A/B testing

The Benefits of A/B Testing

Understand The Concept Of A/B Testing And Use Its Power To Optimize Your Online Conversions

Today, companies are constantly looking for ways to improve their performance and profitability. A/B testing is a powerful tool for implementing effective marketing actions that optimizes the user experience and increases the conversion rate of websites.

This article explores the use of A/B testing to maximize business profits, as well as the factors affecting its performance.

What Factors Influence Conversion?

The rate of conversion of prospects (or visitors) into customers, can be affected by several elements. Here are some examples:

Quality of traffic generated

The more traffic to your site is qualified (i.e. from an audience that precisely matches your offer), the higher your conversion rate will be.

User Experience

Good design and intuitive navigation will aid the buying process and boost customer loyalty.

Offer Quality

A clear and attractive offer will help customers to quickly make a purchase decision.

Website Reputation

A good reputation is key to getting visitors to buy from your site over another.

How to Implement an A/B Testing Strategy?

Set Clear Goals

This is the first step to implementing an effective A/B testing strategy. Goals can include conversion rate, revenue generated, or user engagement.

Define the target audience

Once you’ve established your goals, it’s time to determine your target audience to test on. For this, you can create specific groups according to age, gender, location, etc.

This will provide more accurate results.

Choosing Which Tests To Perform

You can start by experimenting with simple things, like Buy Button placement or text color. Once you’ve had satisfactory results with these quick and cheap tests, you can take it to the next level and test more complex variables such as content, images and products.

Analyze Results

Once you’ve tested different variations of your website or app, it’s time to analyze the results to determine which version will provide the best return on investment (ROI). You will need to compare the variations between each version to find out which one gives the best conversion rate.

What Tools Are Available For A/B Testing?

Free Solutions

  • Google Analytics: This Google service allows users to easily set up simple A/B tests on their websites and then analyze the results with detailed tables and reports.
  • Optimizely: This tool offers an advanced web-based platform that helps businesses plan and execute multiple variations of digital experiences without the need for manual coding.
  • VWO: This tool is one of the best known and offers a detailed analysis of the tested versions, as well as suggestions to improve the conversion rate.

Solutions without free plan

  • Adobe Target: A cloud-based software that allows companies to easily perform A/B testing, multivariate testing and personalization, which can lead to a significant increase in their conversion rate.
  • Kissmetrics: Kissmetrics offers comprehensive, easy-to-use web optimization software for optimizing landing pages and other variations.
  • Qubit Optimize: A tool integrated into the Qubit system that offers a variety of customizable experiences on websites. It also provides detailed analytical reports to help quickly determine which variants perform best.

What are the pros and cons of A/B testing?

A/B testing can be very beneficial to your business if implemented correctly. However, like any other method, there are some considerations to take into account before getting started:


  • Enables accurate measurement of the effects of changes to the website or application.
  • Quick and inexpensive trial without the need for major modifications.
  • Offers the possibility of understanding how visitors interact with the content and the different elements of the site.
  • Can generate a significant increase in revenue per customer (ARPU).


  • Limited number of tests that can be performed due to its relatively complex set-up.
  • The results obtained are not always representative because they are often based on only a small number of data.