The Secret to Successful A / B Testing - 5 Questions for Yourself

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Abraham Lincoln once said: "Give me 6 hours to cut a tree, and the first 4 hours I will spend on sharpening an ax." This quote has a great analogy with A / B testing. If you want to hit the jackpot during the A / B test, you have to prepare very well. And you will learn exactly how to do this in our article.

Michael Aagaard (one of the most famous experts in the field of conversion optimization and copywriting), decided to make a list of the most serious errors in A / B testing. Based on his many years of experience, he came to the conclusion that his main mistake was to conduct too many experiments.

It sounds very strange. What then should an expert in A / B testing focus on, if not for experiments? The answer is inside the article.

The story of one successful A / B test

Let's start with a short story. Working on increasing the conversion of one site, Michael launched an experiment in which the test version of the landing page was very different from the original. As a result of the experiment, the conversion increased so much that the client earned an additional amount with five zeros in just two weeks after the introduction of the test version on the site.

This result was not achieved due to a large number of experiments or ideas for testing . No, this has nothing to do with success at all.

It was possible to conduct a successful A / B test through careful preparation. Most of the time was spent on:

Only after collecting all the information, Michael began to formulate ideas to increase conversion and sales.
Surely, many of you think that A / B testing is the simplest tool to increase conversion. And you are mistaken. Really successful experiments, which allow you to increase sales and break a good five-zeros jackpot, are based on what is listed above.

That is why, if Abraham Lincoln was a 21st-century Internet marketer, his quote would read something like this: “Give me 6 hours to increase the conversion of the landing page, and 4 of them I will spend on analyzing the information.”

Do A / B testing without Web analytics, user understanding is the same as trying to cut a tree with a blunt ax.

The success of A / B testing directly depends on how good the hypothesis is for the experiment.

This is the conclusion Michael came to after running hundreds of tests, most of which were unsuccessful. And all just because he was always chasing quantity, not quality. And in those cases where the emphasis was on preparation, the experiments were most often successful.

So why don't you learn this lesson. Or is it better to step on the same rake?

Most A / B experiments fail either because of the weak hypothesis for testing, or in general because of its absence. No matter how funny it sounds ...

Article in topic - 10 most common errors in A / B testing

The A / B test hypothesis includes information about what you want to change on the page and how this change can affect the final result.

To simplify the process of generating good ideas for testing, we suggest you use the special technique that Michael uses in his work.

5 questions that will help to understand the target audience and run a successful A / B test

This technique is very simple. Its essence is getting the right answers to the right questions in the right order. Who! Where? Why? What? How?

It all starts and ends with an understanding of the target audience. You must know your potential customer exactly.

The question that needs to be answered is: “Who visits my site (the page where you are going to conduct the A / B test)?”

You need to know how the potential customer got on the landing page. Did he see ads on another site? Did he switch to it after he received your newsletter? Or through Google search? In each case, the user behavior will be different, so you should clearly know how the potential client gets on the landing page.

The question that needs to be answered is: “Where was the potential customer before coming to my site?”

You must understand the problems and motivation of your target audience. If you know, on the basis of what factors a potential client makes his decision, you can get it.

Questions to get answers to:
  • Why did he visit my site?
  • Why does he need what I offer?
  • Why would he agree to pay for it?
  • Why can he refuse?

Now you know who the potential client is, where he came from and for what reason, what factors influence his decision. At this stage, you need to determine the content that should be on the landing page.

Questions to get answers to:
  • What should a potential customer know in order to make a purchase?
  • What should I focus on to convey the value of my offer to a potential client?
  • What should I focus on in order to overcome all the objections (answer all the exciting questions) of the potential client?
  • What should happen after he agrees to pay for my offer?
  • What is missing on this landing page and what should I add to it?
  • What needs to be removed from the page?

After you have received answers to all the previous questions, you can go to the most important thing.

The final question: “How should I change the page to increase conversion?”

Having received answers to all the previous questions, you can easily answer the last. And, based on this answer, you can formulate the idea of ​​a successful A / B experiment.

Only 5 questions. Some are easier. Others are harder. To get some answers, you will have to try and delve into web analytics, user analysis, conducting surveys, etc. It all depends on the information already available. But this is precisely the main advantage of this technique.

If you cannot answer any of the questions above, then you do not fully understand your target audience.

If you do not fully understand your target audience, you cannot offer it exactly what you need.

If you cannot do this, then it will be difficult to create a really good A / B experiment that will increase conversion and sales.

Therefore, just try to use this technique in your work. I am sure that you will be satisfied. At a minimum, this will give you a better understanding of the target audience. And in the long run, this is never superfluous.

By the way, it would be very interesting to know how you generate ideas for A / B testing? Leave your comments about this.

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