A/B Testing is a marketing experiment and very helpful with campaign planning and procedures. However, there are many things marketers need to correct that could result in the downfall of their marketing campaigns. Here are 5 of the most typical A/B Testing mistakes you should avoid.
What is A/B testing?
A/B testing is a technique to compare two versions of anything, may it be an email, a webpage, or an app, against each other to determine which one is better. It is used for the optimization of data and to provide a better experience to the user. But one might make a couple of mistakes while using this method. So, here we are going to discuss the 5 most typical A/B testing mistakes one should avoid.
- You called it too early:
The most common mistake one can make is to run the split test way too early. Starting your split test early is of no use as you haven’t collected the correct amount of data yet. For having an in-depth view of the entire marketing strategy, the A/B tests need statistics to provide more accurate results. Results based on little or no information will not be accurate in the long run.
As reported in a study by Brill Mark, after conducting a test, it may become evident that the desired amount of data hasn’t been accurately gathered within a few days. This issue often arises because many split testing platforms don’t factor in the number of conversions tracked for both the original design and the variations. With sufficient time allowed for the test to run, you may observe a gradual diminishing of the initially observed uplift, highlighting the importance of robust data collection and analysis.
It’s simple, no data, no split test. At this stage, it’s better to start a promotional campaign for your product in order to reach the market and gain customers so that you have a database to run a test on. Then slowly, when your database starts to build up, you can collect the data, get the test results, and improve on your strategy. Email marketing is the best way through which one can easily get access to data for the tests.
- Not running tests for weeks:
If you start a test, you should not end it in 2 or 3 days but instead at least run it for a week to get accurate results. This is because you need enough data for a proper analysis. If your traffic is high for the first two days, and you end the test, you will not get the correct result because these tests only show results for those few days.
As per research detailed in Towards Data Science, to obtain accurate results, it’s advisable to persist with your test for at least seven days. Conducting a test for just a brief period, such as one or two days, may not yield the desired insights. Therefore, it’s recommended to conduct a week-long test, allowing sufficient time to analyze both areas for potential improvements and aspects that are performing well. This comprehensive approach ensures a more thorough and reliable assessment of your data.
You should be aware that traffic and conversion rates can vary from day to day. This variation is what you need to study with the results to find a pattern. The open rates of one day can be high while the conversion rate can be low and vice versa. So it’s better to run a week test. Then analyze what improvements can be made and where things are working fine. You need to work it out on a week to week basis for accuracy.
- Giving up:
“Failure is only the opportunity to begin again, this time more intelligently.”
– Henry Ford.
Failure is important in order to learn things from it. Your first test can fail, not only the first one but the second, third, fourth, and even the fifth test, but you shouldn’t just give up. Find the positivity in it. Each failed test tells you about your mistakes and where improvements can be made when you know that you can learn from these mistakes, and it might be possible that your next test would be successful.
Make changes and play around till the results improve. Even after the smallest improvement, see and take note of what caused this and then focus on that. Patience is the key to success. Forget about the money you are losing in this failure. You’ll make a hell lot more when you succeed in bringing the traffic and conversion you were planning to have since the beginning.
- Running Multiple Tests:
When you are trying to get a big picture of things, sometimes, running multiple tests at a time may seem like a good option. However, it does come with its cons. Running multiple tests is not an idea someone would prefer, but still, people use it.
As outlined in a study highlighted by OptinMonster, while the urge to test extensively is understandable, it’s essential to exercise caution. Testing every conceivable variable can lead to participant confusion and produce inconclusive or insignificant results. Each variation introduced into a test can potentially fragment the audience or even yield multiple winners if they lack uniqueness, which can complicate the analysis and decision-making process.
The issue is simply that while it may save you time, it may not give an accurate reading. For example, if you are running a test for the homepage and inbox on a specific product, the traffic may overlap and give you false results, which can be a horrifying thing for your test. On the other hand, if you are running individual tests, each and every test will get its own time and traffic, leading to somewhat slow but accurate results.
You will get separate results for the inbox and homepage, clearly showing you what needs to be improved in which section. Therefore, it’s better to run individual tests.
- Wasting Time on Irrelevant Tests:
Using A/B testing for various things is a good thing as it tells you what works and what does not work. However, there is such a thing as irrelevant testing. While people usually check for inbox, email performance, text, HTML, etc. some tests are useless.
For example, it’s a complete waste of time if you test designs and colors. These factors do not judge nor affect traffic. In fact, it is not even possible to get such results with much accuracy. How would you even get the traffic to run the test on it?
It would be a disaster, a complete waste of money. It would be better if you use all that time and money on a relevant test that may actually bring you results that you can work with. These relevant tests include tests about your content quality, relevance, and engagement. The statistics can then be used to improve the emails or homepages.
