Experimentation is one of the most effective website optimization strategies. It involves testing using a scientific methodology to find the best possible user experience that will convert visitors into customers. In other words, to make your website sell more and/or better.
The term Growth in this context refers to business growth, especially in areas such as startups, emerging companies, digital marketing and business strategies. Closely related to this, CRO, Conversion Rate Optimization, focuses on improving the proportion of website visitors, or app users, who perform a desired action. This action, the business objective, can be buying a product, subscribing to a service or completing a form, among others.
This concept is based on the idea that certain changes in design, content, or user flow can have a significant impact on conversion rate.
In an e-commerce, the conversion rate would be the result of dividing the number of customers you get by the number of visitors you receive. If you multiply this number by 100, you have a percentage value, which is how this metric is indicated.
For example, let's say your online store receives ten thousand visitors per month and 256 people end up buying. To calculate the conversion rate, you just divide 186 by 10,000 and multiply it by 100 to get a conversion rate of 1.86% .
In this task of optimizing the conversion rate, the scientific method helps us to reach more precise and reliable conclusions. This framework consists of focusing the process by covering a series of stages:
Observation and understanding of the problem.
Formulation of solution hypotheses.
Experimentation and testing (this is where AB testing comes in).
Data collection and analysis.
Iteration and optimization.
Implementation.
Monitoring and follow-up.
A practical example of this approach could be the process of optimizing contact capture through a form.
Imagine you have a page on your website aimed at getting your visitors to subscribe to a newsletter and you want to work on improving the number of users who end up subscribing, 1% of visits, for example.
When you study the page where you explain the advantages of subscribing, you suspect that the call to action is not very stimulating and you have the hypothesis that a more aggressive text would perform better. However, you are not sure and would like to check it.
To test this, you can run an experiment to validate your hypothesis by comparing the performance of both options, the current one versus the more aggressive one. Obviously, you need a simple analytics system that collects conversion data for each of them.
Once you have the results, you can analyze them to draw conclusions. They may not be conclusive, or they may lead you to think that you could be even more aggressive and that it would be worth repeating the experience.
Once you have a definitive answer, it is time to implement the winning solution and check that it reflects the same results globally.
This procedure is, in a very schematic way, what web experimentation represents.
What is an AB test?
Although there are many types of experiments applicable to the dynamics I have described in the previous paragraphs, AB tests are one of the best known.
In an A/B test, users (the sample) are randomly divided into two groups: Group A and Group B. Each group receives a different version of the element being tested (the call to action in our example): Group A receives the original version (or the current version, known as the “control”), while Group B receives a slightly modified version, known as the “variant.”
run for a set period of time and meaningful data has been collected, the results are analyzed to determine which version performed better in terms of the specific metric being evaluated, such as click-through rate, conversion rate, time on site, or subscription rate in the previous case.
As you can see, it is really simple, but for it to be reliable, you have denmark whatsapp number data to take into account some statistical details that are not at all trivial. Although the AB testing tools that you will see later already take care of these aspects, it is important that you at least take a look at them to be aware of their relevance.
Don't worry if you get stuck on any of them, the tools will help you quantify the reliability of the result.
Sample size
It is crucial to ensure that you have a large enough sample size to obtain meaningful and representative results. A small sample size can lead to incorrect conclusions. The appropriate sample size depends on several factors, including the desired confidence level, the expected effect size, and the variability of the data.
If you are working on a site with very little traffic, the sample may not be large enough or you may have to extend your test longer than advisable (with factors such as the seasonality of your market or variations in user behavior over time coming into play).
You can check this data in your analytics system. If you don't have one running, you can check the monthly number of users in the Site Tools of your SiteGround hosting to get a rough idea of how many visitors your site receives.
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