I always found it amusing because I would regularly read CRO emails and blog posts with promotional best practices. But what I found from these tools is that for conversions, there is no hidden formula or magic wand that transforms the website into the proverbial “Pot of Gold,” or there are universal best practices. Because of context, this is. Not all users are the same on each site, nor do they communicate the same way with each site.
True optimization must be extracted from details. To understand how our tourists are using our platform, we need to use data and extract our optimizations from there. Therefore, the best practices in CRO are more like orders. For your next experiment, these so-called “best practices” are perfect for getting fast wins or as starting points, but be sure to test them before you roll them out.
Opinions don’t matter: Everyone has an opinion, but that doesn’t mean that we are right or know what works only because we have one. That’s why we’ve got tests. Experiments should be powered by results. There is so much information out there to help us better understand our visitors between data sources such as web analytics, lead data, and customer survey data. This knowledge offers excellent insight into where our guests come from, where they are going, what they are doing, and, most importantly, where they are transforming.
In a low-risk environment, experiments help us test our solutions to these issues. Instead of taking the risk of rolling out what we think will be the right idea, they allow us to see which of our solutions function better.
Do not copy your competitors: To see how they are doing it, it is very normal for us to look at the websites of our rivals. Are they better off doing it than us? Is there something they are doing that we are not and should be? Web design is really psychological. Instantly, we know whether or not we like something. But what we don’t know is whether it’s functioning or not. We want our users to be able to complete the mission on our website they have come to do. We do not know whether they have the answer when we copy others, or if their solution will work for our visitors
We should look at our own data instead of copying them to discover the problem, brainstorm on ideas that might possibly fix it, and then test the solutions. Now … with that said, that doesn’t mean you can’t use a term that you really like and think would solve the problem that’s already out there. Others face the same or equivalent obstacles. Finding something else that you think could work is not unusual. The lesson here should be … just make sure it’s your own. Make sure that the suggested approach is consistent with your priorities and check it out before you go and make a full stop.
CRO is So Much More Than Just A/B Testing: For A / B research alone, CRO is sometimes mistaken. But CRO is so much more than just A / B research, it is a comprehensive quantitative and qualitative study method or structure, while A / B testing is just one tactic that helps us validate our hypotheses. Although A / B tests can tell us which version of our experiment performed better, why that version won is not clarified. Explaining the “why” is also where the CRO process shines. The quantitative and qualitative evaluation allows us to understand how and why our customers engage with our website. When we know that, we will begin to get to the root of our conversion problems and test our solutions to those issues.
CRO is a Process: CRO consists of quantitative and qualitative research as a structured process/framework. It helps us to use different data sources to help us address the “why” when we look at user behavior. There are some variants of the CRO process/framework you can find, but they are quite similar at the heart of it. Both of us use a mix of quantitative and qualitative knowledge to understand how the site communicates with visitors.
Although A / B tests can tell us which version of our experiment performed better, why that version won is not clarified. Explaining the “why” is also where the CRO process shines. The quantitative and qualitative evaluation allows us to understand how and why our customers engage with our website. When we know that, we will begin to get to the root of our conversion problems and test our solutions to those issues.
It is incredible how much information there is out there that can help us understand what is happening on our pages, from web analytics to heatmaps to user reviews obtained by user testing, surveys, and polls. And there it does not end. All this knowledge is what allows us to shape our hypothesis from which we produce our experiment. New theories become the ideas we draw from the effects of our experiments and this process starts all over again.
Every Experiment Needs A Hypothesis: You don’t have a good definition of what you are measuring without a hypothesis. Nor do you have a true way of knowing whether your test has been successful or not.
A hypothesis helps one clearly define the problem, suggest the solution that we believe would solve the problem, and define a key measure that would or would not consider it a success. Make sure there is a hypothesis in any test.
Goals will be well defined: For a theory, goals go hand in hand. Not only can they assess the success and failure of an experiment, but future tests will become the core lessons and ideas we learn from our experiments. Objectives have to be explicitly defined, so there is no doubt as to what we calculate.
Furthermore, targets need to be specifically linked to the experiment. If you customize a landing page with a form, for example, your goal is most likely to increase the number of form submissions, not anything further down the journey of the user.
There are also occasions when you want to track more than one target. This is all right but remember: Each test should have only one primary objective. Each test should have only one primary objective. The main purpose will decide whether your experiment is successful or not. The rest would all be secondary targets.
When you want to track more than one goal, there are also times. This is all right but remember: There should be only one primary target for each test. Each test should only have one primary target. The primary objective will dictate whether or not your experiment is successful. All the remainder will be secondary objectives.
Tracking your goals: Before launching, it is also necessary to confirm the monitoring of your experiment. The last thing you want is to finish your experiment, just to find out then that you didn’t have the proper monitoring in place to check whether your experiment succeeded or not. This sounds fairly easy, but I ran into this myself recently.
Don’t stop, edit, and restart your experiment: What happens when a test is run and you see that a change needs to be made? Most platforms allow you to interrupt, edit, and then restart the experiment. That’s something experts suggest you never do, though. Actually, doing such a thing might really endanger the data. If the shift is significant or small, it can influence the actions of the consumer, which may jeopardize the data’s integrity.
Be patient and let the experiment run its course: When the experiment begins, to see the result and end up continually checking how the test is going, it is easy to get caught up in the excitement. Don’t be alarmed that your test wins one week, and it doesn’t win the next. The truth is, in the beginning, experiments can be very unpredictable.
