Common Pitfalls of Conducting A/B Tests
A/B testing is a technique for comparing two different versions of a product whether it’s a webpage or app to see which one performs better. You can read our A/B testing article to learn more. Not all of the A/B tests may end up with great results. There is also a strong chance that it can totally go wrong and give twisted outcomes. Many companies focus on the designing of their testing elements or the content itself, but the main focus actually should be the execution part of their whole experiment. To prevent mistakes in the experiments, here are some common pitfalls that are needed to be careful.
1. Ending the test too soon
A/B tests may costs money and time regarding the scope of the experiment you do. Leaving the tests early can cause a lack of data which affects the efficiency of the whole A/B testing. To define how much time you’ll spend on a test or how many users’ experiences you would like to analyze is a saver for that. Create a calendar or write down the user count so you can end the test just on time.
2. Testing too many variations at once
Understanding the issues of your apps is important because it helps you to focus on the right place. Clearly defining goals and missing points are important to know which elements to test. It doesn’t mean that you’ll get more insights just because you test everything at once. That will surely impact the clarity of the data since it’s not obvious what to test. It also will slow down the testing period. So dividing the testing items in a timeline and test everything one by one will give you a wider perspective and more accurate results.
3. Testing without hypothesis
The research and analysis phase is the most crucial part of the conversion optimization process. At this step, you must investigate and analyze analytical data as well as non-analytic data such as survey studies, user complaints, and user behaviors to determine where optimization possibilities exist.
All of the concepts you've proposed as a consequence of these investigations fall under the category of hypotheses. Three essential factors must be addressed for a hypothesis to be effective.
4. Too many details, too many complexities
The growth model you established under your company strategy should be the starting point of the testing process. You can easily understand what the greatest problem with your growth is and where you need to fix the problem first when you display your KPIs and growth model. This will reveal the issue on which you should focus your efforts. Check out our AARR funnel article to learn more.
You cannot identify the fields you want to improve will be and what parameters you need to optimize in this area without extracting the KPIs and growth model. You attempt to do copy-paste experiments using only the technologies available to you. In such a setting, true success is nearly difficult to accomplish.
5. Testing without enough sample
The sample size is an important factor to get an accurate analysis. If you have 20 people in your app and even convert 5 people, it gives a high conversion rate but actually, it is not enough data. Testing with not enough numbers will probably give you successful statistical rates but what is the benefit of it? You'll need trustworthy data with a particular amount of consistency before you can do this sort of test. You are wasting your time and money if you wait three months for a test to be completed. You don't have any data? Then make little adjustments till you reach this stage. You can instantly notice the difference in the number of inbound leads that you have made so far. You have nothing to lose in terms of traffic or conversion rate.
6. Spending too much time on unnecessary tests
“Should the continue button be red or blue?”. In essence, there is no such thing as a "correct color." Yes, color is significant, and the color of that button may be significant to you. But it's not just because of the color of the button; it's also because of the importance of the visual hierarchy of the entire page, if not the entire site. In the form, the question should be “Do significant activities draw more attention than others on the page?” rather than “Should the button be red or blue?”.
The list can be extensible just like that. The main goal of A/B testings is to get more and accurate insights from the users, that is why executing it in the right way is very important. The essential point here is not to be misled by the A/B discourse's simplicity and being caught up in the thinking vortex.
