A/B testing. What Is It and What Is It Used for?
Generating new ideas, approaches, and creatives is an integral and quite significant part of working in affiliate marketing. However, each of such new ideas or theories, of course, needs to be carefully examined and tested. But how to do it quickly and effectively? There are many tools for this, and today we decided to talk about one of the most popular ones – A/B testing.
What is A/B testing?
A/B testing is, first and foremost, a practical marketing tool to enhance the efficiency of your advertising campaign or resource as a whole. By correctly using this tool, you can increase conversion rates or determine the best components of your creatives.
Who needs A/B testing and why?
The need for A/B testing is easiest to explain through an example of its use. For instance, you have already launched an advertising campaign. Traffic is coming in, but it’s not delivering the desired results in terms of effectiveness. Sooner or later, you’ll likely consider the need to make some changes, aiming to improve performance without a doubt.
This leads to ideas about what to change, what to fix, and what to remove. New variations of headlines, images, or video content may also come to mind. However, unfortunately, not everything we come up with will necessarily be highly effective and conversion-driving. It is precisely to test such hypotheses and their impact on results that we conduct A/B tests.
As for who needs these A/B tests, the answer is everyone. In short, they are valuable to product managers, marketers, product designers, webmasters, and anyone seeking to improve the performance of their product, service, or resource.
When are A/B tests conducted?
In what cases are A/B tests conducted:
- When it is necessary to obtain reliable data on the quality of changes;
- When there is enough time to conduct the test;
- When there are enough users (traffic) for data analysis;
- When it is the best way to gather data for decision-making regarding innovations.
Conducting tests with low traffic flow, insufficient time for testing, or in any other case would not be appropriate because important metrics and data could easily be missed due to low metric sensitivity or lack of data. Alternatively, waiting for test results for several months is an option, although in such a case, the need for this kind of tool diminishes, as its main purpose is to quickly provide objective data and make decisions in a short timeframe.
A/B testing is an effective tool for testing new theories, but it’s not a magic wand. Based on the data obtained after testing and timely data analysis, the most objective and effective decision is made. However, the tool also helps avoid mistakes rather than pointing to a “conversion” button. Therefore, as with everything, knowing how to work with it is essential, and then everything will work out.