly legal resources within an organization to make site changes.
- A/B testing can lead to missed opportunities. An A/B test gathers data by serving one variation that will (hopefully) win and one version that will (presumably) lose to 50% of your selected website traffic users each. While the experiment gathers data, your website is missing out on conversions from the half of your audience that is viewing the future losing variation. Over time, these missed opportunities will add up.
- Statistical Complexity. Without a deep understanding of statistical principles, marketers can often come to false conclusions by misinterpreting the results of an A/B test. Ensuring experimental validity, reaching statistical significance, and analyzing statistical confidence and power can be a high bar that trips up even the most experienced CRO geeks.
A/B Testing FAQs
Why is A/B testing important?
A/B testing is important because it allows businesses to make data-driven decisions and optimize their digital experiences. By testing different variations, they can identify which elements are most effective in driving user engagement, conversions, or other desired outcomes.
What can I test using A/B testing?
You can test various elements using A/B testing, such as headlines, call-to-action (CTA) buttons, images, layout designs, pricing options, email subject lines, and more. Essentially, you can experiment with and test any component of your digital experience that can be altered.
How do I determine the sample size for an A/B test?
Determining the sample size for an A/B test involves considering factors like desired statistical significance, expected effect size, and baseline conversion rate. Statistical tools and calculators, like online A/B testing calculators or statistical software, can help you determine an appropriate sample size.
How long should an A/B test run?
The duration of an A/B test depends on factors like the size of your audience, the level of traffic or conversions you receive, and the magnitude of the expected changes. Generally, it's recommended to run tests long enough to collect sufficient data and achieve statistical significance, which can typically range from a few days to a few weeks.
What is statistical significance in A/B testing?
Statistical significance in A/B testing indicates the likelihood that the differences observed between two variants are not due to chance. It helps you determine if the results are statistically reliable and can be attributed to the changes you made rather than random variation.
How do I interpret the results of an A/B test?
To interpret A/B test results, you typically compare the performance metrics of the control group (A) with the variation group (B). Look for statistically significant differences in metrics like conversion rate, click-through rate, bounce rate, or revenue. Ideally, the variant with better performance will become the new baseline.
Are there any common pitfalls to avoid in A/B testing?
Yes, there are common pitfalls in A/B testing. Some include testing multiple variations without proper sample size calculations, stopping a test too early, not segmenting your audience properly, or succumbing to false positives/negatives due to low statistical power. It's important to follow best practices and be aware of these pitfalls.
This blog link is a great resource on A/B Testing in Marketing Dos and Don’ts.
Can A/B testing be applied to non-digital experiences?
While A/B testing is commonly associated with digital experiences, the concept can be applied to non-digital scenarios as well. For instance, you can test different store layouts, product packaging designs, or sales strategies. The key is to create two distinct groups and compare the outcomes to determine the best approach.
How do I choose which metric to measure in an A/B test?
When selecting a metric to measure in an A/B test, consider your primary goal. Is it to increase conversions, improve engagement, or boost revenue? Choose a metric that aligns with your objective and directly reflects the impact of the changes you are testing.
Are you an ecommerce marketer looking for more guidance on key website metrics? The Marketer's Ultimate Guide to Ecommerce Metrics includes a number of helpful resources and best practices.
Can A/B testing be used for small businesses with limited resources?
Yes, A/B testing can be beneficial for small businesses. While large-scale tests might not be feasible due to limited resources, small-scale tests can still provide valuable insights. Focus on testing high-impact elements or critical parts of the user journey to maximize resources.
Is there a specific order in which I should test different elements?
There is no specific order for testing elements. However, it is often recommended to start with elements that are likely to have a significant impact on user behavior, such as headlines, CTAs, or pricing. Gradually move on to testing other smaller elements to optimize your entire digital experience.
Should I always choose the variant with the highest conversion rate?
Not necessarily. While the variant with the highest conversion rate might be tempting to choose, it's important to consider other metrics and the overall goals of your business. Look for a balance between different metrics, such as conversion rate, engagement, or revenue, to make an informed decision.
Can A/B testing be done on mobile apps?
Yes, A/B testing can be conducted on mobile apps. There are various A/B testing tools and platforms available that specifically cater to mobile app testing. You can test different UI elements, features, onboarding flows, and more to optimize the user experience on mobile devices.
Are there any ethical considerations in A/B testing?
Yes, there are ethical considerations in A/B testing. It's important to ensure that your testing practices comply with privacy regulations like the General Data Protection Regulation (GDPR) and protect user data. Additionally, be transparent about the testing process and any variations presented to users, and avoid conducting tests that might intentionally mislead or harm users.
How frequently should I perform A/B tests?
The frequency of A/B tests depends on your business objectives and how quickly you want to implement changes. It's best to strike a balance between having enough time to collect sufficient data and avoiding excessive testing that could disrupt user experience. Consider your resources, traffic levels, and the pace of changes you want to make.
Can A/B testing be used to test pricing strategies?
Yes, A/B testing can be used to test pricing strategies. You can create variations with different price points or pricing models and measure the impact on customer behavior, such as conversion rates, revenue, or average order value. This can help you determine the most effective pricing strategy for your products or services.
Recommended Resources – AI-driven Opitmization vs. A/B Testing
Here are recommended resources on how and why AI-driven optimization outperforms A/B testing.
- Case Study: Going Beyond A/B Testing at Tableau: Driving Personalized Experiences and Conversion Performance
- Video: Which Is Better for Me? A/B Testing, Rules-based Personalization, or Predictive Personalization?
- Video: "Winner Take All" Testing vs Personalization
- Blog: Top 6 Things Every Marketer Should Be Testing on Their Website
- Video: How Machine Learning Supercharges Website Optimization