The history of A/B testing can be traced back to the early days of scientific experimentation and statistics. The concept of controlled experiments and hypothesis testing has been employed in various fields for centuries. However, it was the emergence of digital technologies and the growth of online businesses that paved the way for the widespread adoption of A/B testing as a powerful tool for optimization and decision-making.
The origins of A/B testing can be found in the field of direct marketing, where companies sought to understand the impact of different marketing strategies on consumer behaviour. In the mid-20th century, marketers began conducting controlled experiments by sending out two different versions of mailers to segmented groups of their target audience. By comparing the response rates of the two versions, they could determine which approach was more effective in driving customer engagement and conversion.
With the rise of the internet in the late 20th century, A/B testing found a new realm of applications. Web developers and designers started using A/B testing to improve website usability, conversion rates, and overall user experience. The ability to easily create multiple versions of a web page and direct traffic to different variants made it feasible to test and optimize various elements simultaneously.
The term "A/B testing" itself emerged as online businesses and marketers sought to differentiate this method from other types of testing. The letters "A" and "B" represent the two different versions being tested, with "A" typically denoting the control or original version, and "B" representing the variant or the version with changes. The process involves randomly dividing the audience into two groups, with each group being exposed to one version, and then measuring and comparing their performance.
A/B testing gained significant traction and recognition with the growth of data-driven decision-making and the emphasis on optimization in the digital age. The increasing availability of tools and platforms dedicated to A/B testing made it more accessible to businesses of all sizes. Today, A/B testing is a common practice employed by companies across industries, including e-commerce, software development, marketing, and user experience design.
Benefits of A/B testing
The benefits of A/B testing are manifold. Here are some reasons why it is considered a valuable practice.
Data-driven decision making
A/B testing allows businesses to make decisions based on concrete data and evidence rather than relying on assumptions or intuition. It provides objective insights into how changes impact user behaviour, conversion rates, and other key performance indicators.
Optimization and conversion rate improvement
By systematically testing different variations, businesses can identify the most effective strategies for improving conversion rates, reducing bounce rates, increasing engagement, and achieving their goals. A/B testing helps optimize websites, landing pages, marketing campaigns, and product designs for better performance.
Reduction of risk and uncertainty
A/B testing mitigates the risks associated with making significant changes without understanding their potential impact. By testing changes on a subset of the audience, businesses can gain confidence in their decisions before implementing them on a larger scale.
A/B testing fosters a culture of continuous improvement. It allows businesses to learn from each test, iterate on successful changes, and refine their strategies over time. This iterative approach helps drive incremental gains and ongoing optimization.
Enhanced user experience
A/B testing enables businesses to understand how different variations resonate with their target audience. By tailoring experiences to meet users' preferences and needs, businesses can create more engaging and personalized experiences that result in higher satisfaction and loyalty.
In summary, A/B testing has a rich history rooted in scientific experimentation and marketing. Its evolution and widespread adoption in the digital age have been fueled by the need for data-driven decision-making, optimization, and improved user experiences. As businesses strive for continuous improvement and better performance, A/B testing has become an indispensable tool in their arsenal, enabling them to make informed decisions and optimize their offerings based on real user data
Starting an A/B testing program
Before starting an A/B testing program, there are some important things to have in mind. Some key points are.
Define your goals
Before running your experiment, you should have clearly defined goals and objectives you want to achieve through A/B testing. Some examples are to increase your conversion rate, increase the number of leads, increase your user engagement rate or increase click-through rates. Clearly defined goals will help you focus your efforts and measure the effectiveness of your tests accurately.
Build strong hypothesis
A hypothesis is an educated guess on the outcome of your test with research data as foundation. A thorough research will help you build strong hypothesis for your tests. A hypothesis can be formulated as “If we apply [ THIS CHANGE - UX] then [ THESE METRIC CHANGE-DATA] for [ THIS GROUP OF USERS - DATA ] because of [ THIS BEHAVIOURAL REASON - PSYCHOLOGY ]”. It helps you formulate a clear understanding of what you want to achieve and why you believe a certain change will produce better results.
For e.g. you might hypothesise that moving up the add to cart CTA on your product display pages will increase your conversion rate, since you through research have seen that a large percent of your users doesn’t see it.
Prioritizing your test ideas is a good way to ensure that you make efficient use of your resources and test the ideas with highest potential first. We at Symplify use and recommend the PXL framework, which was created by the CXL institute. The framework was made to eliminate as much subjectivity as possible while maintaining customizability.
3 Major Benefits:
- Makes any “potential” or “impact” rating more objective
- Helps to foster a data-informed culture
- Makes the “ease of implementation” rating more objective
A good test idea is one that can impact user behaviour. Instead of guessing what the impact might be, this framework asks you a set of questions and places value on that impact. Grab yourself a copy here!
Calculate sample size and duration
Calculating an appropriate sample size and test duration is crucial to obtaining statistically significant results. A smaller sample size may not provide reliable insights, while an excessively long test duration can introduce problems like cookie pollution. To help you estimate required sample size and test duration, you can use a sample size calculator. Use our calculator here!
Symplify’s stats engine requires users to calculate in advance the sample size for each test with the assumption that each test is a superiority test better known as a one-tailed test. Since 95% of statistical hypothesis in conversion rate optimization are based on research hypotheses that are seeking to determine if a variation is better than the original, this is the most appropriate application of frequentist statistical methods for conversion rate optimization. Read more about how our stats engine works here and how to interpret our statistics here!
The other option is server-side testing, which makes it possible to run experiments on pricing or other aspects handled by your back-end. It’s also a great way to make sure there is no "flickering" at all for large changes.
It's very important to preview the projects before you go live. Preview shows exactly how the changes will look when the project goes live, so test this on all devices / browsers you will run your project on to be certain it works and looks the way you want. For more technical information, don’t hesitate to contact us!
Analyze test results
Based on the results of your A/B test, make data-informed decisions about which variation performed better. Implement the winning version and continue iterating and testing new variations to further optimize your website or product. A/B testing is an ongoing process that allows you to continuously refine and improve your offerings based on real user data.
Document your findings
Keep detailed records of your A/B tests, including the hypotheses, variations tested, and results. Documenting findings allows for better knowledge sharing and facilitates informed decision-making in future optimization efforts.
Remember, A/B testing is most effective when performed with a clear understanding of your goals, careful planning, and systematic analysis of the results. It's important to approach A/B testing as an iterative and data-driven process to drive meaningful improvements and achieve your desired outcomes.
Read how to create A/B tests here!