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Keys to effective A / B testing

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Keys to effective A / B testing

A / B testing is a process in which we carry out an experiment to compare two pages with each other at the same time. In this way, we can know which Bermuda Email Lists gives the best results. Although in reality A / B tests can be applied to different content and objectives, for simplicity we will assume that we are comparing different web pages with each other to improve the conversion.

Typically, A / B testing compares an established version of the page with a new one, or two new pages that differ from each other by a single variable. In either case, it is important to configure the experiment so that both versions of the page receive the same amount of traffic.

How to do an effective A / B testing in 3 steps
A / B tests do not work miracles on their own, but must be part of a comprehensive process to improve conversion rates. Therefore, it is essential to have a structured plan to carry them out. These 3 steps will serve as your guide.

To get somewhere, you first need to know where you are. Therefore, any conversion optimization strategy must start from an assessment of the current situation.

To know if your website is working well, the first thing you need to ask yourself is what your business objectives are and how they translate into metrics for your website. For example, if we have an online florist, the objective may be to increase sales and conversions on the web. This can translate into a “bouquets sold in one month” KPI.

When analyzing your results, it is essential to take into account the segmentation and not just the average data of the site. For example, it is possible that a landing page is performing very well on Android mobiles and very bad on iPhone, which points to a design problem. But if you just look at the average results, you won’t be able to figure it out and correct it.

In addition to relying on Google Analytics data, it is also highly recommended to ask users themselves about what works and what doesn’t. In this way, you can find the why behind the data.

 

So many variables and so little time … In theory, you could do A / B tests of all the elements of your website, but that is not profitable or practical. Therefore, you will have to prioritize the experiments that can help you the most to improve your conversions. These tips will help you figure out what they are:

Prioritize pages with a lot of potential for improvement. If you can bring your most problematic pages to an acceptable level, you will have a lot to win. Take a look at your Analytics and search for pages with high bounce and bounce rates to find out where to start.
Prioritize low-cost, high-value experiments. That is, pages that are easy to test and have the potential to offer good results. A typical example is checkout pages that show high churn rates.
Prioritize the pages with the most traffic. Not only are they the most important for your site’s results, but they also allow you to see the test results faster. In particular, pages with a lot of traffic coming from should be a priority, as they are costing you money.

3) Get down to work
The time has finally come to design your first A / B testing actions.

The first step is to come up with a good hypothesis that makes sense of the experiment. A hypothesis is an assumption about the origin of a problem that guides us on how to solve it. For example, if the problem is “less than 1% of the visitors to the landing page download the “, we can think that this is because they do not see what it could contribute to them. One way to solve this would be to add a list of the benefits that the information in the can bring to users. To demonstrate it, we would do an A / B test comparing a landing page with this list and another without it.

Always remember that a good hypothesis is testable, tries to solve specific conversion problems, and helps us understand customers better.

Another essential step to design your A / B testing is to know what you are going to change between the different variants of the page. There are a number of elements that are easy to modify and can give very good results, so I recommend you start here:

When you do the test, always set yourself the goal of waiting until you have enough data so that the results are statistically relevant . If the number of visits is too low, the influence of chance is greater. In the worst case, you could end up convinced that variant B works better than variant A when in fact the opposite is true. So be patient! And as we always do in marketing, when you finish, learn from your results and start over.

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